Archiv der Kategorie: Big Data

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

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.

Steve Rymell Head of Technology, Airbus CyberSecurity answers What Should Frighten us about AI-Based Malware?

Of all the cybersecurity industry’s problems, one of the most striking is the way attackers are often able to stay one step ahead of defenders without working terribly hard. It’s an issue whose root causes are mostly technical: the prime example are software vulnerabilities which cyber-criminals have a habit of finding out about before vendors and their customers, leading to the almost undefendable zero-day phenomenon which has propelled many famous cyber-attacks.

A second is that organizations struggling with the complexity of unfamiliar and new technologies make mistakes, inadvertently leaving vulnerable ports and services exposed. Starkest of all, perhaps, is the way techniques, tools, and infrastructure set up to help organizations defend themselves (Shodan, for example but also numerous pen-test tools) are now just as likely to be turned against businesses by attackers who tear into networks with the aggression of red teams gone rogue.

Add to this the polymorphic nature of modern malware, and attackers can appear so conceptually unstoppable that it’s no wonder security vendors increasingly emphasize the need not to block attacks but instead respond to them as quickly as possible.

The AI fightback
Some years back, a list of mostly US-based start-ups started a bit of a counter-attack against the doom and gloom with a brave new idea – AI machine learning (ML) security powered by algorithms. In an age of big data, this makes complete sense and the idea has since been taken up by all manner of systems used to for anti-spam, malware detection, threat analysis and intelligence, and Security Operations Centre (SoC) automation where it has been proposed to help patch skills shortages.

I’d rate these as useful advances, but there’s no getting away from the controversial nature of the theory, which has been branded by some as the ultimate example of technology as a ‘black box’ nobody really understands. How do we know that machine learning is able to detect new and unknown types of attack that conventional systems fail to spot? In some cases, it could be because the product brochure says so.

Then the even bigger gotcha hits you – what’s stopping attackers from outfoxing defensive ML with even better ML of their own? If this were possible, even some of the time, the industry would find itself back at square one.

This is pure speculation, of course, because to date nobody has detected AI being used in a cyber-attack, which is why our understanding of how it might work remains largely based around academic research such as IBM’s proof-of-concept DeepLocker malware project.

What might malicious ML look like?
It would be unwise to ignore the potential for trouble. One of the biggest hurdles faced by attackers is quickly understanding what works, for example when sending spam, phishing and, increasingly, political disinformation.

It’s not hard to imagine that big data techniques allied to ML could hugely improve the efficiency of these threats by analyzing how targets react to and share them in real time. This implies the possibility that such campaigns might one day evolve in a matter of hours or minutes; a timescale defender would struggle to counter using today’s technologies.

A second scenario is one that defenders would even see: that cyber-criminals might simulate the defenses of a target using their own ML to gauge the success of different attacks (a technique already routinely used to evade anti-virus). Once again, this exploits the advantage that attackers always have sight of the target, while defenders must rely on good guesses.

Or perhaps ML could simply be used to crank out vast quantities of new and unique malware than is possible today. Whichever of these approaches is taken – and this is only a sample of the possibilities – it jumps out at you how awkward it would be to defend against even relatively simple ML-based attacks. About the only consolation is that if ML-based AI really is a black box that nobody understands then, logically, the attackers won’t understand it either and will waste time experimenting.

Unintended consequences
If we should fear anything it’s precisely this black box effect. There are two parts to this, the biggest of which is the potential for ML-based malware to cause something unintended to happen, especially when targeting critical infrastructure.

This phenomenon has already come to pass with non-AI malware – Stuxnet in 2010 and NotPetya in 2017 are the obvious examples – both of which infected thousands of organizations not on their original target list after unexpectedly ‘escaping’ into the wild.

When it comes to powerful malware exploiting multiple zero days there’s no such thing as a reliably contained attack. Once released, this kind of malware remains pathogenically dangerous until every system it can infect is patched or taken offline, which might be years or decades down the line.

Another anxiety is that because the expertise to understand ML is still thin on the ground, there’s a danger that engineers could come to rely on it without fully understanding its limitations, both for defense and by over-estimating its usefulness in attack. The mistake, then, might be that too many over-invest in it based on marketing promises that end up consuming resources better deployed elsewhere.  Once a more realistic assessment takes hold, ML could end up as just another tool that is good at solving certain very specific problems.

Conclusion
My contradictory-sounding conclusion is that perhaps ML and AI makes no fundamental difference at all. It’s just another stop on a journey computer security has been making since the beginning of digital time. The problem is overcoming our preconceptions about what it is and what it means. Chiefly, we must overcome the tendency to think of ML and AI as mysteriously ‘other’ because we don’t understand it and therefore find it difficult to process the concept of machines making complex decisions.

It’s not as if attackers aren’t breaching networks already with today’s pre-ML technology or that well-prepared defenders aren’t regularly stopping them using the same technology. What AI reminds us is that the real difference is how organizations are defended, not whether they or their attackers use ML and AI or not. That has always been what separates survivors from victims. Cybersecurity remains a working demonstration of how the devil takes the hindmost.

Source: https://www.infosecurity-magazine.com/opinions/frighten-ai-malware-1/

45 Techniques Used by Data Scientists

These techniques cover most of what data scientists and related practitioners are using in their daily activities, whether they use solutions offered by a vendor, or whether they design proprietary tools. When you click on any of the 45 links below, you will find a selection of articles related to the entry in question. Most of these articles are hard to find with a Google search, so in some ways this gives you access to the hidden literature on data science, machine learning, and statistical science. Many of these articles are fundamental to understanding the technique in question, and come with further references and source code.

Starred techniques (marked with a *) belong to what I call deep data science, a branch of data science that has little if any overlap with closely related fields such as machine learning, computer science, operations research, mathematics, or statistics. Even classical machine learