Archiv der Kategorie: Disruption

A Deep Dive Into the Technology of Corporate Surveillance

December 2, 2019

By Bennett Cyphers and Gennie Gebhart

Introduction

Trackers are hiding in nearly every corner of today’s Internet, which is to say nearly every corner of modern life. The average web page shares data with dozens of third-parties. The average mobile app does the same, and many apps collect highly sensitive information like location and call records even when they’re not in use. Tracking also reaches into the physical world. Shopping centers use automatic license-plate readers to track traffic through their parking lots, then share that data with law enforcement. Businesses, concert organizers, and political campaigns use Bluetooth and WiFi beacons to perform passive monitoring of people in their area. Retail stores use face recognition to identify customers, screen for theft, and deliver targeted ads.

The tech companies, data brokers, and advertisers behind this surveillance, and the technology that drives it, are largely invisible to the average user. Corporations have built a hall of one-way mirrors: from the inside, you can see only apps, web pages, ads, and yourself reflected by social media. But in the shadows behind the glass, trackers quietly take notes on nearly everything you do. These trackers are not omniscient, but they are widespread and indiscriminate. The data they collect and derive is not perfect, but it is nevertheless extremely sensitive.

This paper will focus on corporate “third-party” tracking: the collection of personal information by companies that users don’t intend to interact with. It will shed light on the technical methods and business practices behind third-party tracking. For journalists, policy makers, and concerned consumers, we hope this paper will demystify the fundamentals of third-party tracking, explain the scope of the problem, and suggest ways for users and legislation to fight back against the status quo.

Part 1 breaks down “identifiers,” or the pieces of information that trackers use to keep track of who is who on the web, on mobile devices, and in the physical world. Identifiers let trackers link behavioral data to real people.

Part 2 describes the techniques that companies use to collect those identifiers and other information. It also explores how the biggest trackers convince other businesses to help them build surveillance networks.

Part 3 goes into more detail about how and why disparate actors share information with each other. Not every tracker engages in every kind of tracking. Instead, a fragmented web of companies collect data in different contexts, then share or sell it in order to achieve specific goals.

Finally, Part 4 lays out actions consumers and policy makers can take to fight back. To start, consumers can change their tools and behaviors to block tracking on their devices. Policy makers must adopt comprehensive privacy laws to rein in third-party tracking.

Contents

Introduction
First-party vs. third-party tracking
What do they know?
Part 1: Whose Data is it Anyway: How Do Trackers Tie Data to People?
Identifiers on the Web
Identifiers on mobile devices
Real-world identifiers
Linking identifiers over time
Part 2: From bits to Big Data: What do tracking networks look like?
Tracking in software: Websites and Apps
Passive, real-world tracking
Tracking and corporate power
Part 3: Data sharing: Targeting, brokers, and real-time bidding
Real-time bidding
Group targeting and look-alike audiences
Data brokers
Data consumers
Part 4: Fighting back
On the web
On mobile phones
IRL
In the legislature

First-party vs. third-party tracking

The biggest companies on the Internet collect vast amounts of data when people use their services. Facebook knows who your friends are, what you “Like,” and what kinds of content you read on your newsfeed. Google knows what you search for and where you go when you’re navigating with Google Maps. Amazon knows what you shop for and what you buy.

The data that these companies collect through their own products and services is called “first-party data.” This information can be extremely sensitive, and companies have a long track record of mishandling it. First-party data is sometimes collected as part of an implicit or explicit contract: choose to use our service, and you agree to let us use the data we collect while you do. More users are coming to understand that for many free services, they are the product, even if they don’t like it.

However, companies collect just as much personal information, if not more, about people who aren’t using their services. For example, Facebook collects information about users of other websites and apps with its invisible “conversion pixels.” Likewise, Google uses location data to track user visits to brick and mortar stores. And thousand of other data brokers, advertisers, and other trackers lurk in the background of our day-to-day web browsing and device use. This is known as “third-party tracking.” Third-party tracking is much harder to identify without a trained eye, and it’s nearly impossible to avoid completely.

What do they know?

Many consumers are familiar with the most blatant privacy-invasive potential of their devices. Every smartphone is a pocket-sized GPS tracker, constantly broadcasting its location to parties unknown via the Internet. Internet-connected devices with cameras and microphones carry the inherent risk of conversion into silent wiretaps. And the risks are real: location data has been badly abused in the past. Amazon and Google have both allowed employees to listen to audio recorded by their in-home listening devices, Alexa and Home. And front-facing laptop cameras have been used by schools to spy on students in their homes.

But these better known surveillance channels are not the most common, or even necessarily the most threatening to our privacy. Even though we spend many of our waking hours in view of our devices’ Internet-connected cameras, it’s exceedingly rare for them to record anything without a user’s express intent. And to avoid violating federal and state wiretapping laws, tech companies typically refrain from secretly listening in on users’ conversations. As the rest of this paper will show, trackers learn more than enough from thousands of less dramatic sources of data. The unsettling truth is that although Facebook doesn’t listen to you through your phone, that’s just because it doesn’t need to.

The most prevalent threat to our privacy is the slow, steady, relentless accumulation of relatively mundane data points about how we live our lives. This includes things like browsing history, app usage, purchases, and geolocation data. These humble parts can be combined into an exceptionally revealing whole. Trackers assemble data about our clicks, impressions, taps, and movement into sprawling behavioral profiles, which can reveal political affiliation, religious belief, sexual identity and activity, race and ethnicity, education level, income bracket, purchasing habits, and physical and mental health.

Despite the abundance of personal information they collect, tracking companies frequently use this data to derive conclusions that are inaccurate or wrong. Behavioral advertising is the practice of using data about a user’s behavior to predict what they like, how they think, and what they are likely to buy, and it drives much of the third-party tracking industry. While behavioral advertisers sometimes have access to precise information, they often deal in sweeping generalizations and “better than nothing” statistical guesses. Users see the results when both uncannily accurate and laughably off-target advertisements follow them around the web. Across the marketing industry, trackers use petabytes of personal data to power digital tea reading. Whether trackers’ inferences are correct or not, the data they collect represents a disproportionate invasion of privacy, and the decisions they make based on that data can cause concrete harm.

Part 1: Whose Data is it Anyway: How Do Trackers Tie Data to People?

Most third-party tracking is designed to build profiles of real people. That means every time a tracker collects a piece of information, it needs an identifier—something it can use to tie that information to a particular person. Sometimes a tracker does so indirectly: by correlating collected data with a particular device or browser, which might in turn later be correlated to one person or perhaps a small group of people like a household.

To keep track of who is who, trackers need identifiers that are unique, persistent, and available. In other words, a tracker is looking for information (1) that points only to you or your device, (2) that won’t change, and (3) that it has easy access to. Some potential identifiers fit all three of these requirements, but trackers can still make use of an identifier that checks only two of these three boxes. And trackers can combine multiple weak identifiers to create a single, strong one.

An identifier that checks all three boxes might be a name, an email, or a phone number. It might also be a “name” that the tracker itself gives you, like “af64a09c2” or “921972136.1561665654”. What matters most to the tracker is that the identifier points to you and only you. Over time, it can build a rich enough profile about the person known as “af64a09c2”—where they live, what they read, what they buy—that a conventional name is not necessary. Trackers can use artificial identifiers, like cookies and mobile ad IDs, to reach users with targeted messaging. And data that isn’t tied to a real name is no less sensitive: “anonymous” profiles of personal information can nearly always be linked back to real people.

Some types of identifiers, like cookies, are features built into the tech that we use. Others, like browser fingerprints, emerge from the way those technologies work. This section will break down how trackers on the web and in mobile apps are able to identify and attribute data points.

This section will describe a representative sample of identifiers that third-party trackers can use. It is not meant to be exhaustive; there are more ways for trackers to identify users than we can hope to cover, and new identifiers will emerge as technology evolves. The tables below give a brief overview of how unique, persistent, and available each type of identifier is.

Web Identifiers Unique Persistent Available
Cookies Yes Until user deletes In some browsers without tracking protection
IP address Yes On the same network, may persist for weeks or months Always
TLS state Yes For up to one week In most browsers
Local storage super cookie Yes Until user deletes Only in third-party IFrames; can be blocked by tracker blockers
Browser fingerprint Only on certain browsers Yes Almost always; usually requires JavaScript access, sometimes blocked by tracker blockers

 

Phone Identifiers Unique Persistent Available
Phone number Yes Until user changes Readily available from data brokers; only visible to apps with special permissions
IMSI and IMEI number Yes Yes Only visible to apps with special permissions
Advertising ID Yes Until user resets Yes, to all apps
MAC address Yes Yes To apps: only with special permissionsTo passive trackers: visible unless OS performs randomization or device is in airplane mode

 

Other Identifiers Unique Persistent Available
License plate Yes Yes Yes
Face print Yes Yes Yes
Credit card number Yes Yes, for months or years To any companies involved in payment processing

Identifiers on the Web

Browsers are the primary way most people interact with the Web. Each time you visit a website, code on that site may cause your browser to make dozens or even hundreds of requests to hidden third parties. Each request contains several pieces of information that can be used to track you.

Anatomy of a Request

Almost every piece of data transmitted between your browser and the servers of the websites you interact with occurs in the form of an HTTP request. Basically, your browser asks a web server for content by sending it a particular URL. The web server can respond with content, like text or an image, or with a simple acknowledgement that it received your request. It can also respond with a cookie, which can contain a unique identifier for tracking purposes.

Each website you visit kicks off dozens or hundreds of different requests. The URL you see in the address bar of your browser is the address for the first request, but hundreds of other requests are made in the background. These requests can be used for loading images, code, and styles, or simply for sharing data.

A diagram depicting the various parts of a URL

Parts of a URL. The domain tells your computer where to send the request, while the path and parameters carry information that may be interpreted by the receiving server however it wants.

The URL itself contains a few different pieces of information. First is the domain, like “nytimes.com”. This tells your browser which server to connect to. Next is the path, a string at the end of the domain like “/section/world.html”. The server at nytimes.com chooses how to interpret the path, but it usually specifies a piece of content to serve—in this case, the world news section. Finally, some URLs have parameters at the end in the form of “?key1=value1&key2=value2”. The parameters usually carry extra information about the request, including queries made by the user, context about the page, and tracking identifiers.

A computer sending a single request to a website at "eff.org."

The path of a request. After it leaves your machine, the request is redirected by your router to your ISP, which sends it through a series of intermediary routing stations in “the Internet.” Finally, it arrives at the server specified by the domain, which can decide how (or if) to respond.

The URL isn’t all that gets sent to the server. There are also HTTP headers, which contain extra information about the request like your device’s language and security settings, the “referring” URL, and cookies. For example, the User-Agent header identifies your browser type, version, and operating system. There’s also lower-level information about the connection, including IP address and shared encryption state. Some requests contain even more configurable information in the form of POST data. POST requests are a way for websites to share chunks of data that are too large or unwieldy to fit in a URL. They can contain just about anything.

Some of this information, like the URL and POST data, is specifically tailored for each individual request; other parts, like your IP address and any cookies, are sent automatically by your machine. Almost all of it can be used for tracking.

A URL bar and the data that’s sent along with a website request.

Data included with a background request. In the image, although the user has navigated to fafsa.gov, the page triggers a third-party request to facebook.com in the background. The URL isn’t the only information that gets sent to the receiving server; HTTP Headers contain information like your User Agent string and cookies, and POST data can contain anything that the server wants.

The animation immediately above contains data we collected directly from a normal version of Firefox. If you want to check it out for yourself, you can. All major browsers have an “inspector” or “developer” mode which allows users to see what’s going on behind the scenes, including all requests coming from a particular tab. In Chrome and Firefox, you can access this interface with Crtl+Shift+I (or ⌘+Shift+I on Mac). The “Network” tab has a log of all the requests made by a particular page, and you can click on each one to see where it’s going and what information it contains.

Identifiers shared automatically

Some identifiable information is shared automatically along with each request. This is either by necessity—as with IP addresses, which are required by the underlying protocols that power the Internet—or by design—as with cookies. Trackers don’t need to do anything more than trigger a request, any request, in order to collect the information described here.

//website.com. This is shown as a HTTP request, processed by a first-party server, and delivering the requested content. A separate red line shows that the HTTP request is also forwarded to a third-party server, given an assigned ID, and a tracking cookie that is included in the requested content.

Each time you visit a website by typing in a URL or clicking on a link, your computer makes a request to that website’s server (the “first party”). It may also make dozens or hundreds of requests to other servers, many of which may be able to track you.

Cookies

The most common tool for third-party tracking is the HTTP cookie. A cookie is a small piece of text that is stored in your browser, associated with a particular domain. Cookies were invented to help website owners determine whether a user had visited their site before, which makes them ideal for behavioral tracking. Here’s how they work.

The first time your browser makes a request to a domain (like www.facebook.com), the server can attach a Set-Cookie header to its reply. This will tell your browser to store whatever value the website wants—for example, `c_user:“100026095248544″` (an actual Facebook cookie taken from the author’s browser). Then, every time your browser makes a request to www.facebook.com in the future, it sends along the cookie that was set earlier. That way, every time Facebook gets a request, it knows which individual user or device it’s coming from.

//website.com. The server responds with website content and a cookie.

The first time a browser makes a request to a new server, the server can reply with a “Set-Cookie” header that stores a tracking cookie in the browser.

Not every cookie is a tracker. Cookies are also the reason that you don’t have to log in every single time you visit a website, as well as the reason your cart doesn’t empty if you leave a website in the middle of shopping. Cookies are just a means of sharing information from your browser to the website you are visiting. However, they are designed to be able to carry tracking information, and third-party tracking is their most notorious use.

Luckily, users can exercise a good deal of control over how their browsers handle cookies. Every major browser has an optional setting to disable third-party cookies (though it is usually turned off by default.) In addition, Safari and Firefox have recently started restricting access to third-party cookies for domains they deem to be trackers. As a result of this “cat and mouse game” between trackers and methods to block them, third-party trackers are beginning to shift away from relying solely on cookies to identify users, and are evolving to rely on other identifiers.

Cookies are always unique, and they normally persist until a user manually clears them. Cookies are always available to trackers in unmodified versions of Chrome, but third-party cookies are no longer available to many trackers in Safari and Firefox. Users can always block cookies themselves with browser extensions.

IP Address

Each request you make over the Internet contains your IP address, a temporary identifier that’s unique to your device. Although it is unique, it is not necessarily persistent: your IP address changes every time you move to a new network (e.g., from home to work to a coffee shop). Thanks to the way IP addresses work, it may change even if you stay connected to the same network.

There are two types of IP addresses in widespread use, known as IPv4 and IPv6. IPv4 is a technology that predates the Web by a decade. It was designed for an Internet used by just a few hundred institutions, and there are only around 4 billion IPV4 addresses in the world to serve over 22 billion connected devices today. Even so, over 70% of Internet traffic still uses IPv4.

As a result, IPv4 addresses used by consumer devices are constantly being reassigned. When a device connects to the Internet, its internet service provider (ISP) gives it a “lease” on an IPv4 address. This lets the device use a single address for a few hours or a few days. When the lease is up, the ISP can decide to extend the lease or grant it a new IP. If a device remains on the same network for extended periods of time, its IP may change every few hours — or it may not change for months.

IPv6 addresses don’t have the same scarcity problem. They do not need to change, but thanks to a privacy-preserving extension to the technical standard, most devices generate a new, random IPv6 address every few hours or days. This means that IPv6 addresses may be used for short-term tracking or to link other identifiers, but cannot be used as standalone long-term identifiers.

IP addresses are not perfect identifiers on their own, but with enough data, trackers can use them to create long-term profiles of users, including mapping relationships between devices. You can hide your IP address from third-party trackers by using a trusted VPN or the Tor browser.

IP addresses are always unique, and always available to trackers unless a user connects through a VPN or Tor. Neither IPv4 nor IPv6 addresses are guaranteed to persist for longer than a few days, although IPv4 addresses may persist for several months

TLS State

Today, most traffic on the web is encrypted using Transport Layer Security, or TLS. Any time you connect to a URL that starts with “https://” you’re connecting using TLS. This is a very good thing. The encrypted connection that TLS and HTTPS provide prevents ISPs, hackers, and governments from spying on web traffic, and it ensures that data isn’t being intercepted or modified on the way to its destination.

However, it also opens up new ways for trackers to identify users. TLS session IDs and session tickets are cryptographic identifiers that help speed up encrypted connections. When you connect to a server over HTTPS, your browser starts a new TLS session with the server.

The session setup involves some expensive cryptographic legwork, so servers don’t like to do it more often than they have to. Instead of performing a full cryptographic “handshake” between the server and your browser every time you reconnect, the server can send your browser a session ticket that encodes some of the shared encryption state. The next time you connect to the same server, your browser sends the session ticket, allowing both parties to skip the handshake. The only problem with this is that the session ticket can be exploited by trackers as a unique identifier.

TLS session tracking was only brought to the public’s attention recently in an academic paper, and it’s not clear how widespread its use is in the wild.

Like IP addresses, session tickets are always unique. They are available unless the user’s browser is configured to reject them, as Tor is. Server operators can usually configure session tickets to persist for up to a week, but browsers do reset them after a while.

Identifiers created by trackers

Sometimes, web-based trackers want to use identifiers beyond just IP addresses (which are unreliable and not persistent), cookies (which a user can clear or block), or TLS state (which expires within hours or days). To do so, trackers need to put in a little more effort. They can use JavaScript to save and load data in local storage or perform browser fingerprinting.

Local storage “cookies” and IFrames

Local storage is a way for websites to store data in a browser for long periods of time. Local storage can help a web-based text editor save your settings, or allow an online game to save your progress. Like cookies, local storage allows third-party trackers to create and save unique identifiers in your browser.

Also like cookies, data in local storage is associated with a specific domain. This means if example.com sets a value in your browser, only example.com web pages and example.com’s IFrames can access it. An IFrame is like a small web page within a web page. Inside an IFrame, a third-party domain can do almost everything a first-party domain can do. For example, embedded YouTube videos are built using IFrames; every time you see a YouTube video on a site other than YouTube, it’s running inside a small page-within-a-page. For the most part, your browser treats the YouTube IFrame like a full-fledged web page, giving it permission to read and write to YouTube’s local storage. Sure enough, YouTube uses that storage to save a unique “device identifier” and track users on any page with an embedded video.

Local storage “cookies” are unique, and they persist until a user manually clears their browser storage. They are only available to trackers which are able to run JavaScript code inside a third-party IFrame. Not all cookie-blocking measures take local storage cookies into account, so local storage cookies may sometimes be available to trackers for which normal cookie access is blocked.

Fingerprinting

Browser fingerprinting is one of the most complex and insidious forms of web-based tracking. A browser fingerprint consists of one or more attributes that, on their own or when combined, uniquely identify an individual browser on an individual device. Usually, the data that go into a fingerprint are things that the browser can’t help exposing, because they’re just part of the way it interacts with the web. These include information sent along with the request made every time the browser visits a site, along with attributes that can be discovered by running JavaScript on the page. Examples include the resolution of your screen, the specific version of software you have installed, and your time zone. Any information that your browser exposes to the websites you visit can be used to help assemble a browser fingerprint. You can get a sense of your own browser’s fingerprint with EFF’s Panopticlick project.

The reliability of fingerprinting is a topic of active research, and must be measured against the backdrop of ever-evolving web technologies. However, it is clear that new techniques increase the likelihood of unique identification, and the number of sites that use fingerprinting is increasing as well. A recent report found that at least a third of the top 500 sites visited by Americans employ some form of browser fingerprinting. The prevalence of fingerprinting on sites also varies considerably with the category of website.

Researchers have found canvas fingerprinting techniques to be particularly effective for browser identification. The HTML Canvas is a feature of HTML5 that allows websites to render complex graphics inside of a web page. It’s used for games, art projects, and some of the most beautiful sites on the Web. Because it’s so complex and performance-intensive, it works a little bit differently on each different device. Canvas fingerprinting takes advantage of this.

Subtle differences in the way shapes and text are rendered on the two computers lead to very different fingerprints.

Canvas fingerprinting. A tracker renders shapes, graphics, and text in different fonts, then computes a “hash” of the pixels that get drawn. The hash will be different on devices with even slight differences in hardware, firmware, or software.

A tracker can create a “canvas” element that’s invisible to the user, render a complicated shape or string of text using JavaScript, then extract data about exactly how each pixel on the canvas is rendered. The operating system, browser version, graphics card, firmware version, graphics driver version, and fonts installed on your computer all affect the final result.

For the purposes of fingerprinting, individual characteristics are hardly ever measured in isolation. Trackers are most effective in identifying a browser when they combine multiple characteristics together, stitching the bits of information left behind into a cohesive whole. Even if one characteristic, like a canvas fingerprint, is itself not enough to uniquely identify your browser, it can usually be combined with others — your language, time zone, or browser settings — in order to identify you. And using a combination of simple bits of information is much more effective than you might guess.

Fingerprints are often, but not always, unique. Some browsers, like Tor and Safari, are specifically designed so that their users are more likely to look the same, which removes or limits the effectiveness of browser fingerprinting. Browser fingerprints tend to persist as long as a user has the same hardware and software: there’s no setting you can fiddle with to “reset” your fingerprint. And fingerprints are usually available to any third parties who can run JavaScript in your browser.

Identifiers on mobile devices

Smartphones, tablets, and ebook readers usually have web browsers that work the same way desktop browsers do. That means that these types of connected devices are susceptible to all of the kinds of tracking described in the section above.

However, mobile devices are different in two big ways. First, users typically need to sign in with an Apple, Google, or Amazon account to take full advantage of the devices’ features. This links device identifiers to an account identity, and makes it easier for those powerful corporate actors to profile user behavior. For example, in order to save your home and work address in Google Maps, you need to turn on Google’s “Web and App Activity,” which allows it to use your location, search history, and app activity to target ads.

Second, and just as importantly, most people spend most of their time on their mobile device in apps outside of the browser. Trackers in apps can’t access cookies the same way web-based trackers can. But by taking advantage of the way mobile operating systems work, app trackers can still access unique identifiers that let them tie activity back to your device. In addition, mobile phones—particularly those running the Android and iOS operating systems—have access to a unique set of identifiers that can be used for tracking.

In the mobile ecosystem, most tracking happens by way of third-party software development kits, or SDKs. An SDK is a library of code that app developers can choose to include in their apps. For the most part, SDKs work just like the Web resources that third parties exploit, as discussed above: they allow a third party to learn about your behavior, device, and other characteristics. An app developer who wants to use a third-party analytics service or serve third-party ads downloads a piece of code from, for example, Google or Facebook. The developer then includes that code in the published version of their app. The third-party code thus has access to all the data that the app does, including data protected behind any permissions that the app has been granted, such as location or camera access.

On the web, browsers enforce a distinction between “first party” and “third party” resources. That allows them to put extra restrictions on third-party content, like blocking their access to browser storage. In mobile apps, this distinction doesn’t exist. You can’t grant a privilege to an app without granting the same privilege to all the third party code running inside it.

Phone numbers

The phone number is one of the oldest unique numeric identifiers, and one of the easiest to understand. Each number is unique to a particular device, and numbers don’t change often. Users are encouraged to share their phone numbers for a wide variety of reasons (e.g., account verification, electronic receipts, and loyalty programs in brick-and-mortar stores). Thus, data brokers frequently collect and sell phone numbers. But phone numbers aren’t easy to access from inside an app. On Android, phone numbers are only available to third-party trackers in apps that have been granted certain permissions. iOS prevents apps from accessing a user’s phone number at all.

Phone numbers are unique and persistent, but usually not available to third-party trackers in most apps.

Hardware identifiers: IMSI and IMEI

Every device that can connect to a mobile network is assigned a unique identifier called an International Mobile Subscriber Identity (IMSI) number. IMSI numbers are assigned to users by their mobile carriers and stored on SIM cards, and normal users can’t change their IMSI without changing their SIM. This makes them ideal identifiers for tracking purposes.

Similarly, every mobile device has an International Mobile Equipment Identity (IMEI) number “baked” into the hardware. You can change your SIM card and your phone number, but you can’t change your IMEI without buying a new device.

IMSI numbers are shared with your cell provider every time you connect to a cell tower—which is all the time. As you move around the world, your phone sends out pings to nearby towers to request information about the state of the network. Your phone carrier can use this information to track your location (to varying degrees of accuracy). This is not quite third-party tracking, since it is perpetrated by a phone company that you have a relationship with, but regardless many users may not realize that it’s happening.

Software and apps running on a mobile phone can also access IMSI and IMEI numbers, though not as easily. Mobile operating systems lock access to hardware identifiers behind permissions that users must approve and can later revoke. For example, starting with Android Q, apps need to request the “READ_PRIVILEGED_PHONE_STATE” permission in order to read non-resettable IDs. On iOS, it’s not possible for apps to access these identifiers at all. This makes other identifiers more attractive options for most app-based third-party trackers. Like phone numbers, IMSI and IMEI numbers are unique and persistent, but not readily available, as most trackers have a hard time accessing them.

Advertising IDs

An advertising ID is a long, random string of letters and numbers that uniquely identifies a mobile device. Advertising IDs aren’t part of any technical protocols, but are built in to the iOS and Android operating systems.

Ad IDs on mobile phones are analogous to cookies on the Web. Instead of being stored by your browser and shared with trackers on different websites like cookies, ad IDs are stored by your phone and shared with trackers in different apps. Ad IDs exist for the sole purpose of helping behavioral advertisers link user activity across apps on a device.

Unlike IMSI or IMEI numbers, ad IDs can be changed and, on iOS, turned off completely. Ad IDs are enabled by default on both iOS and Android, and are available to all apps without any special permissions. On both platforms, the ad ID does not reset unless the user does so manually.

Both Google and Apple encourage developers to use ad IDs for behavioral profiling in lieu of other identifiers like IMEI or phone number. Ostensibly, this gives users more control over how they are tracked, since users can reset their identifiers by hand if they choose. However, in practice, even if a user goes to the trouble to reset their ad ID, it’s very easy for trackers to identify them across resets by using other identifiers, like IP address or in-app storage. Android’s developer policy instructs trackers not to engage in such behavior, but the platform has no technical safeguards to stop it. In February 2019, a study found that over 18,000 apps on the Play store were violating Google’s policy.

Ad IDs are unique, and available to all apps by default. They persist until users manually reset them. That makes them very attractive identifiers for surreptitious trackers.

MAC addresses

Every device that can connect to the Internet has a hardware identifier called a Media Access Control (MAC) address. MAC addresses are used to set up the initial connection between two wireless-capable devices over WiFi or Bluetooth.

MAC addresses are used by all kinds of devices, but the privacy risks associated with them are heightened on mobile devices. Websites and other servers you interact with over the Internet can’t actually see your MAC address, but any networking devices in your area can. In fact, you don’t even have to connect to a network for it to see your MAC address; being nearby is enough.

Here’s how it works. In order to find nearby Bluetooth devices and WiFi networks, your device is constantly sending out short radio signals called probe requests. Each probe request contains your device’s unique MAC address. If there is a WiFi hotspot in the area, it will hear the probe and send back its own “probe response,” addressed with your device’s MAC, with information about how you can connect to it.

But other devices in the area can see and intercept the probe requests, too. This means that companies can set up wireless “beacons” that silently listen for MAC addresses in their vicinity, then use that data to track the movement of specific devices over time. Beacons are often set up in businesses, at public events, and even in political campaign yard signs. With enough beacons in enough places, companies can track users’ movement around stores or around a city. They can also identify when two people are in the same location and use that information to build a social graph.

A smartphone emits probe request to scan for available WiFi and Bluetooth connections. Several wireless beacons listen passively to the requests.

In order to find nearby Bluetooth devices and WiFi networks, your device is constantly sending out short radio signals called probe requests. Each probe request contains your device’s unique MAC address. Companies can set up wireless “beacons” that silently listen for MAC addresses in their vicinity, then use that data to track the movement of specific devices over time.

This style of tracking can be thwarted with MAC address randomization. Instead of sharing its true, globally unique MAC address in probe requests, your device can make up a new, random, “spoofed” MAC address to broadcast each time. This makes it impossible for passive trackers to link one probe request to another, or to link them to a particular device. Luckily, the latest versions of iOS and Android both include MAC address randomization by default.

MAC address tracking remains a risk for laptops, older phones, and other devices, but the industry is trending towards more privacy-protective norms.

Hardware MAC addresses are globally unique. They are also persistent, not changing for the lifetime of a device. They are not readily available to trackers in apps, but are available to passive trackers using wireless beacons. However, since many devices now obfuscate MAC addresses by default, they are becoming a less reliable identifier for passive tracking.

Real-world identifiers

Many electronic device identifiers can be reset, obfuscated, or turned off by the user. But real-world identifiers are a different story: it’s illegal to cover your car’s license plate while driving (and often while parked), and just about impossible to change biometric identifiers like your face and fingerprints.

License plates

Every car in the United States is legally required to have a license plate that is tied to their real-world identity. As far as tracking identifiers go, license plate numbers are about as good as it gets. They are easy to spot and illegal to obfuscate. They can’t be changed easily, and they follow most people wherever they go.

Automatic license plate readers, or ALPRs, are special-purpose cameras that can automatically identify and record license plate numbers on passing cars. ALPRs can be installed at fixed points, like busy intersections or mall parking lots, or on other vehicles like police cars. Private companies operate ALPRs, use them to amass vast quantities of traveler location data, and sell this data to other businesses (as well as to police).

Unfortunately, tracking by ALPRs is essentially unavoidable for people who drive. It’s not legal to hide or change your license plate, and since most ALPRs operate in public spaces, it’s extremely difficult to avoid the devices themselves.

License plates are unique, available to anyone who can see the vehicle, and extremely persistent. They are ideal identifiers for gathering data about vehicles and their drivers, both for law enforcement and for third-party trackers.

Face biometrics

Faces are another class of unique identifier that are extremely attractive to third-party trackers. Faces are unique and highly inconvenient to change. Luckily, it’s not illegal to hide your face from the general public, but it is impractical for most people to do so.

Everyone’s face is unique, available, and persistent. However, current face recognition software will sometimes confuse one face for another. Furthermore, research has shown that algorithms are much more prone to making these kinds of errors when identifying people of color, women, and older individuals.

Facial recognition has already seen widespread deployment, but we are likely just beginning to feel the extent of its impact. In the future, facial recognition cameras may be in stores, on street corners, and docked on computer-aided glasses. Without strong privacy regulations, average people will have virtually no way to fight back against pervasive tracking and profiling via facial recognition.

Credit/debit cards

Credit card numbers are another excellent long-term identifier. While they can be cycled out, most people don’t change their credit card numbers nearly as often as they clear their cookies. Additionally, credit card numbers are tied directly to real names, and anyone who receives your credit card number as part of a transaction also receives your legal name.

What most people may not understand is the amount of hidden third parties involved with each credit card transaction. If you buy a widget at a local store, the store probably contracts with a payment processor who provides card-handling services. The transaction also must be verified by your bank as well as the bank of the card provider. The payment processor in turn may employ other companies to validate its transactions, and all of these companies may receive information about the purchase. Banks and other financial institutions are regulated by the Gramm-Leach-Bliley Act, which mandates data security standards, requires them to disclose how user data is shared, and gives users the right to opt out of sharing. However, other financial technology companies, like payment processors and data aggregators, are significantly less regulated.

Linking identifiers over time

Often, a tracker can’t rely on a single identifier to act as a stable link to a user. IP addresses change, people clear cookies, ad IDs can be reset, and more savvy users might have “burner” phone numbers and email addresses that they use to try to separate parts of their identity. When this happens, trackers don’t give up and start a new user profile from scratch. Instead, they typically combine several identifiers to create a unified profile. This way, they are less likely to lose track of the user when one identifier or another changes, and they can link old identifiers to new ones over time.

Trackers have an advantage here because there are so many different ways to identify a user. If a user clears their cookies but their IP address doesn’t change, linking the old cookie to the new one is trivial. If they move from one network to another but use the same browser, a browser fingerprint can link their old session to their new one. If they block third-party cookies and use a hard-to-fingerprint browser like Safari, trackers can use first-party cookie sharing in combination with TLS session data to build a long-term profile of user behavior. In this cat-and-mouse game, trackers have technological advantages over individual users.

Part 2: From bits to Big Data: What do tracking networks look like?

In order to track you, most tracking companies need to convince website or app developers to include custom tracking code in their products. That’s no small thing: tracking code can have a number of undesirable effects for publishers. It can slow down software, annoy users, and trigger regulation under laws like GDPR. Yet the largest tracking networks cover vast swaths of the Web and the app stores, collecting data from millions of different sources all the time. In the physical world, trackers can be found in billboards, retail stores, and mall parking lots. So how and why are trackers so widespread? In this section, we’ll talk about what tracking networks look like in the wild.

A bar graph showing market share of different web tracking companies. Google is the most prevalent, monitoring over 80% of traffic on the web.

Top trackers on the Web, ranked by the proportion of web traffic that they collect data from. Google collects data about over 80% of measured web traffic. Source: WhoTracks.me, by Cliqz GBMH.

Tracking in software: Websites and Apps

Ad networks

A graphic of a web page, with three ads separated and outlined. Each ad is served by a different ad server.

Each ad your browser loads may come from a different advertising server, and each server can build its own profile of you based on your activity. Each time you connect to that server, it can use a cookie to link that activity to your profile.

The dominant market force behind third-party tracking is the advertising industry, as discussed below in Part 3. So it’s no surprise that online ads are one of the primary vectors for data collection. In the simplest model, a single third-party ad network serves ads on a number of websites. Each publisher that works with the ad network must include a small snippet of code on their website that will load an ad from the ad server. This triggers a request to the ad server each time a user visits one of the cooperating sites, which lets the ad server set third-party cookies into users’ browsers and track their activity across the network. Similarly, an ad server might provide an ad-hosting software development kit (SDK) for mobile app developers to use. Whenever a user opens an app that uses the SDK, the app makes a request to the ad server. This request can contain the advertising ID for the user’s device, thus allowing the ad server to profile the user’s activity across apps.

In reality, the online ad ecosystem is even more complicated. Ad exchanges host “real time auctions” for individual ad impressions on web pages. In the process, they may load code from several other third-party advertising providers, and may share data about each impression with many potential advertisers participating in the auction. Each ad you see might be responsible for sharing data with dozens of trackers. We’ll go into more depth about Real Time Bidding and other data-sharing activities in Part 3.

Analytics and tracking pixels

Tracking code often isn’t associated with anything visible to users, like a third-party ad. On the web, a significant portion of tracking happens via invisible, 1-pixel-by-1-pixel “images” that exist only to trigger requests to the trackers. These “tracking pixels” are used by many of the most prolific data collectors on the web, including Google Analytics, Facebook, Amazon, and DoubleVerify.

When website owners install a third party’s tracking pixels, they usually do so in exchange for access to some of the data the third party collects. For example, Google Analytics and Chartbeat use pixels to collect information, and offer website owners and publishers insights about what kinds of people are visiting their sites. Going another level deeper, advertising platforms like Facebook also offer “conversion pixels,” which allow publishers to keep track of how many click-throughs their own third-party ads are getting.

The biggest players in web-based analytics offer similar services to mobile apps. Google Analytics and Facebook are two of the most popular SDKs on both Android and iOS. Like their counterparts on the Web, these services silently collect information about users of mobile apps and then share some of that information with the app developers themselves.

Mobile third-party trackers convince app developers to install their SDKs by providing useful features like analytics or single sign-on. SDKs are just big blobs of code that app developers add to their projects. When they compile and distribute an app, the third-party code ships with it. Unlike Web-based tools, analytics services in mobile apps don’t need to use “pixels” or other tricks to trigger third-party requests.

Another class of trackers work on behalf of advertisers rather than first-party sites or apps. These companies work with advertisers to monitor where, how, and to whom their ads are being served. They often don’t work with first-party publishers at all; in fact, their goal is to gather data about publishers as well as users.

DoubleVerify is one of the largest such services. Third-party advertisers inject DoubleVerify code alongside their ads, and DoubleVerify estimates whether each impression is coming from a real human (as opposed to a bot), whether the human is who the advertiser meant to target, and whether the page around the ad is “brand safe.” According to its privacy policy, the company measures “how long the advertisement was displayed in the consumer’s browser” and “the display characteristics of the ad on the consumer’s browser.” In order to do all that, DoubleVerify gathers detailed data about users’ browsers; it is by far the largest source of third-party browser fingerprinting on the web. It collects location data, including data from other third-party sources, to try to determine whether a user is viewing an ad in the geographic area that the advertiser targeted.

Other companies in the space include Adobe, Oracle, and Comscore.

Embedded media players

Sometimes, third-party trackers serve content that users actually want to see. On the web, embedding third-party content is extremely common for blogs and other media sites. Some examples include video players for services like YouTube, Vimeo, Streamable, and Twitter, and audio widgets for Soundcloud, Spotify, and podcast-streaming services. These media players nearly always run inside IFrames, and therefore have access to local storage and the ability to run arbitrary JavaScript. This makes them well-suited to tracking users as well.

Social media widgets

Social media companies provide a variety of services to websites, such as Facebook Like buttons and Twitter Share buttons. These are often pitched as ways for publishers to improve traffic numbers on their own platforms as well as their presence on social media. Like and Share buttons can be used for tracking in the same way that pixels can: the “button” is really an embedded image which triggers a request to the social media company’s server.

More sophisticated widgets, like comment sections, work more like embedded media players. They usually come inside of IFrames and enjoy more access to users’ browsers than simple pixels or images. Like media players, these widgets are able to access local storage and run JavaScript in order to compute browser fingerprints.

Finally, the biggest companies (Facebook and Google in particular) offer account management services to smaller companies, like “Log in with Google.” These services, known as “single sign-on,” are attractive to publishers for several reasons. Independent websites and apps can offload the work of managing user accounts to the big companies. Users have fewer username/password pairs to remember, and less frequently go through annoying sign up/log-in flows. But for users, there is a price: account management services allow log-in providers to act as a third party and track their users’ activity on all of the services they log into. Log-in services are more reliable trackers than pixels or other simple widgets because they force users to confirm their identity.

CAPTCHAs

CAPTCHAs are a technology that attempts to separate users from robots. Publishers install CAPTCHAs on pages where they want to be particularly careful about blocking automated traffic, like sign-up forms and pages that serve particularly large files.

Google’s ReCAPTCHA is the most popular CAPTCHA technology on the web. Every time you connect to a site that uses recaptcha, your browser connects to a *.google.com domain in order to load the CAPTCHA resources and shares all associated cookies with Google. This means that its CAPTCHA network is another source of data that Google can use to profile users.

While older CAPTCHAs asked users to read garbled text or click on pictures of bikes, the new ReCAPTCHA v3 records “interactions with the website” and silently guesses whether a user is human. ReCAPTCHA scripts don’t send raw interaction data back to Google. Rather, they generate something akin to a behavioral fingerprint, which summarizes the way a user has interacted with a page. Google feeds this into a machine-learning model to estimate how likely the user is to be human, then returns that score to the first-party website. In addition to making things more convenient for users, this newer system benefits Google in two ways. First, it makes CAPTCHAS invisible to most users, which may make them less aware that Google (or anyone) is collecting data about them. Second, it leverages Google’s huge set of behavioral data to cement its dominance in the CAPTCHA market, and ensures that any future competitors will need their own tranches of interaction data in order to build tools that work in a similar way.

Session replay services

Session replay services are tools that website or app owners can install in order to actually record how users interact with their services. These services operate both on websites and in apps. They log keystrokes, mouse movements, taps, swipes, and changes to the page, then allow first-party sites to “re-play” individual users’ experiences after the fact. Often, users are given no indication that their actions are being recorded and shared with third parties.

These creepy tools create a massive risk that sensitive data, like medical information, credit card numbers, or passwords, will be recorded and leaked. The providers of session replay services usually leave it up to their clients to designate certain data as off-limits. But for clients, the process of filtering out sensitive information is subtle, painstaking, and time-consuming, and it clashes with replay services’ promises to get set up “in a matter of seconds.” As a result, independent auditing has found that sensitive data ends up in the recordings, and that session replay service providers often fail to secure that data appropriately.

Passive, real-world tracking

WiFi hotspots and wireless beacons

Many consumer devices emit wireless “probe” signals, and many companies install commercial beacons that intercept these probes all over the physical world. Some devices randomize the unique MAC address device identifiers they share in probes, protecting themselves from passive tracking, but not all do. And connecting to an open WiFi network or giving an app Bluetooth permissions always opens a device up to tracking.

As we discussed above, WiFi hotspots, wireless beacons, and other radio devices can be used to “listen” for nearby devices. Companies like Comcast (which provides XFinity WiFi) and Google (which provides free WiFi in Starbucks and many other businesses) have WiFi hotspots installed all over the world; Comcast alone boasts over 18 million XFinity WiFi installations. Dozens of other companies that you likely haven’t heard of provide free WiFi to coffee shops, restaurants, events, and hotels.

Companies also pay to install wireless beacons in real-world businesses and public spaces. Bluetooth-enabled beacons have been installed around retail stores, at political rallies, in campaign lawn signs, and on streetlight poles.

Wireless beacons are capable of tracking on two levels. First, and most concerning, wireless beacons can passively monitor the “probes” that devices send out all the time. If a device is broadcasting its hardware MAC address, companies can use the probes they collect to track its user’s movement over time.

A laptop emits probe requests containing its a MAC address. Wireless Bbeacons listen for the probes and tie the requests to a profile of the user.

WiFi hotspots and bluetooth beacons can listen for probes that wireless devices send out automatically. Trackers can use each device’s MAC address to create a profile of it based on where they’ve seen that device.

Second, when a user connects to a WiFi hotspot or to a Bluetooth beacon, the controller of the hotspot or beacon can connect the device’s MAC address to additional identifiers like IP address, cookies, and ad ID. Many WiFi hotspot operators also use a sign-in page to collect information about users’ real names or email addresses. Then, when users browse the web from that hotspot, the operator can collect data on all the traffic coming from the user’s device, much like an ISP. Bluetooth beacons are used slightly differently. Mobile phones allow apps to access the Bluetooth interface with certain permissions. Third-party trackers in apps with Bluetooth permissions can automatically connect to Bluetooth beacons in the real world, and they can use those connections to gather fine-grained location data.

Thankfully, both iOS and Android devices now send obfuscated MAC addresses with probes by default. This prevents the first, passive style of tracking described above.

But phones aren’t the only devices with wireless capability. Laptops, e-readers, wireless headphones, and even cars are often outfitted with Bluetooth capability. Some of these devices don’t have the MAC randomization features that recent models of smartphones do, making them vulnerable to passive location tracking.

Furthermore, even devices with MAC randomization usually share static MAC addresses when they actually connect to a wireless hotspot or Bluetooth device. This heightens the risks of the second style of tracking described above, which occurs when the devices connect to public WiFi networks or local Bluetooth beacons.

Vehicle tracking and ALPRs

Automated license plate readers, or ALPRs, are cameras outfitted with the ability to detect and read license plates. They can also use other characteristics of cars, like make, model, color, and wear, in order to help identify them. ALPRs are often used by law enforcement, but many ALPR devices are owned by private companies. These companies collect vehicle data indiscriminately, and once they have it, they can re-sell it to whomever they want: local police, federal immigration enforcement agencies, private data aggregators, insurance companies, lenders, or bounty hunters.

Different companies gather license plate data from different sources, and sell it to different audiences. Digital Recognition Network, or DRN, sources its data from thousands of repossession agencies around the country, and sells data to insurance agencies, private investigators, and “asset recovery” companies. According to an investigation by Motherboard, the vast majority of individuals about whom DRN collects data are not suspected of a crime or behind on car payments. The start-up Flock Safety offers ALPR-powered “neighborhood watch” services. Concerned homeowners can install ALPRs on their property in order to record and share information about cars that drive through their neighborhood.

DRN is owned by VaaS International Holdings, a Fort Worth-based company that brands itself as “the preeminent provider of license plate recognition (‘LPR’) and facial recognition products and data solutions.” It also owns Vigilant Solutions, another private purveyor of ALPR technology. Vigilant’s clients include law enforcement agencies and private shopping centers. Vigilant pools data from thousands of sources around the country into a single database, which it calls “PlateSearch.” Scores of law enforcement agencies pay for access to PlateSearch. According to EFF’s research, approximately 99.5% of the license plates recorded by Vigilant are not connected to a public safety interest at the time they are scanned.

Cameras and machine vision aren’t the only technologies enabling vehicle tracking. Passive MAC address tracking can also be used to track vehicle movement. Phones inside of vehicles, and sometimes the vehicles themselves, broadcast probe requests including their MAC addresses. Wireless beacons placed strategically around roads can listen for those signals. One company, Libelium, sells a wireless beacon that is meant to be installed on streetlights in order to track nearby traffic.

Face recognition cameras

Face recognition has been deployed widely by law enforcement in some countries, including China and the UK. This has frightening implications: it allows mass logging of innocent people’s activities. In China, it has been used to monitor and control members of the Uighur minority community.

We’ve covered the civil liberties harms associated with law enforcement use of face recognition extensively in the past. But face recognition also has been deployed in a number of private industries. Airlines use face recognition to authenticate passengers before boarding. Concert venues and ticket sellers have used it to screen concert-goers. Retailers use face recognition to identify people who supposedly are greater risks for shoplifting, which is especially concerning considering that the underlying mugshot databases are riddled with unfair racial disparities, and the technology is more likely to misidentify people of color. Private security companies sell robots equipped with face recognition to monitor public spaces and help employers keep tabs on employees. And schools and even summer camps use it to keep tabs on kids.

Big tech companies have begun investing in facial recognition for payment processing, which would give them another way to link real-world activity to users’ online personas. Facebook has filed a patent on a system that would link faces to social media profiles in order to process payments. Also, Amazon’s brick-and-mortar “Go” stores rely on biometrics to track who enters and what they take in order to charge them accordingly.

In addition, many see facial recognition as a logical way to bring targeted advertising to the physical world. Face recognition cameras can be installed in stores, on billboards, and in malls to profile people’s behavior, build dossiers on their habits, and target messages at them. In January 2019, Walgreens began a pilot program using face recognition cameras installed on LED-screen fridge doors. The idea is that, instead of looking through a plate of glass to see the contents of a fridge, consumers can look at a screen which will display graphics indicating what’s inside. The camera can perform facial recognition on whoever is standing in front of the fridge, and the graphics can be dynamically changed to serve ads targeted to that person. Whether or not Walgreens ends up deploying this technology at a larger scale, this appears to be one direction retailers are heading.

Payment processors and financial technology

Financial technology, or “fintech,” is a blanket term for the burgeoning industry of finance-adjacent technology companies. Thousands of relatively new tech companies act as the technological glue between old-guard financial institutions and newer technologies, including tracking and surveillance. When they are regulated, fintech companies are often subject to less government oversight than traditional institutions like banks.

Payment processors are companies that accept payments on behalf of other businesses. As a result, they are privy to huge amounts of information about what businesses sell and what people buy. Since most financial transactions involve credit card numbers and names, it is easy for payment processors to tie the data they collect to real identities. Some of these companies are pure service providers, and don’t use data for any purposes other than moving money from one place to another. Others build profiles of consumers or businesses and then monetize that data. For example, Square is a company that makes credit card readers for small businesses. It also uses the information it collects to serve targeted ads from third parties and to underwrite loans through its Square Capital program.

Some fintech companies offer financial services directly to users, like Intuit, the company behind TurboTax and Mint. Others provide services to banks or businesses. In the fintech world, “data aggregators” act as intermediaries between banks and other services, like money management apps. In the process, data aggregators gain access to all the data that passes through their pipes, including account balances, outstanding debts, and credit card transactions for millions of people. In addition, aggregators often collect consumers’ usernames and passwords in order to extract data from their banks. Yodlee, one of the largest companies in the space, sells transaction data to hedge funds, which mine the information to inform stock market moves. Many users are unaware that their data is used for anything other than operating the apps they have signed up for.

Tracking and corporate power

Many of the companies that benefit most from data tracking have compelling ways to entice web developers, app creators, and store managers to install their tracking technology. Companies with monopolies or near-monopolies can use their market power to build tracking networks, monitor and inhibit smaller competitors, and exploit consumer privacy for their own economic advantage. Corporate power and corporate surveillance reinforce one another in several ways.

First, dominant companies like Google and Facebook can pressure publishers into installing their tracking code. Publishers rely on the world’s biggest social network and the world’s biggest search engine to drive traffic to their own sites. As a result, most publishers need to advertise on those platforms. And in order to track how effective their ads are, they have no choice but to install Google and Facebook’s conversion measurement code on their sites and apps. Google, Facebook, and Amazon also act as third-party ad networks, together controlling over two-thirds of the market. That means publishers who want to monetize their content have a hard time avoiding the big platforms’ ad tracking code.

Second, vertically integrated tech companies can gain control of both sides of the tracking market. Google administers the largest behavioral advertising system in the world, which it powers by collecting data from its Android phones and Chrome browser—the most popular mobile operating system and most popular web browser in the world. Compared to its peer operating systems and browsers, Google’s user software makes it easier for its trackers to collect data.

When the designers of the Web first described browsers, they called them “user agents:” pieces of software that would act on their users’ behalf on the Internet. But when a browser maker is also a company whose main source of revenue is behavioral advertising, the company’s interest in user privacy and control is pitted against the company’s interest in tracking. The company’s bottom line usually comes out on top.

Third, data can be used to profile not just people, but also competitor companies. The biggest data collectors don’t just know how we act, they also know more about the market—and their competitors—than anyone else. Google’s tracking tools monitor over 80% of traffic on the Web, which means it often knows as much about it’s competitors’ traffic as its competitors do (or more). Facebook (via third-party ads, analytics, conversion pixels, social widgets, and formerly its VPN app Onavo) also monitors the use and growth of websites, apps, and publishers large and small. Amazon already hosts a massive portion of the Internet in its Amazon Web Services computing cloud, and it is starting to build its own formidable third-party ad network. These giants use this information to identify nascent competitors, and then buy them out or clone their products before they become significant threats. According to confidential internal documents, Facebook used data about users’ app habits from Onavo, its VPN, to inform its acquisition of WhatsApp.

Fourth, as tech giants concentrate tracking power into their own hands, they can use access to data as an anticompetitive cudgel. Facebook was well aware that access to its APIs (and the detailed private data that entailed) were invaluable to other social companies. It has a documented history of granting or withholding access to user data in order to undermine its competition.

Furthermore, Google and Facebook have both begun adopting policies that restrict competitors’ access to their data without limiting what they collect themselves. For example, most of the large platforms now limit the third-party trackers on their own sites. In its own version of RTB, Google has recently begun restricting access to ad identifiers and other information that would allow competing ad networks to build user profiles. And following the Cambridge Analytica incident, Facebook started locking down access to third-party APIs, without meaningfully changing anything about the data that Facebook itself collects on users. On the one hand, restricting third-party access can have privacy benefits. On the other, kicking third-party developers and outside actors off Facebook’s and Google’s platform services can make competition problems worse, give incumbent giants sole power over the user data they have collected, and cement their privacy-harmful business practices. Instead of seeing competition and privacy as isolated concerns, empowering users requires addressing both to reduce large companies’ control over users’ data and attention.

Finally, big companies can acquire troves of data from other companies in mergers and acquisitions. Google Analytics began its life as the independent company Urchin, which Google purchased in 2005. In 2007, Google supercharged its third-party advertising networks by purchasing Doubleclick, then as now a leader in the behaviorally targeted ad market. In late 2019, it purchased the health data company Fitbit, merging years of step counts and exercise logs into its own vast database of users’ physical activity.

In its brief existence, Facebook has acquired 67 other companies. Amazon has acquired 91, and Google, 214—an average of over 10 per year. Many of the smaller firms that Facebook, Amazon, or Google have acquired had access to tremendous amounts of data and millions of active users. With each acquisition, those data sources are folded into the already-massive silos controlled by the tech giants. And thanks to network effects, the data becomes more valuable when it’s all under one roof. On its own, Doubleclick could assemble pseudonymous profiles of users’ browsing history. But as a part of Google, it can merge that data with real names, locations, cross-device activity, search histories, and social graphs.

Multi-billion dollar tech giants are not the only companies tracking us, nor are they the most irresponsible actors in the space. But the bigger they are, the more they know. And the more kinds of data a company has access to, the more powerful its profiles of users and competitors will be. In the new economy of personal information, the rich are only getting richer.

Part 3: Data sharing: Targeting, brokers, and real-time bidding

Where does the data go when it’s collected? Most trackers don’t collect every piece of information by themselves. Instead, companies work together, collecting data for themselves and sharing it with each other. Sometimes, companies with information about the same individual will combine it only briefly to determine which advertiser will serve which ad to that person. In other cases, companies base their entire business model on collecting and selling data about individuals they never interact with. In all cases, the type of data they collect and share can impact their target’s experience, whether by affecting the ads they’re exposed to or by determining which government databases they end up cataloged in. Moreover, the more a user’s data is spread around, the greater the risk that they will be affected by a harmful data breach. This section will explore how personal information gets shared and where it goes.

Real-time bidding

Real-time bidding is the system that publishers and advertisers use to serve targeted ads. The unit of sale in the Internet advertising world is the “impression.” Every time a person visits a web page with an ad, that person views an ad impression. Behind the scenes, an advertiser pays an ad network for the right to show you an ad, and the ad network pays the publisher of the web page where you saw the ad. But before that can happen, the publisher and the ad network have to decide which ad to show. To do so, they conduct a milliseconds-long auction, in which the auctioneer offers up a user’s personal information, and then software on dozens of corporate servers bid on the rights to that user’s attention. Data flows in one direction, and money flows in the other.

Such “real-time bidding” is quite complex, and the topic could use a whitepaper on its own. Luckily, there are tremendous, in-depth resources on the topic already. Dr. Johnny Ryan and Brave have written a series on the privacy impact of RTB. There is also a doctoral thesis on the privacy implications of the protocol. This section will give a brief overview of what the process looks like, much of which is based on Ryan’s work.

//website.com” also shares information, including a cookie and other request headers, with other third-party servers. This information is sent to a Supply-Side Platform (SSP), which is the server that begins the real-time bidding auction . This SSP matches the cookie to user 552EFF, which is Ava’s device. The SSP then fills out a “bid request”, which includes information like year of birth, gender (“f?”), keywords (“coffee, goth”), and geo (“USA”), and sends it to DSP servers.

Supply-side platforms use cookies to identify a user, then distribute “bid requests” with information about the user to potential advertisers.

First, data flows from your browser to the ad networks, also known as “supply-side platforms” (SSPs). In this economy, your data and your attention are the “supply” that ad networks and SSPs are selling. Each SSP receives your identifying information, usually in the form of a cookie, and generates a “bid request” based on what it knows about your past behavior. Next, the SSP sends this bid request to each of the dozens of advertisers who have expressed interest in showing ads.

A screenshot of a table describing the information content of the User object from the AdCOM 1.0 specification.

The `user` object in an OpenRTB bid request contains the information a particular supply-side platform knows about the subject of an impression, including one or more unique IDs, age, gender, location, and interests. Source: https://github.com/InteractiveAdvertisingBureau/AdCOM/blob/master/AdCOM%20v1.0%20FINAL.md#object–user-

The bid request contains information about your location, your interests, and your device, and includes your unique ID. The screenshot above shows the information included in an OpenRTB bid request.

A demand-side platform server winning the bid.

After the auction is complete, winning bidders pay supply-side platforms, SSPs pay the publisher, and the publisher shows the user an ad. At this point, the winning advertiser can collect even more information from the user’s browser.

Finally, it’s the bidders’ turn. Using automated systems, the advertisers look at your info, decide whether they’d like to advertise to you and which ad they want to show, then respond to the SSP with a bid. The SSP determines who won the auction and displays the winner’s ad on the publisher’s web page.

All the information in the bid request is shared before any money changes hands. Advertisers who don’t win the auction still receive the user’s personal information. This enables “shadow bidding.” Certain companies may pretend to be interested in buying impressions, but intentionally bid to lose in each auction with the goal of collecting as much data as possible as cheaply as possible.

Furthermore, there are several layers of companies that participate in RTB between the SSP and the advertisers, and each layer of companies also vacuums up user information. SSPs interface with “ad exchanges,” which share data with “demand side platforms” (DSPs), which also share and purchase data from data brokers. Publishers work with SSPs to sell their ad space, advertisers work with DSPs to buy it, and ad exchanges connect buyers and sellers. You can read a breakdown of the difference between SSPs and DSPs, written for advertisers, here. Everyone involved in the process gets to collect behavioral data about the person who triggered the request.

During the bidding process, advertisers and the DSPs they work with can use third-party data brokers to augment their profiles of individual users. These data brokers, which refer to themselves innocuously as “data management platforms” (DMPs), sell data about individuals based on the identifiers and demographics included in a bid request. In other words, an advertiser can share a user ID with a data broker and receive that user’s behavioral profile in return.

Source: Zhang, W., Yuan, S., Wang, J., and Shen, X. (2014b). Real-time bidding benchmarking with ipinyou dataset. arXiv preprint arXiv:1407.7073.

The diagram above gives another look at the flow of information and money in a single RTB auction.

In summary: (1) a user’s visit to a page triggers an ad request from the page’s publisher to an ad exchange. This is our real-time bidding “auctioneer.” The ad exchange (2) requests bids from advertisers and the DSPs they work with, sending them information about the user in the process. The DSP then (3) augments the bid request data with more information from data brokers, or DMPs. Advertisers (4) respond with a bid for the ad space. After (5) a millisecond-long auction, the ad exchange (6) picks and notifiers the winning advertiser. The ad exchange (7) serves that ad to the user, complete with the tracking technology described above. The advertiser will (8) receive information about how the user interacted with the ad, e.g. how long they looked at it, what they clicked, if they purchased anything, etc. That data will feed back into the DSP’s information about that user and other users who share their characteristics, informing future RTB bids.

From the perspective of the user who visited the page, RTB causes two discrete sets of privacy invasions. First, before they visited the page, an array of companies tracked their personal information, both online and offline, and merged it all into a sophisticated profile about them. Then, during the RTB process, a different set of companies used that profile to decide how much to bid for the ad impression. Second, as a result of the user’s visit to the page, the RTB participants harvest additional information from the visiting user. That information is injected into the user’s old profile, to be used during subsequent RTBs triggered by their next page visits. Thus, RTB is both a cause of tracking and a means of tracking.

RTB on the web: cookie syncing

Cookie syncing is a method that web trackers use to link cookies with one another and combine the data one company has about a user with data that other companies might have.

Mechanically, it’s very simple. One tracking domain triggers a request to another tracker. In the request, the first tracker sends a copy of its own tracking cookie. The second tracker gets both its own cookie and the cookie from the first tracker. This allows it to “compare notes” with the other tracker while building up its profile of the user.

Cookie sharing is commonly used as a part of RTB. In a bid request, the SSP shares its own cookie ID with all of the potential bidders. Without syncing, the demand side platforms might have their own profiles about users linked to their own cookie IDs. A DSP might not know that the user “abc” from Doubleclick (Google’s ad network) is the same as its own user “xyz”. Cookie syncing lets them be sure. As part of the bidding process, SSPs commonly trigger cookie-sync requests to many DSPs at a time. That way, the next time that SSP sends out a bid request, the DSPs who will be bidding can use their own behavioral profiles about the user to decide how to bid.

A laptop makes a request for a hidden element on the page, which kicks off the "cookie sync" process described below.

Cookie syncing. An invisible ‘pixel’ element on the page triggers a request to an ad exchange or SSP, which redirects the user to a DSP. The redirect URL contains information about the SSP’s cookie that lets the DSP link it to its own identifier. A single SSP may trigger cookie syncs to many different DSPs at a time.

RTB in mobile apps

RTB was created for the Web, but it works just as well for ads in mobile apps. Instead of cookies, trackers use ad IDs. The ad IDs baked into iOS and Android make trackers’ jobs easier. On the web, each advertiser has its own cookie ID, and demand-side platforms need to sync data with DMPs and with each other in order to tie their data to a specific user.

But on mobile devices, each user has a single, universal ad ID that is accessible from every app. That means that the syncing procedures described above on the web are not necessary on mobile; advertisers can use ad IDs to confirm identity, share data, and build more detailed profiles upon which to base bids.

Group targeting and look-alike audiences

Sometimes, large platforms do not disclose their data; rather, they lease out temporary access to their data-powered tools. Facebook, Google, and Twitter all allow advertisers to target categories of people with ads. For example, Facebook lets advertisers target users with certain “interests” or “affinities.”

The companies do not show advertisers the actual identities of individuals their campaigns target. If you start a Facebook campaign targeting “people interested in Roller Derby in San Diego,” you can’t see a list of names right away. However, this kind of targeting does allow advertisers to reach out directly to roller derby-going San Diegans and direct them to an outside website or app. When targeted users click on an ad, they are directed off of Facebook and to the advertiser’s domain. At this point, the advertiser knows they came from Facebook and that they are part of the targeted demographic. Once users have landed on the third-party site, the advertiser can use data exchange services to match them with behavioral profiles or even real-world identities.

In addition, Facebook allows advertisers to build “look-alike audiences” based on other groups of people. For example, suppose you’re a payday loan company with a website. You can install an invisible Facebook pixel on a page that your debtors visit, make a list of people who visit that page, and then ask Facebook to create a “look-alike” audience of people who Facebook thinks are “similar” to the ones on your list. You can then target those people with ads on Facebook, directing them back to your website, where you can use cookies and data exchanges to identify who they are.

These “look-alike” features are black boxes. Without the ability to audit or study them, it’s impossible to know what kinds of data they use and what kinds of information about users they might expose. We urge advertisers to disclose more information about them and to allow independent testing.

Data brokers

Data brokers are companies that collect, aggregate, process, and sell data. They operate out of sight from regular users, but in the center of the data-sharing economy. Often, data brokers have no direct relationships with users at all, and the people about whom they sell data may not be aware they exist. Data brokers purchase information from a variety of smaller companies, including retailers, financial technology companies, medical research companies, online advertisers, cellular providers, Internet of Things device manufacturers, and local governments. They then sell data or data-powered services to advertisers, real estate agents, market research companies, colleges, governments, private bounty hunters, and other data brokers.

This is another topic that is far too broad to cover here, and others have written in depth about the data-selling ecosystem. Cracked Labs’ report on corporate surveillance is both accessible and in-depth. Pam Dixon of the World Privacy Forum has also done excellent research into data brokers, including a report from 2014 and testimony before the Senate in 2015 and 2019.

The term “data broker” is broad. It includes “mom and pop” marketing firms that assemble and sell curated lists of phone numbers or emails, and behemoths like Oracle that ingest data from thousands of different streams and offer data-based services to other businesses.

Some brokers sell raw streams of information. This includes data about retail purchase behavior, data from Internet of Things devices, and data from connected cars. Others act as clearinghouses between buyers and sellers of all kinds of data. For example, Narrative promises to help sellers “unlock the value of [their] data” and help buyers “access the data [they] need.” Dawex describes itself as “a global data marketplace where you can meet, sell and buy data directly.”

Another class of companies act as middlemen or “aggregators,” licensing raw data from several different sources, processing it, and repackaging it as a specific service for other businesses. For example, major phone carriers sold access to location data to aggregators called Zumigo and Microbilt, which in turn sold access to a broad array of other companies, with the resulting market ultimately reaching down to bail bondsmen and bounty hunters (and an undercover reporter). EFF is now suing AT&T for selling this data without users’ consent and for misleading the public about its privacy practices.

Many of the largest data brokers don’t sell the raw data they collect. Instead, they collect and consume data from thousands of different sources, then use it to assemble their own profiles and draw inferences about individuals. Oracle, one of the world’s largest data brokers, owns Bluekai, one of the largest third-party trackers on the web. Credit reporting agencies, including Equifax and Experian, are also particularly active here. While the U.S. Fair Credit Reporting Act governs how credit raters can share specific types of data, it doesn’t prevent credit agencies from selling most of the information that trackers collect today, including transaction information and browsing history. Many of these companies advertise their ability to derive psychographics, which are “innate” characteristics that describe user behavior. For example, Experian classifies people into financial categories like “Credit Hungry Card Switcher,” “Disciplined, Passive Borrower,” and “Insecure Debt Dependent,” and claims to cover 95% of the U.S. population. Cambridge Analytica infamously used data about Facebook likes to derive “OCEAN scores”—ratings for openness, conscientiousness, extraversion, agreeableness, and neuroticism—about millions of voters, then sold that data to political campaigns.

Finally, many brokers use their internal profiles to offer “identity resolution” or “enrichment” services to others. If a business has one identifier, like a cookie or email address, it can pay a data broker to “enrich” that data and learn other information about the person. It can also link data tied to one identifier (like a cookie) to data from another (like a mobile ad ID). In the real-time bidding world, these services are known as “data management platforms.” Real-time bidders can use these kinds of services to learn who a particular user is and what their interests are, based only on the ID included with the bid request.

For years, data brokers have operated out of sight and out of mind of the general public. But we may be approaching a turning point. In 2018, Vermont passed the nation’s first law requiring companies that buy and sell third-party data to register with the secretary of state. As a result, we now have access to a list of over 120 data brokers and information about their business models. Furthermore, when the California Consumer Privacy Act goes into effect in 2020, consumers will have the right to access the personal information that brokers have about them for free, and to opt out of having their data sold.

Data consumers

So far, this paper has discussed how data is collected, shared, and sold. But where does it end up? Who are the consumers of personal data, and what do they do with it?

Targeted advertising

By far the biggest, most visible, and most ubiquitous data consumers are targeted advertisers. Targeted advertising allows advertisers to reach users based on demographics, psychographics, and other traits. Behavioral advertising is a subset of targeted advertising that leverages data about users’ past behavior in order to personalized ads.

The biggest data collectors are also the biggest targeted advertisers. Together, Google and Facebook control almost 60% of the digital ad market in the U.S., and they use their respective troves of data in order to do so. Google, Facebook, Amazon, and Twitter offer end-to-end targeting services where advertisers can target high-level categories of users, and the advertisers don’t need to have access to any data themselves. Facebook lets advertisers target users based on location; demographics like age, gender, education, and income; and interests like hobbies, music genres, celebrities, and political leaning. Some of the “interests” Facebook uses are based on what users have “liked” or commented on, and others are derived based on Facebook’s third-party tracking. While Facebook uses its data to match advertisers to target audiences, Facebook does not share its data with those advertisers.

Real-time bidding (RTB) involves more data sharing, and there are a vast array of smaller companies involved in different levels of the process. The big tech companies offer services in this space as well: Google’s Doubleclick Bid Manager and Amazon DSP are both RTB demand-side platforms. In RTB, identifiers are shared so that the advertisers themselves (or their agents) can decide whether they want to reach each individual and what ad they want to show. In the RTB ecosystem, advertisers collect their own data about how users behave, and they may use in-house machine learning models in order to predict which users are most likely to engage with their ads or buy their products.

Some advertisers want to reach users on Facebook or Google, but don’t want to use the big companies’ proprietary targeting techniques. Instead, they can buy lists of contact information from data brokers, then upload those lists directly to Facebook or Google, who will reach those users across all of their platforms. This system undermines big companies’ efforts to rein in discriminatory or otherwise malicious targeting. Targeting platforms like Google and Facebook do not allow advertisers to target users of particular ethnicities with ads for jobs, housing, or credit. However, advertisers can buy demographic information about individuals from data brokers, upload a list of names who happen to be from the same racial group, and have the platform target those people directly. Both Google and Facebook forbid the use of “sensitive information” to target people with contact lists, but it’s unclear how they enforce these policies.

Political campaigns and interest groups

Companies aren’t the only entities that try to benefit from data collection and targeted advertising. Cambridge Analytica used ill-gotten personal data to estimate “psychographics” for millions of potential voters, then used that data to help political campaigns. In 2018, the group CatholicVote used cell-phone location data to determine who had been inside a Catholic church, then targeted them with “get out the vote” ads. Anti-abortion groups used similar geo-fencing technology to target ads to women while they were at abortion clinics..

And those incidents are not isolated. Some non-profits that rely on donations buy data to help narrow in on potential donors. Many politicians around the country have used open voter registration data to target voters. The Democratic National Committee is reportedly investing heavily in its “data warehouse” ahead of the 2020 election. And Deep Root Analytics, a consulting firm for the Republican party, was the source of the largest breach of US voter data in history; it had been collecting names, registration details, and “modeled” ethnicity and religion data about nearly 200 million Americans.

Debt collectors, bounty hunters, and fraud investigators

Debt collectors, bounty hunters, and repossession agencies all purchase and use location data from a number of sources. EFF is suing AT&T for its role in selling location data to aggregators, which enabled a secondary market that allowed access by bounty hunters. However, phone carriers aren’t the only source of that data. The bail bond company Captira sold location data gathered from cell phones and ALPRs to bounty hunters for as little as $7.50. And thousands of apps collect “consensual” location data using GPS permissions, then sell that data to downstream aggregators. This data can be used to locate fugitives, debtors, and those who have not kept up with car payments. And as investigations have shown, it can also be purchased—and abused—by nearly anyone.

Cities, law enforcement, intelligence agencies

The public sector also purchases data from the private sector for all manner of applications. For example, U.S. Immigration and Customs Enforcement bought ALPR data from Vigilant to help locate people the agency intends to deport. Government agencies contract with data brokers for myriad tasks, from determining eligibility for human services to tax collection, according to the League of California Cities, in a letter seeking an exception from that state’s consumer data privacy law for contracts between government agencies and data brokers. Advocates have long decried these arrangements between government agencies and private data brokers as a threat to consumer data privacy, as well as an end-run around legal limits on governments’ own databases. And of course, national security surveillance often rests on the data mining of private companies’ reservoirs of consumer data. For example, as part of the PRISM program revealed by Edward Snowden, the NSA collected personal data directly from Google, YouTube, Facebook, and Yahoo.

Part 4: Fighting back

You might want to resist tracking to avoid being targeted by invasive or manipulative ads. You might be unhappy that your private information is being bartered and sold behind your back. You might be concerned that someone who wishes you harm can access your location through a third-party data broker. Perhaps you fear that data collected by corporations will end up in the hands of police and intelligence agencies. Or third-party tracking might just be a persistent nuisance that gives you a vague sense of unease.

But the unfortunate reality is that tracking is hard to avoid. With thousands of independent actors using hundreds of different techniques, corporate surveillance is widespread and well-funded. While there’s no switch to flip that can prevent every method of tracking, there’s still a lot that you can do to take back your privacy. This section will go over some of the ways that privacy-conscious users can avoid and disrupt third-party tracking.

Each person should decide for themselves how much effort they’re willing to put into protecting their privacy. Small changes can seriously cut back on the amount of data that trackers can collect and share, like installing EFF’s tracker-blocker extension Privacy Badger in your browser and changing settings on a phone. Bigger changes, like uninstalling third-party apps and using Tor, can offer stronger privacy guarantees at the cost of time, convenience, and sometimes money. Stronger measures may be worth it for users who have serious concerns.

Finally, keep in mind that none of this is your fault. Privacy shouldn’t be a matter of personal responsibility. It’s not your job to obsess over the latest technologies that can secretly monitor you, and you shouldn’t have to read through a quarter million words of privacy-policy legalese to understand how your phone shares data. Privacy should be a right, not a privilege for the well-educated and those flush with spare time. Everyone deserves to live in a world—online and offline—that respects their privacy.

In a better world, the companies that we choose to share our data with would earn our trust, and everyone else would mind their own business. That’s why EFF files lawsuits to compel companies to respect consumers’ data privacy, and why we support legislation that would make privacy the law of the land. With the help of our members and supporters, we are making progress, but changing corporate surveillance policies is a long and winding path. So for now, let’s talk about how you can fight back.

On the web

There are several ways to limit your exposure to tracking on the Web. First, your choice of browser matters. Certain browser developers take more seriously their software’s role as a “user agent” acting on your behalf. Apple’s Safari takes active measures against the most common forms of tracking, including third-party cookies, first-to-third party cookie sharing, and fingerprinting. Mozilla’s Firefox blocks third-party cookies from known trackers by default, and Firefox’s Private Browsing mode will block requests to trackers altogether.

Browser extensions like EFF’s Privacy Badger and uBlock Origin offer another layer of protection. In particular, Privacy Badger learns to block trackers using heuristics, which means it might catch new or uncommon trackers that static, list-based blockers miss. This makes Privacy Badger a good supplement to the built-in protections offered by Firefox, which rely on the Disconnect list. And while Google Chrome does not block any tracking behavior by default, installing Privacy Badger or another tracker-blocking extension in Chrome will allow you to use it with relatively little exposure to tracking. (However, planned changes in Chrome will likely affect the security and privacy tools that many use to block tracking.)

The browser extension, Privacy Badger, blocks a third-party tracker

Browser extensions like EFF’s Privacy Badger offer a layer of protection against third-party tracking on the web. Privacy Badger learns to block trackers using heuristics, which means it might catch new or uncommon trackers that static, list-based blockers miss.

No tracker blocker is perfect. All tracker blockers must make exceptions for companies that serve legitimate content. Privacy Badger, for example, maintains a list of domains which are known to perform tracking behaviors as well as serving content that is necessary for many sites to function, such as content delivery networks and video hosts. Privacy Badger restricts those domains’ ability to track by blocking cookies and access to local storage, but dedicated trackers can still access IP addresses, TLS state, and some kinds of fingerprintable data.

If you’d like to go the extra mile and are comfortable with tinkering, you can install a network-level filter in your home. Pi-hole filters all traffic on a local network at the DNS level. It acts as a personal DNS server, rejecting requests to domains which are known to host trackers. Pi-hole blocks tracking requests coming from devices which are otherwise difficult to configure, like smart TVs, game consoles, and Internet of Things products.

For people who want to reduce their exposure as much as possible, Tor Browser is the gold standard for privacy. Tor uses an onion routing service to totally mask its users’ IP addresses. It takes aggressive steps to reduce fingerprinting, like blocking access to the HTML canvas by default. It completely rejects TLS session tickets and clears cookies at the end of each session.

Unfortunately, browsing the web with Tor in 2019 is not for everyone. It significantly slows down traffic, so pages take much longer to load, and streaming video or other real-time content is very difficult. Worse, much of the modern web relies on invisible CAPTCHAs that block or throttle traffic from sources deemed “suspicious.” Traffic from Tor is frequently classified as high-risk, so doing something as simple as a Google search with Tor can trigger CAPTCHA tests. And since Tor is a public network which attackers also use, some websites will block Tor visitors altogether.

On mobile phones

Blocking trackers on mobile devices is more complicated. There isn’t one solution, like a browser or an extension, that can cover many bases. And unfortunately, it’s simply not possible to control certain kinds of tracking on certain devices.

The first line of defense against tracking is your device’s settings.

App permissions page. “ width=“1081″ height=“1849″>

Both iOS and Android let users view and control the permissions that each app has access to. You should check the permissions that your apps have, and remove the permissions that aren’t needed. While you are at it, you might simply remove the apps you are not using. In addition to per-app settings, you can change global settings that affect how your device collects and shares particularly sensitive information, like location. You can also control how apps are allowed to access the Internet when they are not in use, which can prevent passive tracking.

Both operating systems also have options to reset your device’s ad ID in different ways. On iOS, you can remove the ad ID entirely by setting it to a string of zeros. (Here are some other ways to block ad tracking on iOS.) On Android, you can manually reset it. This is equivalent to clearing your cookies, but not blocking new ones: it won’t disable tracking entirely, but will make it more difficult for trackers to build a unified profile about you.

Android also has a setting to “opt out of interest-based ads.” This sends a signal to apps that the user does not want to have their data used for targeted ads, but it doesn’t actually stop the apps from doing so by means of the ad ID. Indeed, recent research found that tens of thousands of apps simply ignore the signal.

On iOS, there are a handful of apps that can filter tracking activity from other apps. On Android, it’s not so easy. Google bans ad- and tracker-blockers from its app store, the Play Store, so it has no officially vetted apps of this kind. It’s possible to “side-load” blockers from outside of the Play Store, but this can be very risky. Make sure you only install apps from publishers you trust, preferably with open source code.

You should also think about the networks your devices are communicating with. It is best to avoid connecting to unfamiliar public WiFi networks. If you do, the “free” WiFi probably comes at the cost of your data.

Wireless beacons are also trying to collect information from your device. They can only collect identifying information if your devices are broadcasting their hardware MAC addresses. Both iOS and Android now randomize these MAC addresses by default, but other kinds of devices may not. Your e-reader, smart watch, or car may be broadcasting probe requests that trackers can use to derive location data. To prevent this, you can usually turn off WiFi and Bluetooth or set your device to “airplane mode.” (This is also a good way to save battery!)

Finally, if you really need to be anonymous, using a “burner phone” can help you control tracking associated with inherent hardware identifiers.

IRL

In the real world, opting out isn’t so simple.

As we’ve described, there are many ways to modify the way your devices work to prevent them from working against you. But it’s almost impossible to avoid tracking by face recognition cameras and automatic license plate readers. Sure, you can paint your face to disrupt face recognition algorithms, you can choose not to own a car to stay out of ALPR companies’ databases, and you can use cash or virtual credit cards to stop payment processors from profiling you. But these options aren’t realistic for most people most of the time, and it’s not feasible for anyone to avoid all the tracking that they’re exposed to.

Knowledge is, however, half the battle. For now, face recognition cameras are most likely to identify you in specific locations, like airports, during international travel. ALPR cameras are much more pervasive and harder to avoid, but if absolutely necessary, it is possible to use public transit or other transportation methods to limit how often your vehicle is tracked.

In the legislature

Some jurisdictions have laws to protect users from tracking. The General Data Protection Regulation (GDPR) in the European Union gives those it covers the right to access and delete information that’s been collected about them. It also requires companies to have a legitimate reason to use data, which could come from a “legitimate interest” or opt-in consent. The GDPR is far from perfect, and its effectiveness will depend on how regulators and courts implement it in the years to come. But it gives meaningful rights to users and prescribes real consequences for companies who violate them.

In the U.S., a smattering of state and federal laws offer specific protections to some. Vermont’s data privacy law brings transparency to data brokers. The Illinois Biometric Information Protection Act (BIPA) requires companies to get consent from users before collecting or sharing biometric identifiers. In 2020, the California Consumer Privacy Act (CCPA) will take effect, giving users there the right to access their personal information, delete it, and opt out of its sale. Some communities have passed legislation to limit government use of face recognition, and more plan to pass it soon.

At the federal level, some information in some circumstances is protected by laws like HIPAA, FERPA, COPPA, the Video Privacy Protection Act, and a handful of financial data privacy laws. However, these sector-specific federal statutes apply only to specific types information about specific types of people when held by specific businesses. They have many gaps, which are exploited by trackers, advertisers, and data brokers.

To make a long story very short, most third-party data collection in the U.S. is unregulated. That’s why EFF advocates for new laws to protect user privacy. People should have the right to know what personal information is collected about them and what is done with it. We should be free from corporate processing of our data unless we give our informed opt-in consent. Companies shouldn’t be able to charge extra or degrade service when users choose to exercise their privacy rights. They should be held accountable when they misuse or mishandle our data. And people should have the right to take companies to court when their privacy is violated.

The first step is to break the one-way mirror. We need to shed light on the tangled network of trackers that lurk in the shadows behind the glass. In the sunlight, these systems of commercial surveillance are exposed for what they are: Orwellian, but not omniscient; entrenched, but not inevitable. Once we, the users, understand what we’re up against, we can fight back.

Source: https://www.eff.org/wp/behind-the-one-way-mirror

Why robots will soon be picking soft fruits and salad

London (CNN Business)

It takes a certain nimbleness to pick a strawberry or a salad. While crops like wheat and potatoes have been harvested mechanically for decades, many fruits and vegetables have proved resistant to automation. They are too easily bruised, or too hard for heavy farm machinery to locate.

But recently, technological developments and advances in machine learning have led to successful trials of more sensitive and dexterous robots, which use cameras and artificial intelligence to locate ripe fruit and handle it with care and precision.
Developed by engineers at the University of Cambridge, the Vegebot is the first robot that can identify and harvest iceberg lettuce — bringing hope to farmers that one of the most demanding crops for human pickers could finally be automated.
First, a camera scans the lettuce and, with the help of a machine learning algorithm trained on more than a thousand lettuce images, decides if it is ready for harvest. Then a second camera guides the picking cage on top of the plant without crushing it. Sensors feel when it is in the right position, and compressed air drives a blade through the stalk at a high force to get a clean cut.

The Vegebot uses machine learning to identify ripe, immature and diseased lettuce heads

Its success rate is high, with 91% of the crop accurately classified, according to a study published in July. But the robot is still much slower than humans, taking 31 seconds on average to pick one lettuce. Researchers say this could easily be sped up by using lighter materials.
Such adjustments would need to be made if the robot was used commercially. „Our goal was to prove you can do it, and we’ve done it,“ Simon Birrell, co-author of the study, tells CNN Business. „Now it depends on somebody taking the baton and running forward,“ he says.

More mouths to feed, but less manual labor

With the world’s population expected to climb to 9.7 billion in 2050 from 7.7 billion today — meaning roughly 80 million more mouths to feed each year — agriculture is under pressure to meet rising demand for food production.
Added pressures from climate change, such as extreme weather, shrinking agricultural lands and the depletion of natural resources, make innovation and efficiency all the more urgent.
This is one reason behind the industry’s drive to develop robotics. The global market for agricultural drones and robots is projected to grow from $2.5 billion in 2018 to $23 billion in 2028, according to a report from market intelligence firm BIS Research.
„Agriculture robots are expected to have a higher operating speed and accuracy than traditional agriculture machinery, which shall lead to significant improvements in production efficiency,“ Rakhi Tanwar, principal analyst of BIS Research, tells CNN Business.

Fruit picking robots like this one, developed by Fieldwork Robotics, operate for more than 20 hours a day

On top of this, growers are facing a long-term labor shortage. According to the World Bank, the share of total employment in agriculture in the world has declined from 43% in 1991 to 28% in 2018.
Tanwar says this is partly due to a lack of interest from younger generations. „The development of robotics in agriculture could lead to a massive relief to the growers who suffer from economic losses due to labor shortage,“ she says.
Robots can work all day and night, without stopping for breaks, and could be particularly useful during intense harvest periods.
„The main benefit is durability,“ says Martin Stoelen, a lecturer in robotics at the University of Plymouth and founder of Fieldwork Robotics, which has developed a raspberry-picking robot in partnership with Hall Hunter, one of the UK’s major berry growers.
Their robots, expected to go into production next year, will operate more than 20 hours a day and seven days a week during busy periods, „which human pickers obviously can’t do,“ says Stoelen.

Octinion's robot picks one strawberry every five seconds

Sustainable farming and food waste

Robots could also lead to more sustainable farming practices. They could enable growers to use less water, less fuel, and fewer pesticides, as well as producing less waste, says Tanwar.
At the moment, a field is typically harvested once, and any unripe fruits or vegetables are left to rot. Whereas, a robot could be trained to pick only ripe vegetables and, working around the clock, it could come back to the same field multiple times to pick any stragglers.
Birrell says that this will be the most important impact of robot pickers. „Right now, between a quarter and a third of food just rots in the field, and this is often because you don’t have humans ready at the right time to pick them,“ he says.
A successful example of this is the strawberry-picking robot developed by Octinion, a Belgium-based engineering startup.
The robot — which launched this year and is being used by growers in the UK and the Netherlands — is mounted on a self-driving trolley to serve table top strawberry production.
It uses 3D vision to locate the ripe berry, softly grips it with a pair of plastic pincers, and — just like a human — turns it 90 degrees to snap it from the stalk, before dropping it gently into a punnet.
„Robotics have the potential to convert the market from (being) supply-driven to demand-driven,“ says Tom Coen, CEO and founder of Octinion. „That will then help to reduce food waste and increase prices,“ he adds.

Harsh conditions

One major challenge with agricultural robots is adapting them for all-weather conditions. Farm machinery tends to be heavy-duty so that it can withstand rain, snow, mud, dust and heat.
„Building robots for agriculture is very different to building it for factories,“ says Birrell. „Until you’re out in the field, you don’t realize how robust it needs to be — it gets banged and crashed, you go over uneven surfaces, you get rained on, you get dust, you get lightning bolts.“
California-based Abundant Robotics has built an apple robot to endure the full range of farm conditions. It consists of an apple-sucking tube on a tractor-like contraption, which drives itself down an orchard row, while using computer vision to locate ripe fruit.
This spells the start of automation for orchard crops, says Dan Steere, CEO of Abundant Robotics. „Automation has steadily improved agricultural productivity for centuries,“ he says. „[We] have missed out on much of those benefits until now.“

What Proroguing UK Parliament means to Brexit – UK Parliament Suspension

Source: https://edition.cnn.com/2019/08/28/uk/uk-parliament-suspension-what-it-means-for-brexit-gbr-intl/index.html

 

the combination of repressive regimes with IT monopolies endows those regimes with a built-in advantage over open societies

Source: https://www.wired.com/story/mortal-danger-chinas-push-into-ai/

Governments and companies worldwide are investing heavily in artificial intelligence in hopes of new profits, smarter gadgets, and better health care. Financier and philanthropist George Soros told the World Economic Forum in Davos Thursday that the technology may also undermine free societies and create a new era of authoritarianism.

“I want to call attention to the mortal danger facing open societies from the instruments of control that machine learning and artificial intelligence can put in the hands of repressive regimes,” Soros said. He made an example of China, repeatedly calling out the country’s president, Xi Jinping.

China’s government issued a broad AI strategy in 2017, asserting that it would surpass US prowess in the technology by 2030. As in the US, much of the leading work on AI in China takes place inside a handful of large tech companies, such as search engine Baidu and retailer and payments company Alibaba.

Soros argued that AI-centric tech companies like those can become enablers of authoritarianism. He pointed to China’s developing “social credit” system, aimed at tracking citizens’ reputations by logging financial activity, online interactions, and even energy use, among other things. The system is still taking shape, but depends on data and cooperation from companies like payments firm Ant Financial, a spinout of Alibaba. “The social credit system, if it became operational, would give Xi Jinping total control over the people,” Soros said.

Soros argued that synergy like that between corporate and government AI projects creates a more potent threat than was posed by Cold War–era autocrats, many of whom spurned corporate innovation. “The combination of repressive regimes with IT monopolies endows those regimes with a built-in advantage over open societies,” Soros said. “They pose a mortal threat to open societies.”

Soros is far from the first to raise an alarm about the dangers of AI technology. It’s a favorite topic of Elon Musk, and last year Henry Kissinger called for a US government commission to examine the technology’s risks. Google cofounder Sergey Brin warned in Alphabet’s most recent annual shareholder letter that AI technology had downsides, including the potential to manipulate people. Canada and France plan to establish an intergovernmental group to study how AI changes societies.

The financier attempted to draft Donald Trump into his AI vigilance campaign. He advised the president to be tougher on Chinese telecoms manufacturers ZTE and Huawei, to prevent them from dominating the high-bandwidth 5G mobile networks being built around the world. Both companies are already reeling from sanctions by the US and other governments.

Soros also urged the well-heeled attendees of Davos to help forge international mechanisms to prevent AI-enhanced authoritarianism—and that could both include and contain China. He asked them to imagine a technologically oriented version of the treaty signed after World War II that underpins the United Nations, binding countries into common standards for human rights and freedoms.

Here is the text of Soros’s speech:

I want to use my time tonight to warn the world about an unprecedented danger that’s threatening the very survival of open societies.

Last year when I stood before you I spent most of my time analyzing the nefarious role of the IT monopolies. This is what I said: “An alliance is emerging between authoritarian states and the large data rich IT monopolies that bring together nascent systems of corporate surveillance with an already developing system of state sponsored surveillance. This may well result in a web of totalitarian control the likes of which not even George Orwell could have imagined.”

Tonight I want to call attention to the mortal danger facing open societies from the instruments of control that machine learning and artificial intelligence can put in the hands of repressive regimes. I’ll focus on China, where Xi Jinping wants a one-party state to reign supreme.

A lot of things have happened since last year and I’ve learned a lot about the shape that totalitarian control is going to take in China.

All the rapidly expanding information available about a person is going to be consolidated in a centralized database to create a “social credit system.” Based on that data, people will be evaluated by algorithms that will determine whether they pose a threat to the one-party state. People will then be treated accordingly.

The social credit system is not yet fully operational, but it’s clear where it’s heading. It will subordinate the fate of the individual to the interests of the one-party state in ways unprecedented in history.

I find the social credit system frightening and abhorrent. Unfortunately, some Chinese find it rather attractive because it provides information and services that aren’t currently available and can also protect law-abiding citizens against enemies of the state.

China isn’t the only authoritarian regime in the world, but it’s undoubtedly the wealthiest, strongest and most developed in machine learning and artificial intelligence. This makes Xi Jinping the most dangerous opponent of those who believe in the concept of open society. But Xi isn’t alone. Authoritarian regimes are proliferating all over the world and if they succeed, they will become totalitarian.

As the founder of the Open Society Foundations, I’ve devoted my life to fighting totalizing, extremist ideologies, which falsely claim that the ends justify the means. I believe that the desire of people for freedom can’t be repressed forever. But I also recognize that open societies are profoundly endangered at present.

What I find particularly disturbing is that the instruments of control developed by artificial intelligence give an inherent advantage to authoritarian regimes over open societies. For them, instruments of control provide a useful tool; for open societies, they pose a mortal threat.

I use “open society” as shorthand for a society in which the rule of law prevails as opposed to rule by a single individual and where the role of the state is to protect human rights and individual freedom. In my personal view, an open society should pay special attention to those who suffer from discrimination or social exclusion and those who can’t defend themselves.

By contrast, authoritarian regimes use whatever instruments of control they possess to maintain themselves in power at the expense of those whom they exploit and suppress.

How can open societies be protected if these new technologies give authoritarian regimes a built-in advantage? That’s the question that preoccupies me. And it should also preoccupy all those who prefer to live in an open society.

Open societies need to regulate companies that produce instruments of control, while authoritarian regimes can declare them “national champions.” That’s what has enabled some Chinese state-owned companies to catch up with and even surpass the multinational giants.

This, of course, isn’t the only problem that should concern us today. For instance, man-made climate change threatens the very survival of our civilization. But the structural disadvantage that confronts open societies is a problem which has preoccupied me and I’d like to share with you my ideas on how to deal with it.

My deep concern for this issue arises out of my personal history. I was born in Hungary in 1930 and I’m Jewish. I was 13 years old when the Nazis occupied Hungary and started deporting Jews to extermination camps.

I was very fortunate because my father understood the nature of the Nazi regime and arranged false identity papers and hiding places for all members of his family, and for a number of other Jews as well. Most of us survived.

The year 1944 was the formative experience of my life. I learned at an early age how important it is what kind of political regime prevails. When the Nazi regime was replaced by Soviet occupation I left Hungary as soon as I could and found refuge in England.

At the London School of Economics I developed my conceptual framework under the influence of my mentor, Karl Popper. That framework proved to be unexpectedly useful when I found myself a job in the financial markets. The framework had nothing to do with finance, but it is based on critical thinking. This allowed me to analyze the deficiencies of the prevailing theories guiding institutional investors. I became a successful hedge fund manager and I prided myself on being the best paid critic in the world.

Running a hedge fund was very stressful. When I had made more money than I needed for myself or my family, I underwent a kind of midlife crisis. Why should I kill myself to make more money? I reflected long and hard on what I really cared about and in 1979 I set up the Open Society Fund. I defined its objectives as helping to open up closed societies, reducing the deficiencies of open societies and promoting critical thinking.

My first efforts were directed at undermining the apartheid system in South Africa. Then I turned my attention to opening up the Soviet system. I set up a joint venture with the Hungarian Academy of Science, which was under Communist control, but its representatives secretly sympathized with my efforts. This arrangement succeeded beyond my wildest dreams. I got hooked on what I like to call “political philanthropy.” That was in 1984.

In the years that followed, I tried to replicate my success in Hungary and in other Communist countries. I did rather well in the Soviet empire, including the Soviet Union itself, but in China it was a different story.

My first effort in China looked rather promising. It involved an exchange of visits between Hungarian economists who were greatly admired in the Communist world, and a team from a newly established Chinese think tank which was eager to learn from the Hungarians.

Based on that initial success, I proposed to Chen Yizi, the leader of the think tank, to replicate the Hungarian model in China. Chen obtained the support of Premier Zhao Ziyang and his reform-minded policy secretary Bao Tong.

A joint venture called the China Fund was inaugurated in October 1986. It was an institution unlike any other in China. On paper, it had complete autonomy.

Bao Tong was its champion. But the opponents of radical reforms, who were numerous, banded together to attack him. They claimed that I was a CIA agent and asked the internal security agency to investigate. To protect himself, Zhao Ziyang replaced Chen Yizi with a high-ranking official in the external security police. The two organizations were co-equal and they couldn’t interfere in each other’s affairs.

I approved this change because I was annoyed with Chen Yizi for awarding too many grants to members of his own institute and I was unaware of the political infighting behind the scenes. But applicants to the China Fund soon noticed that the organization had come under the control of the political police and started to stay away. Nobody had the courage to explain to me the reason for it.

Eventually, a Chinese grantee visited me in New York and told me, at considerable risk to himself. Soon thereafter, Zhao Ziyang was removed from power and I used that excuse to close the foundation. This happened just before the Tiananmen Square massacre in 1989 and it left a “black spot” on the record of the people associated with the foundation. They went to great length to clear their names and eventually they succeeded.

In retrospect, it’s clear that I made a mistake in trying to establish a foundation which operated in ways that were alien to people in China. At that time, giving a grant created a sense of mutual obligation between the donor and recipient and obliged both of them to remain loyal to each other forever.

So much for history. Let me now turn to the events that occurred in the last year, some of which surprised me.

When I first started going to China, I met many people in positions of power who were fervent believers in the principles of open society. In their youth they had been deported to the countryside to be re-educated, often suffering hardships far greater than mine in Hungary. But they survived and we had much in common. We had all been on the receiving end of a dictatorship.

They were eager to learn about Karl Popper’s thoughts on the open society. While they found the concept very appealing, their interpretation remained somewhat different from mine. They were familiar with Confucian tradition, but there was no tradition of voting in China. Their thinking remained hierarchical and carried a built-in respect for high office. I, on the other hand I was more egalitarian and wanted everyone to have a vote.

So, I wasn’t surprised when Xi Jinping ran into serious opposition at home; but I was surprised by the form it took. At last summer’s leadership convocation at the seaside resort of Beidaihe, Xi Jinping was apparently taken down a peg or two. Although there was no official communique, rumor had it that the convocation disapproved of the abolition of term limits and the cult of personality that Xi had built around himself.

It’s important to realize that such criticisms were only a warning to Xi about his excesses, but did not reverse the lifting of the two-term limit. Moreover, “The Thought of Xi Jinping,” which he promoted as his distillation of Communist theory was elevated to the same level as the “Thought of Chairman Mao.” So Xi remains the supreme leader, possibly for lifetime. The ultimate outcome of the current political infighting remains unresolved.

I’ve been concentrating on China, but open societies have many more enemies, Putin’s Russia foremost among them. And the most dangerous scenario is when these enemies conspire with, and learn from, each other on how to better oppress their people.

The question poses itself, what can we do to stop them?

The first step is to recognize the danger. That’s why I’m speaking out tonight. But now comes the difficult part. Those of us who want to preserve the open society must work together and form an effective alliance. We have a task that can’t be left to governments.

History has shown that even governments that want to protect individual freedom have many other interests and they also give precedence to the freedom of their own citizens over the freedom of the individual as a general principle.

My Open Society Foundations are dedicated to protecting human rights, especially for those who don’t have a government defending them. When we started four decades ago there were many governments which supported our efforts but their ranks have thinned out. The US and Europe were our strongest allies, but now they’re preoccupied with their own problems.

Therefore, I want to focus on what I consider the most important question for open societies: what will happen in China?

The question can be answered only by the Chinese people. All we can do is to draw a sharp distinction between them and Xi Jinping. Since Xi has declared his hostility to open society, the Chinese people remain our main source of hope.

And there are, in fact, grounds for hope. As some China experts have explained to me, there is a Confucian tradition, according to which advisors of the emperor are expected to speak out when they strongly disagree with one of his actions or decrees, even that may result in exile or execution.

This came as a great relief to me when I had been on the verge of despair. The committed defenders of open society in China, who are around my age, have mostly retired and their places have been taken by younger people who are dependent on Xi Jinping for promotion. But a new political elite has emerged that is willing to uphold the Confucian tradition. This means that Xi will continue to have a political opposition at home.

Xi presents China as a role model for other countries to emulate, but he’s facing criticism not only at home but also abroad. His Belt and Road Initiative has been in operation long enough to reveal its deficiencies.

It was designed to promote the interests of China, not the interests of the recipient countries; its ambitious infrastructure projects were mainly financed by loans, not by grants, and foreign officials were often bribed to accept them. Many of these projects proved to be uneconomic.

The iconic case is in Sri Lanka. China built a port that serves its strategic interests. It failed to attract sufficient commercial traffic to service the debt and enabled China to take possession of the port. There are several similar cases elsewhere and they’re causing widespread resentment.

Malaysia is leading the pushback. The previous government headed by Najib Razak sold out to China but in May 2018 Razak was voted out of office by a coalition led by Mahathir Mohamed. Mahathir immediately stopped several big infrastructure projects and is currently negotiating with China how much compensation Malaysia will still have to pay.

The situation is not as clear-cut in Pakistan, which has been the largest recipient of Chinese investments. The Pakistani army is fully beholden to China but the position of Imran Khan who became prime minister last August is more ambivalent. At the beginning of 2018, China and Pakistan announced grandiose plans in military cooperation. By the end of the year, Pakistan was in a deep financial crisis. But one thing became evident: China intends to use the Belt and Road Initiative for military purposes as well.

All these setbacks have forced Xi Jinping to modify his attitude toward the Belt and Road Initiative. In September, he announced that “vanity projects” will be shunned in favor of more carefully conceived initiatives and in October, the People’s Daily warned that projects should serve the interests of the recipient countries.

Customers are now forewarned and several of them, ranging from Sierra Leone to Ecuador, are questioning or renegotiating projects.

Most importantly, the US government has now identified China as a “strategic rival.” President Trump is notoriously unpredictable, but this decision was the result of a carefully prepared plan. Since then, the idiosyncratic behavior of Trump has been largely superseded by a China policy adopted by the agencies of the administration and overseen by Asian affairs advisor of the National Security Council Matt Pottinger and others. The policy was outlined in a seminal speech by Vice President Mike Pence on October 4th.

Even so, declaring China a strategic rival is too simplistic. China is an important global actor. An effective policy towards China can’t be reduced to a slogan.

It needs to be far more sophisticated, detailed and practical; and it must include an American economic response to the Belt and Road Initiative. The Pottinger plan doesn’t answer the question whether its ultimate goal is to level the playing field or to disengage from China altogether.

Xi Jinping fully understood the threat that the new US policy posed for his leadership. He gambled on a personal meeting with President Trump at the G20 meeting in Buenos Aires. In the meantime, the danger of global trade war escalated and the stock market embarked on a serious sell-off in December. This created problems for Trump who had concentrated all his efforts on the 2018 midterm elections. When Trump and Xi met, both sides were eager for a deal. No wonder that they reached one, but it’s very inconclusive: a ninety-day truce.

In the meantime, there are clear indications that a broad based economic decline is in the making in China, which is affecting the rest of the world. A global slowdown is the last thing the market wants to see.

The unspoken social contract in China is built on steadily rising living standards. If the decline in the Chinese economy and stock market is severe enough, this social contract may be undermined and even the business community may turn against Xi Jinping. Such a downturn could also sound the death knell of the Belt and Road Initiative, because Xi may run out of resources to continue financing so many lossmaking investments.

On the question of global internet governance, there’s an undeclared struggle between the West and China. China wants to dictate rules and procedures that govern the digital economy by dominating the developing world with its new platforms and technologies. This is a threat to the freedom of the Internet and indirectly open society itself.

Last year I still believed that China ought to be more deeply embedded in the institutions of global governance, but since then Xi Jinping’s behavior has changed my opinion. My present view is that instead of waging a trade war with practically the whole world, the US should focus on China. Instead of letting ZTE and Huawei off lightly, it needs to crack down on them. If these companies came to dominate the 5G market, they would present an unacceptable security risk for the rest of the world.

Regrettably, President Trump seems to be following a different course: make concessions to China and declare victory while renewing his attacks on US allies. This is liable to undermine the US policy objective of curbing China’s abuses and excesses.

To conclude, let me summarize the message I’m delivering tonight. My key point is that the combination of repressive regimes with IT monopolies endows those regimes with a built-in advantage over open societies. The instruments of control are useful tools in the hands of authoritarian regimes, but they pose a mortal threat to open societies.

China is not the only authoritarian regime in the world but it is the wealthiest, strongest and technologically most advanced. This makes Xi Jinping the most dangerous opponent of open societies. That’s why it’s so important to distinguish Xi Jinping’s policies from the aspirations of the Chinese people. The social credit system, if it became operational, would give Xi total control over the people. Since Xi is the most dangerous enemy of the open society, we must pin our hopes on the Chinese people, and especially on the business community and a political elite willing to uphold the Confucian tradition.

This doesn’t mean that those of us who believe in the open society should remain passive. The reality is that we are in a Cold War that threatens to turn into a hot one. On the other hand, if Xi and Trump were no longer in power, an opportunity would present itself to develop greater cooperation between the two cyber-superpowers.

It is possible to dream of something similar to the United Nations Treaty that arose out of the Second World War. This would be the appropriate ending to the current cycle of conflict between the US and China. It would reestablish international cooperation and allow open societies to flourish. That sums up my message.

What gaming will look like in 10 years

What gaming will look like in a year or two, let alone 10, is a matter of some debate. Battle-royale games have reshaped multiplayer experiences; augmented reality marries the fantastic and real in unprecedented ways. Google is leading a charge away from traditional consoles by launching a cloud-gaming service, Stadia, later this year. Microsoft’s next version of the Xbox will presumably integrate cloud gaming as well to allow people to play Xbox games on multiple devices. Sony’s plans in this regard are still unclear—it’s one of the many things Cerny is keeping mum on, saying only that “we are cloud-gaming pioneers, and our vision should become clear as we head toward launch”—but it’s hard to think there won’t be more news coming on that front.

For now, there’s the living room. It’s where the PlayStation has sat through four generations—and will continue to sit at least one generation more.

https://www.wired.com/story/exclusive-sony-next-gen-console/

Apple will be around for a long time. But the next Apple just isn’t Apple.

Apple, the iPhone, and the Innovator’s Dilemma

David Paul Morris/Bloomberg/Getty Images

If you re-read the first few chapters of The Innovator’s Dilemma and you insert “Apple” every time Clayton Christensen mentions “a company,” a certain picture emerges: Apple is a company on the verge of being disrupted, and the next great idea in tech and consumer electronics will not materialize from within the walls of its Cupertino spaceship.

The Innovator’s Dilemma, of course, is about the trap that successful companies fall into time and time again. They’re well managed, they’re responsive to their customers, and they’re market leaders. And yet, despite doing everything right, they fail to see the next wave of innovation coming, they get disrupted, and they ultimately fail.

In the case of Apple, the company is trapped by its success, and that success is spelled “iPhone.”

Take, for example, Christensen’s description of the principles of good management that inevitably lead to the downfall of successful companies: “that you should always listen to and respond to the needs of your best customers, and that you should focus investments on those innovations that promise the highest returns.”

Molly Wood (@mollywood) is an Ideas contributor at WIRED and the host and senior editor of Marketplace Tech, a daily national radio broadcast covering the business of technology. She has covered the tech industry at CNET, The New York Times, and in various print, television, digital and audio formats for nearly 20 years. (Ouch.)

Then think about the iPhone, which, despite some consumer-unfriendly advances like the lost headphone jack and ever-changing charging ports, has also been adjusted and tweaked and frozen by what customers want: bigger screens, great cameras, ease of use, and a consistent interface. And the bulk of Apple’s investment since 2007, when the iPhone came out, has been about maintaining, developing, and selling this one device.

In the last quarter of 2018, the iPhone accounted for $51 billion of Apple’s $84 billion in revenue. Its success, the economic halo around it, and its seeming invincibility since its launch have propelled Apple to heights few companies have ever imagined. But the device will also be its undoing.

Here’s what happens when you have a product that successful: You get comfortable. More accurately, you get protective. You don’t want to try anything new. The new things you do try have to be justified in the context of that precious jewel—the “core product.”

So even something like Apple’s Services segment—the brightest non-iPhone spot in its earnings lately—mostly consists of services that benefit the iPhone. It’s Apple Music, iTunes, iCloud—and although Apple doesn’t break out its numbers, the best estimate is that a third or more of its Services revenue is driven by the 30 percent cut it takes from … yep, apps downloaded from the App Store.

The other bright spot in the company’s latest earnings report is its Wearables, Home, and Accessories category. Here again, Apple doesn’t break out the numbers, but the wearables part of that segment is where all the growth is, and that means Apple Watches. And you know what’s still tied nice and tight to the iPhone? Apple Watches.

Even Apple’s best-selling accessories are most likely AirPods, which had a meme-tastic holiday season and are, safe to say, used mostly in conjunction with iPhones. (I’d bet the rest of the accessories dollars are coming from dongles and hubs, since there’s nary a port to be found on any of its new MacBooks.) As for stand-alones, its smart speakers are reportedly great, but they’re not putting a dent in Amazon or Google, by latest count. Apple TV, sure. Fine. But Roku shouldn’t have been embedded in a TV before Apple was.

And none of these efforts count as a serious attempt at diversification.

You may be tempted to argue that Apple is, in fact, working on other projects. The Apple acquisition rumors never cease; nor do the confident statements that the company definitely, absolutely, certainly has a magical innovation in the works that will spring full grown like Athena from the forehead of Zeus any day now. I’m here to say, I don’t think there’s a nascent warrior goddess hiding in there.

Witness Apple’s tottering half-steps into new markets that are unrelated to the iPhone: It was early with a voice assistant but has stalled behind Amazon and even Google Assistant. It wasn’t until last year that the company hired a bona fide machine-learning expert in John Giannandrea, former head of search and AI at Google—and he didn’t get put on the executive team until December 2018. That’s late.

There’s its half-hearted dabble in self-driving technology that was going to be a car, then became software, then became 200 people laid off. Its quailing decade-long attempt to build a streaming service would be sort of comical if there weren’t clearly so much money being thrown around, and so tentatively at that. Rumors of its launch go back as far as 2015, although now it’s supposed to launch in April—this time they mean it.

But even if the streaming service actually arrives, can it really compete against YouTube, PlayStation, Sling, DirecTV, Hulu, and just plain old Netflix? Apple’s original programming is also apparently “not coming as soon as you think.” Analysts are, at this point, outright begging Apple to buy a studio or other original content provider, just to have something to show against Netflix and Amazon originals.

Of course, lots of companies innovate through acquisition, and everyone loves to speculate about what companies Apple might buy. Rumors have ranged from GoPro to BlackBerry to Tesla to the chipmaker ARM. Maybe Netflix. Maybe Tesla. Maybe Disney. Maybe Wired. (Apple News is a hugely successful product … mostly on iPhones, of course.) But at every turn, Apple has declined to move, other than its $3 billion Beats buy in 2014 (which it appears to be abandoning, or cannibalizing, these days).

Now, let me be clear, once again. None of this is to suggest that Apple is doing anything wrong. Indeed, according to Christensen, one of the hallmarks of the innovator’s dilemma is the company’s success, smooth operations, great products, and happy customers. That’s one of the things that makes it a dilemma: A company doesn’t realize anything’s wrong, because, well, nothing is. Smartphone sales may be slowing, but Apple is still a beloved brand, its products are excellent, its history and cachet are unmatched. But that doesn’t mean it has a plan to survive the ongoing decline in global smartphones sales.

The Innovator’s Dilemma does say an entrenched company can sometimes pull out of the quicksand by setting up a small, autonomous spinoff that has the power to move fast, pursue markets that are too small to move the needle for a company making $84 billion a quarter, and innovate before someone else gets there first.

Well, Apple has no autonomous innovation divisions that I know of, and the guys in charge are the same guys who have been in charge for decades: Tim Cook, Eddy Cue, Phil Schiller, Craig Federighi, Jony Ive—all have been associated with Apple since the late ’80s or ’90s. (I mean, has there ever really been a time without Jony Ive?)

You see what I’m saying here: brilliant team with a long record of execution and unparalleled success. Possibly not a lot of fresh ideas.

And then there’s the final option for innovation, one that Apple has availed itself of many times in the past. As Steve Jobs often said, quoting Picasso: “Good artists copy; great artists steal.” The iPod was born of existing MP3 players; the iPhone improved on clunky, ugly smartphones already on the market. The MacOS and the computer mouse were developed to maturity (yes, with permission) after being invented at Xerox PARC.

So maybe Apple will find the hottest thing in tech that’s still slightly unknown and come out with a better version. But is there such a thing as a way-sexier cloud computing business?

I guess it’s possible that the rumored virtual- and augmented-reality headset that Apple is supposed to release in 2020 will take the world by storm and popularize VR in a way that no one imagined, and like AirPods, will take a look that’s painfully dorky on the surface and turn it into a not-quite-ironic must-have statement of affluence and cool. It’s happened before. But this time, I think the company will get beaten to that punch—or whatever punch is next. Apple will be around for a long time. But the next Apple just isn’t Apple.

Source: https://www.wired.com/story/ideas-molly-wood-apple/

June 2018 Tech News & Trends to Watch

1. Companies Worldwide Strive for GDPR Compliance

By now, everyone with an email address has seen a slew of emails announcing privacy policy updates. You have Europe’s GDPR legislation to thank for your overcrowded inbox. GDPR creates rules around how much data companies are allowed to collect, how they’re able to use that data, and how clear they have to be with consumers about it all.

Companies around the world are scrambling to get their business and its practices into compliance – a significant task for many of them. While technically, the deadline to get everything in order passed on May 25, for many companies the process will continue well into June and possibly beyond. Some companies are even shutting down in Europe for good, or for as long as it takes them to get in compliance.

Even with the deadline behind us, the GDPR continues to be a top story for the tech world and may remain so for some time to come.

 

2. Amazon Provides Facial Recognition Tech to Law Enforcement

Amazon can’t seem to go a whole month without showing up in a tech news roundup. This month it’s for a controversial story: selling use of Rekognition, their facial recognition software, to law enforcement agencies on the cheap.

Civil rights groups have called for the company to stop allowing law enforcement access to the tech out of concerns that increased government surveillance can pose a threat to vulnerable communities in the country. In spite of the public criticism, Amazon hasn’t backed off on providing the tech to authorities, at least as of this time.

 

3. Apple Looks Into Self-Driving Employee Shuttles

Of the many problems facing our world, the frustrating work commute is one that many of the brightest minds in tech deal with just like the rest of us. Which makes it a problem the biggest tech companies have a strong incentive to try to solve.

Apple is one of many companies that’s invested in developing self-driving cars as a possible solution, but while that goal is still (probably) years away, they’ve narrowed their focus to teaming up with VW to create self-driving shuttles just for their employees.  Even that project is moving slower than the company had hoped, but they’re aiming to have some shuttles ready by the end of the year.

 

4. Court Weighs in on President’s Tendency to Block Critics on Twitter

Three years ago no one would have imagined that Twitter would be a president’s go-to source for making announcements, but today it’s used to that effect more frequently than official press conferences or briefings.

In a court battle that may sound surreal to many of us, a judge just found that the president can no longer legally block other users on Twitter.  The court asserted that blocking users on a public forum like Twitter amounts to a violation of their First Amendment rights. The judgment does still allow for the president and other public officials to mute users they don’t agree with, though.

 

5. YouTube Launches Music Streaming Service

YouTube joined the ranks of Spotify, Pandora, and Amazon this past month with their own streaming music service. Consumers can use a free version of the service that includes ads, or can pay $9.99 for the ad-free version.

youtube music service

With so many similar services already on the market, people weren’t exactly clamoring for another music streaming option. But since YouTube is likely to remain the reigning source for videos, it doesn’t necessarily need to unseat Spotify to still be okay. And with access to Google’s extensive user data, it may be able to provide more useful recommendations than its main competitors in the space, which is one way the service could differentiate itself.

 

6. Facebook Institutes Political Ad Rules

Facebook hasn’t yet left behind the controversies of the last election. The company is still working to proactively respond to criticism of its role in the spread of political propaganda many believe influenced election results. One of the solutions they’re trying is a new set of rules for any political ads run on the platform.

Any campaign that intends to run Facebook ads is now required to verify their identity with a card Facebook mails to their address that has a verification code. While Facebook has been promoting these new rules for a few weeks to politicians active on the platform, some felt blindsided when they realized, right before their primaries no less, that they could no longer place ads without waiting 12 to 15 days for a verification code to come in the mail. Politicians in this position blame the company for making a change that could affect their chances in the upcoming election.

Even in their efforts to avoid swaying elections, Facebook has found themselves criticized for doing just that. They’re probably feeling at this point like they just can’t win.

 

7. Another Big Month for Tech IPOs

This year has seen one tech IPO after another and this month is no different. Chinese smartphone company Xiaomi has a particularly large IPO in the works. The company seeks to join the Hong Kong stock exchange on June 7 with an initial public offering that experts anticipate could reach $10 billion.

The online lending platform Greensky started trading on the New York Stock Exchange on May 23 and sold 38 million shares in its first day, 4 million more than expected. This month continues 2018’s trend of tech companies going public, largely to great success.

 

8. StumbleUpon Shuts Down

In the internet’s ongoing evolution, there will always be tech companies that win and those that fall by the wayside. StumbleUpon, a content discovery platform that had its heyday in the early aughts, is officially shutting down on June 30.

Since its 2002 launch, the service has helped over 40 million users “stumble upon” 60 billion new websites and pieces of content. The company behind StumbleUpon plans to create a new platform that serves a similar purpose that may be more useful to former StumbleUpon users called Mix.

 

9. Uber and Lyft Invest in Driver Benefits

In spite of their ongoing success, the popular ridesharing platforms Uber and Lyft have faced their share of criticism since they came onto the scene. One of the common complaints critics have made is that the companies don’t provide proper benefits to their drivers. And in fact, the companies have fought to keep drivers classified legally as contractors so they’re off the hook for covering the cost of employee taxes and benefits.

Recently both companies have taken steps to make driving for them a little more attractive. Uber has begun offering Partner Protection to its drivers in Europe, which includes health insurance, sick pay, and parental leave ­ ­– so far nothing similar in the U.S. though. For its part, Lyft is investing $100 million in building driver support centers where their drivers can stop to get discounted car maintenance, tax help, and customer support help in person from Lyft staff. It’s not the same as getting full employee benefits (in the U.S. at least), but it’s something.

Source: https://www.hostgator.com/blog/june-tech-trends-to-watch/

What is GDPR – General Data Protection Regulation

Source Techcrunch.com

European Union lawmakers proposed a comprehensive update to the bloc’s data protection and privacy rules in 2012.

Their aim: To take account of seismic shifts in the handling of information wrought by the rise of the digital economy in the years since the prior regime was penned — all the way back in 1995 when Yahoo was the cutting edge of online cool and cookies were still just tasty biscuits.

Here’s the EU’s executive body, the Commission, summing up the goal:

The objective of this new set of rules is to give citizens back control over of their personal data, and to simplify the regulatory environment for business. The data protection reform is a key enabler of the Digital Single Market which the Commission has prioritised. The reform will allow European citizens and businesses to fully benefit from the digital economy.

For an even shorter the EC’s theory is that consumer trust is essential to fostering growth in the digital economy. And it thinks trust can be won by giving users of digital services more information and greater control over how their data is used. Which is — frankly speaking — a pretty refreshing idea when you consider the clandestine data brokering that pervades the tech industry. Mass surveillance isn’t just something governments do.

The General Data Protection Regulation (aka GDPR) was agreed after more than three years of negotiations between the EU’s various institutions.

It’s set to apply across the 28-Member State bloc as of May 25, 2018. That means EU countries are busy transposing it into national law via their own legislative updates (such as the UK’s new Data Protection Bill — yes, despite the fact the country is currently in the process of (br)exiting the EU, the government has nonetheless committed to implementing the regulation because it needs to keep EU-UK data flowing freely in the post-brexit future. Which gives an early indication of the pulling power of GDPR.

Meanwhile businesses operating in the EU are being bombarded with ads from a freshly energized cottage industry of ‘privacy consultants’ offering to help them get ready for the new regs — in exchange for a service fee. It’s definitely a good time to be a law firm specializing in data protection.

GDPR is a significant piece of legislation whose full impact will clearly take some time to shake out. In the meanwhile, here’s our guide to the major changes incoming and some potential impacts.

Data protection + teeth

A major point of note right off the bat is that GDPR does not merely apply to EU businesses; any entities processing the personal data of EU citizens need to comply. Facebook, for example — a US company that handles massive amounts of Europeans’ personal data — is going to have to rework multiple business processes to comply with the new rules. Indeed, it’s been working on this for a long time already.

Last year the company told us it had assembled “the largest cross functional team” in the history of its family of companies to support GDPR compliance — specifying this included “senior executives from all product teams, designers and user experience/testing executives, policy executives, legal executives and executives from each of the Facebook family of companies”.

“Dozens of people at Facebook Ireland are working full time on this effort,” it said, noting too that the data protection team at its European HQ (in Dublin, Ireland) would be growing by 250% in 2017. It also said it was in the process of hiring a “top quality data protection officer” — a position the company appears to still be taking applications for.

The new EU rules require organizations to appoint a data protection officer if they process sensitive data on a large scale (which Facebook very clearly does). Or are collecting info on many consumers — such as by performing online behavioral tracking. But, really, which online businesses aren’t doing that these days?

The extra-territorial scope of GDPR casts the European Union as a global pioneer in data protection — and some legal experts suggest the regulation will force privacy standards to rise outside the EU too.

Sure, some US companies might prefer to swallow the hassle and expense of fragmenting their data handling processes, and treating personal data obtained from different geographies differently, i.e. rather than streamlining everything under a GDPR compliant process. But doing so means managing multiple data regimes. And at very least runs the risk of bad PR if you’re outed as deliberately offering a lower privacy standard to your home users vs customers abroad.

Ultimately, it may be easier (and less risky) for businesses to treat GDPR as the new ‘gold standard’ for how they handle all personal data, regardless of where it comes from.

And while not every company harvests Facebook levels of personal data, almost every company harvests some personal data. So for those with customers in the EU GDPR cannot be ignored. At very least businesses will need to carry out a data audit to understand their risks and liabilities.

Privacy experts suggest that the really big change here is around enforcement. Because while the EU has had long established data protection standards and rules — and treats privacy as a fundamental right — its regulators have lacked the teeth to command compliance.

But now, under GDPR, financial penalties for data protection violations step up massively.

The maximum fine that organizations can be hit with for the most serious infringements of the regulation is 4% of their global annual turnover (or €20M, whichever is greater). Though data protection agencies will of course be able to impose smaller fines too. And, indeed, there’s a tiered system of fines — with a lower level of penalties of up to 2% of global turnover (or €10M).

This really is a massive change. Because while data protection agencies (DPAs) in different EU Member States can impose financial penalties for breaches of existing data laws these fines are relatively small — especially set against the revenues of the private sector entities that are getting sanctioned.

In the UK, for example, the Information Commissioner’s Office (ICO) can currently impose a maximum fine of just £500,000. Compare that to the annual revenue of tech giant Google (~$90BN) and you can see why a much larger stick is needed to police data processors.

It’s not necessarily the case that individual EU Member States are getting stronger privacy laws as a consequence of GDPR (in some instances countries have arguably had higher standards in their domestic law). But the beefing up of enforcement that’s baked into the new regime means there’s a better opportunity for DPAs to start to bark and bite like proper watchdogs.

GDPR inflating the financial risks around handling personal data should naturally drive up standards — because privacy laws are suddenly a whole lot more costly to ignore.

More types of personal data that are hot to handle

So what is personal data under GDPR? It’s any information relating to an identified or identifiable person (in regulatorspeak people are known as ‘data subjects’).

While ‘processing’ can mean any operation performed on personal data — from storing it to structuring it to feeding it to your AI models. (GDPR also includes some provisions specifically related to decisions generated as a result of automated data processing but more on that below).

A new provision concerns children’s personal data — with the regulation setting a 16-year-old age limit on kids’ ability to consent to their data being processed. However individual Member States can choose (and some have) to derogate from this by writing a lower age limit into their laws.

GDPR sets a hard cap at 13-years-old — making that the defacto standard for children to be able to sign up to digital services. So the impact on teens’ social media habits seems likely to be relatively limited.

The new rules generally expand the definition of personal data — so it can include information such as location data, online identifiers (such as IP addresses) and other metadata. So again, this means businesses really need to conduct an audit to identify all the types of personal data they hold. Ignorance is not compliance.

GDPR also encourages the use of pseudonymization — such as, for example, encrypting personal data and storing the encryption key separately and securely — as a pro-privacy, pro-security technique that can help minimize the risks of processing personal data. Although pseudonymized data is likely to still be considered personal data; certainly where a risk of reidentification remains. So it does not get a general pass from requirements under the regulation.

Data has to be rendered truly anonymous to be outside the scope of the regulation. (And given how often ‘anonymized’ data-sets have been shown to be re-identifiable, relying on any anonymizing process to be robust enough to have zero risk of re-identification seems, well, risky.)

To be clear, given GDPR’s running emphasis on data protection via data security it is implicitly encouraging the use of encryption above and beyond a risk reduction technique — i.e. as a way for data controllers to fulfill its wider requirements to use “appropriate technical and organisational measures” vs the risk of the personal data they are processing.

The incoming data protection rules apply to both data controllers (i.e. entities that determine the purpose and means of processing personal data) and data processors (entities that are responsible for processing data on behalf of a data controller — aka subcontractors).

Indeed, data processors have some direct compliance obligations under GDPR, and can also be held equally responsible for data violations, with individuals able to bring compensation claims directly against them, and DPAs able to hand them fines or other sanctions.

So the intent for the regulation is there be no diminishing in responsibility down the chain of data handling subcontractors. GDPR aims to have every link in the processing chain be a robust one.

For companies that rely on a lot of subcontractors to handle data operations on their behalf there’s clearly a lot of risk assessment work to be done.

As noted above, there is a degree of leeway for EU Member States in how they implement some parts of the regulation (such as with the age of data consent for kids).

Consumer protection groups are calling for the UK government to include an optional GDPR provision on collective data redress to its DP bill, for example — a call the government has so far rebuffed.

But the wider aim is for the regulation to harmonize as much as possible data protection rules across all Member States to reduce the regulatory burden on digital businesses trading around the bloc.

On data redress, European privacy campaigner Max Schrems — most famous for his legal challenge to US government mass surveillance practices that resulted in a 15-year-old data transfer arrangement between the EU and US being struck down in 2015 — is currently running a crowdfunding campaign to set up a not-for-profit privacy enforcement organization to take advantage of the new rules and pursue strategic litigation on commercial privacy issues.

Schrems argues it’s simply not viable for individuals to take big tech giants to court to try to enforce their privacy rights, so thinks there’s a gap in the regulatory landscape for an expert organization to work on EU citizen’s behalf. Not just pursuing strategic litigation in the public interest but also promoting industry best practice.

The proposed data redress body — called noyb; short for: ‘none of your business’ — is being made possible because GDPR allows for collective enforcement of individuals’ data rights. And that provision could be crucial in spinning up a centre of enforcement gravity around the law. Because despite the position and role of DPAs being strengthened by GDPR, these bodies will still inevitably have limited resources vs the scope of the oversight task at hand.

Some may also lack the appetite to take on a fully fanged watchdog role. So campaigning consumer and privacy groups could certainly help pick up any slack.

Privacy by design and privacy by default

Another major change incoming via GDPR is ‘privacy by design’ no longer being just a nice idea; privacy by design and privacy by default become firm legal requirements.

This means there’s a requirement on data controllers to minimize processing of personal data — limiting activity to only what’s necessary for a specific purpose, carrying out privacy impact assessments and maintaining up-to-date records to prove out their compliance.

Consent requirements for processing personal data are also considerably strengthened under GDPR — meaning lengthy, inscrutable, pre-ticked T&Cs are likely to be unworkable. (And we’ve sure seen a whole lot of those hellish things in tech.) The core idea is that consent should be an ongoing, actively managed process; not a one-off rights grab.

As the UK’s ICO tells it, consent under GDPR for processing personal data means offering individuals “genuine choice and control” (for sensitive personal data the law requires a higher standard still — of explicit consent).

There are other legal bases for processing personal data under GDPR — such as contractual necessity; or compliance with a legal obligation under EU or Member State law; or for tasks carried out in the public interest — so it is not necessary to obtain consent in order to process someone’s personal data. But there must always be an appropriate legal basis for each processing.

Transparency is another major obligation under GDPR, which expands the notion that personal data must be lawfully and fairly processed to include a third principle of accountability. Hence the emphasis on data controllers needing to clearly communicate with data subjects — such as by informing them of the specific purpose of the data processing.

The obligation on data handlers to maintain scrupulous records of what information they hold, what they are doing with it, and how they are legally processing it, is also about being able to demonstrate compliance with GDPR’s data processing principles.

But — on the plus side for data controllers — GDPR removes the requirement to submit notifications to local DPAs about data processing activities. Instead, organizations must maintain detailed internal records — which a supervisory authority can always ask to see.

It’s also worth noting that companies processing data across borders in the EU may face scrutiny from DPAs in different Member States if they have users there (and are processing their personal data).

Although the GDPR sets out a so-called ‘one-stop-shop’ principle — that there should be a “lead” DPA to co-ordinate supervision between any “concerned” DPAs — this does not mean that, once it applies, a cross-EU-border operator like Facebook is only going to be answerable to the concerns of the Irish DPA.

Indeed, Facebook’s tactic of only claiming to be under the jurisdiction of a single EU DPA looks to be on borrowed time. And the one-stop-shop provision in the GDPR seems more about creating a co-operation mechanism to allow multiple DPAs to work together in instances where they have joint concerns, rather than offering a way for multinationals to go ‘forum shopping’ — which the regulation does not permit (per WP29 guidance).

Another change: Privacy policies that contain vague phrases like ‘We may use your personal data to develop new services’ or ‘We may use your personal data for research purposes’ will not pass muster under the new regime. So a wholesale rewriting of vague and/or confusingly worded T&Cs is something Europeans can look forward to this year.

Add to that, any changes to privacy policies must be clearly communicated to the user on an ongoing basis. Which means no more stale references in the privacy statement telling users to ‘regularly check for changes or updates’ — that just won’t be workable.

The onus is firmly on the data controller to keep the data subject fully informed of what is being done with their information. (Which almost implies that good data protection practice could end up tasting a bit like spam, from a user PoV.)

The overall intent behind GDPR is to inculcate an industry-wide shift in perspective regarding who ‘owns’ user data — disabusing companies of the notion that other people’s personal information belongs to them just because it happens to be sitting on their servers.

“Organizations should acknowledge they don’t exist to process personal data but they process personal data to do business,” is how analyst Gartner research director Bart Willemsen sums this up. “Where there is a reason to process the data, there is no problem. Where the reason ends, the processing should, too.”

The data protection officer (DPO) role that GDPR brings in as a requirement for many data handlers is intended to help them ensure compliance.

This officer, who must report to the highest level of management, is intended to operate independently within the organization, with warnings to avoid an internal appointment that could generate a conflict of interests.

Which types of organizations face the greatest liability risks under GDPR? “Those who deliberately seem to think privacy protection rights is inferior to business interest,” says Willemsen, adding: “A recent example would be Uber, regulated by the FTC and sanctioned to undergo 20 years of auditing. That may hurt perhaps similar, or even more, than a one-time financial sanction.”

“Eventually, the GDPR is like a speed limit: There not to make money off of those who speed, but to prevent people from speeding excessively as that prevents (privacy) accidents from happening,” he adds.

Another right to be forgotten

Under GDPR, people who have consented to their personal data being processed also have a suite of associated rights — including the right to access data held about them (a copy of the data must be provided to them free of charge, typically within a month of a request); the right to request rectification of incomplete or inaccurate personal data; the right to have their data deleted (another so-called ‘right to be forgotten’ — with some exemptions, such as for exercising freedom of expression and freedom of information); the right to restrict processing; the right to data portability (where relevant, a data subject’s personal data must be provided free of charge and in a structured, commonly used and machine readable form).

All these rights make it essential for organizations that process personal data to have systems in place which enable them to identify, access, edit and delete individual user data — and be able to perform these operations quickly, with a general 30 day time-limit for responding to individual rights requests.

GDPR also gives people who have consented to their data being processed the right to withdraw consent at any time. Let that one sink in.

Data controllers are also required to inform users about this right — and offer easy ways for them to withdraw consent. So no, you can’t bury a ‘revoke consent’ option in tiny lettering, five sub-menus deep. Nor can WhatsApp offer any more time-limit opt-outs for sharing user data with its parent multinational, Facebook. Users will have the right to change their mind whenever they like.

The EU lawmakers’ hope is that this suite of rights for consenting consumers will encourage respectful use of their data — given that, well, if you annoy consumers they can just tell you to sling yer hook and ask for a copy of their data to plug into your rival service to boot. So we’re back to that fostering trust idea.

Add in the ability for third party organizations to use GDPR’s provision for collective enforcement of individual data rights and there’s potential for bad actors and bad practice to become the target for some creative PR stunts that harness the power of collective action — like, say, a sudden flood of requests for a company to delete user data.

Data rights and privacy issues are certainly going to be in the news a whole lot more.

Getting serious about data breaches

But wait, there’s more! Another major change under GDPR relates to security incidents — aka data breaches (something else we’ve seen an awful, awful lot of in recent years) — with the regulation doing what the US still hasn’t been able to: Bringing in a universal standard for data breach disclosures.

GDPR requires that data controllers report any security incidents where personal data has been lost, stolen or otherwise accessed by unauthorized third parties to their DPA within 72 hours of them becoming aware of it. Yes, 72 hours. Not the best part of a year, like er Uber.

If a data breach is likely to result in a “high risk of adversely affecting individuals’ rights and freedoms” the regulation also implies you should ‘fess up even sooner than that — without “undue delay”.

Only in instances where a data controller assesses that a breach is unlikely to result in a risk to the rights and freedoms of “natural persons” are they exempt from the breach disclosure requirement (though they still need to document the incident internally, and record their reason for not informing a DPA in a document that DPAs can always ask to see).

“You should ensure you have robust breach detection, investigation and internal reporting procedures in place,” is the ICO’s guidance on this. “This will facilitate decision-making about whether or not you need to notify the relevant supervisory authority and the affected individuals.”

The new rules generally put strong emphasis on data security and on the need for data controllers to ensure that personal data is only processed in a manner that ensures it is safeguarded.

Here again, GDPR’s requirements are backed up by the risk of supersized fines. So suddenly sloppy security could cost your business big — not only in reputation terms, as now, but on the bottom line too. So it really must be a C-suite concern going forward.

Nor is subcontracting a way to shirk your data security obligations. Quite the opposite. Having a written contract in place between a data controller and a data processor was a requirement before GDPR but contract requirements are wider now and there are some specific terms that must be included in the contract, as a minimum.

Breach reporting requirements must also be set out in the contract between processor and controller. If a data controller is using a data processor and it’s the processor that suffers a breach, they’re required to inform the controller as soon as they become aware. The controller then has the same disclosure obligations as per usual.

Essentially, data controllers remain liable for their own compliance with GDPR. And the ICO warns they must only appoint processors who can provide “sufficient guarantees” that the regulatory requirements will be met and the rights of data subjects protected.

tl;dr, be careful who and how you subcontract.

Right to human review for some AI decisions

Article 22 of GDPR places certain restrictions on entirely automated decisions based on profiling individuals — but only in instances where these human-less acts have a legal or similarly significant effect on the people involved.

There are also some exemptions to the restrictions — where automated processing is necessary for entering into (or performance of) a contract between an organization and the individual; or where it’s authorized by law (e.g. for the purposes of detecting fraud or tax evasion); or where an individual has explicitly consented to the processing.

In its guidance, the ICO specifies that the restriction only applies where the decision has a “serious negative impact on an individual”.

Suggested examples of the types of AI-only decisions that will face restrictions are automatic refusal of an online credit application or an e-recruiting practices without human intervention.

Having a provision on automated decisions is not a new right, having been brought over from the 1995 data protection directive. But it has attracted fresh attention — given the rampant rise of machine learning technology — as a potential route for GDPR to place a check on the power of AI blackboxes to determine the trajectory of humankind.

The real-world impact will probably be rather more prosaic, though. And experts suggest it does not seem likely that the regulation, as drafted, equates to a right for people to be given detailed explanations of how algorithms work.

Though as AI proliferates and touches more and more decisions, and as its impacts on people and society become ever more evident, pressure may well grow for proper regulatory oversight of algorithmic blackboxes.

In the meanwhile, what GDPR does in instances where restrictions apply to automated decisions is require data controllers to provide some information to individuals about the logic of an automated decision.

They are also obliged to take steps to prevent errors, bias and discrimination. So there’s a whiff of algorithmic accountability. Though it may well take court and regulatory judgements to determine how stiff those steps need to be in practice.

Individuals do also have a right to challenge and request a (human) review of an automated decision in the restricted class.

Here again the intention is to help people understand how their data is being used. And to offer a degree of protection (in the form of a manual review) if a person feels unfairly and harmfully judged by an AI process.

The regulation also places some restrictions on the practice of using data to profile individuals if the data itself is sensitive data — e.g. health data, political belief, religious affiliation etc — requiring explicit consent for doing so. Or else that the processing is necessary for substantial public interest reasons (and lies within EU or Member State law).

While profiling based on other types of personal data does not require obtaining consent from the individuals concerned, it still needs a legal basis and there is still a transparency requirement — which means service providers will need to inform users they are being profiled, and explain what it means for them.

And people also always have the right to object to profiling activity based on their personal data.

 

Source: https://techcrunch.com/2018/01/20/wtf-is-gdpr/

Harvards View on Types of Project Managers

Read harvard business review here: https://hbr.org/2017/07/the-4-types-of-project-manager

Few issues garner more attention among top executives than how best to grow their organizations. However, few executives work systematically with the types of employees they need to realize various growth opportunities. Your organization’s growth opportunities fall into four different categories, and in order to develop your business in a commercially sustainable manner, you need four specific types of project manager to pursue them. These types emerged from our ongoing work of understanding how different business development projects can drive strategic renewal in organizations, and the matrix below has helped in capturing potential misalignments between employees and projects.

The employee types and the growth opportunities that they are best at pursuing can be positioned along two dimensions: (1) Is the growth opportunity in line with our existing strategy? (2) Can a reliable business case be made? These two questions create a matrix that distinguishes the four different kinds of project leaders, each of which is optimally suited for a different type of project.

Will every organization need all four types of employees to sustainably develop and grow their organizations? We argue that even the most stable and conservative industries may be threatened by disruption — and the most dynamic and hypercompetitive industries also entail incremental growth opportunities that can be quantified and realistically assessed. Consequently, there is often a job for all four types of employees in most organizations, although the optimal dose of each can differ. At the very least, executives need to be aware of the variety of growth opportunities that they may be losing out on by leaning heavily on a single type of project manager.

The Four Types

The four types pursue different growth opportunities and follow different communicative logics to gain support within the organization (see the table below). In other words, you need them all because they see and support different types of growth opportunities. In that respect, they complement each other. This does not necessarily mean that you need an equal number of each, as most organizations must predominantly rely on executors to ensure the alignment and feasibility needed to maintain profits in the short term. However, you will need a few prophetsgamblers, and experts to be able to identify and pursue growth opportunities at the periphery that can help you renew your organization beyond the chosen path. In the following, we further explain the characteristics of each of the different types.

Prophet. This type of project manager actively pursues business opportunities that lie outside the existing strategic boundaries in an area where it is extremely difficult to obtain trustworthy data concerning the likelihood of success. Hence, the prophet seeks to gain organizational followers for a grand vision of a growth opportunity that is strategically different from the status quo — and without trustworthy quantitative evidence, consequently relying on organizational members making a leap of faith in support of the vision. Obviously, running such projects is risky, as it is likely that the growth opportunities will not materialize, and therefore that the employee may be a “false prophet.” Be that as it may, a prophet is needed to challenge the existing strategy and to pursue overlooked growth opportunities.

A constructive use of this employee type is found at Google, which has a unit called X (formerly Google X), which is a self-proclaimed moonshot factory. Employees in this unit seek to solve big problems using breakthrough technologies and radical solutions. Hence, the projects in X tend to be outside Google’s current domain and strategic focus. In such projects, it is typically impossible to realistically assess the likelihood of success before they are tried out.

Gambler. This type of project manager actively pursues business opportunities that lie within the existing strategic boundaries but have no good business case attached, as trustworthy data concerning the likelihood of success is lacking. Hence, the gambler seeks to gain organizational followers for a big bet on a growth opportunity that is consistent with the current strategy but without trustworthy quantitative evidence. In other words, gamblers play by the rules of the game as they pursue growth opportunities within the existing strategy, but they cannot predict the likelihood of success. Consequently, the gambler seeks to engage other organizational members who also like bets. This can obviously be viewed as an uncertain path, as there is some likelihood that the growth opportunities are not feasible and that they may therefore result in significant losses. However, gamblers are necessary, as they can update the existing strategy by pursuing analytically overlooked growth opportunities.

This type of project champion is documented in a study by Paddy Miller and Thomas Wedell-Wedellsborg, which shows that MTV’s first digitally integrated and interactive program, Top Selection, was initially tried under the radar before the project’s backers had sufficient proof of concept to get managerial approval to continue. This project was driven by gamblers, as they stayed inside the existing strategic boundaries but were unable to document the likelihood of success before the idea had been tested.

Expert. This type of project manager actively pursues business opportunities that lie outside the existing strategic boundaries but for which trustworthy data builds a solid business case. Hence, experts wish to gain organizational followers for a change in action in favor of a growth opportunity that is inconsistent with the current strategy but is supported by solid, trustworthy quantitative evidence. Consequently, experts rely on organizational members actually listening to their advice. Although the growth opportunities are well supported and should therefore be feasible, the main challenge is to make organizational members aware of the need for strategic change and of the urgent need to act in this regard. The expert is needed to challenge the existing strategy by pursuing well-supported growth opportunities that lie outside the organization’s current strategy.

Experts in action are seen in the well-known story of Intel’s transition from memory chips to microprocessors, where key employees within the organization tried to persuade Intel’s management of the value of the opportunity for some time. It took the executive team several years of internal soul-searching before they were ready to make the organizational transition. In this case, the growth opportunity was outside the existing strategy, but it was possible to document the commercial potential and the likelihood of success with some certainty.

Executor. This project manager actively pursues business opportunities that lie within the existing strategic boundaries and have great cases. The executor gains organizational followers for a sure-thing growth opportunity that is consistent with the current strategy and is backed by trustworthy quantitative evidence. In other words, there is no risk, no uncertainty, and no challenge — just a need for execution. Consequently, executors rely on organizational members to follow their rigorous analyses of a strategically embraced project. This can be viewed as the most certain path to success, as the growth opportunity is well documented and aligned with the existing strategy. However, the executor can only point to a limited number of growth opportunities that are low-hanging fruit — the executor cannot provide insights into the more radical and unknown business opportunities. Many who bear the formal title of business developer systematically analyze, prepare, and support growth opportunities that lie within the strategic boundaries and for which it is possible to realistically assess the likelihood of success.

For instance, DuPont has a systematic approach for assessing and implementing growth opportunities. It entails a phased and systematic handling of new opportunities within a disciplined framework built on best practices, providing standardized guidance throughout the process from initial concept to subsequent commercialization. A comprehensive business case is essential to initiate the process — and as the approach involves key work streams and “blocks of work” that the core team must plan and execute in an effective manner, it is particularly suitable for executors.

How Do They Interact?

The various types of project managers may struggle in their interactions with each other. For instance, a prophet may see an executor as overly bureaucratic and rigid, while an executor may view a prophet as unrealistic and disorganized. Consequently, conflict tends to loom among the different types.

What typically happens is that the logic of one of the types becomes dominant throughout the organization. The fact that a single logic pervades the organization at the expense of the others may mean that key employees of a different type leave the organization and take their ideas with them. Moreover, relying on a single type of logic may lead to organizational inertia, which is dangerous in dynamic and evolving markets. You need to ensure enough room for all of the logics within the organization, ideally by introducing boundary-spanning individuals who can navigate among these logics. In this regard, it is beneficial if top management adopts a “bridging” role to allow for coexistence and diversity.

As an executive, you can similarly seek to stimulate a fruitful understanding and interaction among the different types of employees. For instance, having identified the different types within your organization, you can set up a workshop where one type meets and discusses with their alter ego (that is, executors talk to prophets, and gamblers talk to experts). This interaction can help clarify differences in opinions, routines and values — which may help create a greater mutual understanding and respect among the different employee types.

Do Executives Contribute to the Problem?

Executives partly contribute to unsuccessful projects and unrealized growth opportunities when they don’t think through who should be assigned to which projects. Prophets, gamblers, experts, and executors each have their own strengths and weaknesses that are optimally suited to fit certain project types. Therefore, no type is inherently better or rarer than the others.

Executives contribute to organizational failure when they misalign projects and project managers, but this fact is often hidden in the ruins of a failed project. There may be a tendency to see prophets and gamblers featuring on prominent magazine covers or taking newspaper headlines if they succeed with their high-profile projects. For this reason, executives tend to assume that prophets and gamblers are the best. In such cases, executives may be likely to promote good executors to run a prophet-type project, as senior management may think that the executor is finally ready for this big opportunity (with potentially disastrous results). Or executives may assume that they should tap prophets to run a project that really needs a great executor — which may lead to managerial befuddlement when the prophet doesn’t succeed. Instead of assuming that certain types are better than others, executives need to be aware of, value, and give appropriate room to all four types — and match them with the right projects.

The bottom line is that the diversity of styles offers a competitive advantage in terms of business development, and all four types are necessary pieces of your organizational constellation, even though the optimal dose of each may differ. As an executive, it is crucial that you:

  • Make sure you have each type within your organization
  • Make room for each type to work in their own manner
  • Make sense of their respective ideas, by following their respective logics
  • Make time for matching projects and project managers correctly

Meeting the various types where they are, and paying attention to their diverse ways of thinking, will help you obtain the needed diversity among your employees to develop your business. Moreover, this resonates with comprehensive findings that emphasize that the hallmark of great managers is that they discover and capitalize on the unique strengths of individual employees.

Growth and business development are top priorities in most C-suites across the globe, but too few executives focus on maintaining a wide range of people to ensure the identification of novel opportunities. Therefore, executives who want to develop their businesses need to first develop the right amount of staff diversity to drive a diverse portfolio of growth opportunities. Only when diverse people are on board can an organization drive commercially sustainable growth.


Carsten Lund Pedersen is Postdoc at the Department of Strategic Management and Globalization at Copenhagen Business School, where he researches in project-based strategy, employee autonomy and matching employee types with business development projects.


Thomas Ritter is a Professor of Market Strategy and Business Development at the Department of Strategic Management and Globalization at Copenhagen Business School, where he researches business model innovation, market strategies, and market management.

 

 

 

 

 

Take Down Request by the Spiegel Germany Online

Dear Spiegel Online (www.spiegel.de)

Never before in the existence of this personal blog (since 2011 – the day Steve Jobs died) have we received an article take down request where a correctly quoted article that we posted was requested to be taken down AND a website wanted money for the max. 1-2 hours that we had the article online.

Our vision: We create Innovation, enable exchange and try to give the best ideas to the world by always correctly quoting them.

By following take down requests immediately (yesterday it took us 10 minutes between their email at 14.47 and us having it taken down fully at 14.57) we comply with the internet rule-set of respecting other wishes fully. As a consequence we have never encountered any troubles with anyone and we would like to keep that this way.

Since June 19th 2017. Then it happenend: German Online Newspaper The Spiegel, head of law department Jan Siegel, requested the take down of the cooperational column written by internet activist Sascha Lobo that we thought would fit perfectly to the innovational approach on our website. We are not sure if we can post the link to the article but as a reference here it goes:

http://www.spiegel.de/netzwelt/netzpolitik/homepod-alexa-und-co-bevormundung-durch-kuenstliche-intelligenz-kolumne-a-1151017.html

We are deeply sorry that we cannot feature Sascha Lobo anymore, although he states on his website that his texts can be used under the Creative Commons Licence when correctly quoted by naming him as author and with the URL provided and most importantly unchanged. That’s what we did and now “The Spiegel” tries to money punish us with this?

So the authors rights are diminished by the newspapers rights?
Does anybody understand German author rights?
The author explicitly states on his website  http://saschalobo.com/impressum/ „Die Texte (mit Ausnahme der Kommentare durch Dritte) stehen sämtlich unter der Creative Commons-Lizenz (CC-BY-NC-SA 2.0 DE).“
In our understanding this means that you can use the text under the Creative Commons Licence for free when being private like here at dieidee.eu. So that the newspaper later cannot deny this and cannot punish you with money requests for literally a handful article impressions?

We hope to be able to resolve this matter in a friendly and respectful way with the Spiegel as we state here clearly no harm done, no harm will be done in the future, and please state clearly on your website which author (or internet activist as with Sascha Lobo) allows the usage of his texts on any internet website.

Your thankfully
dieidee.eu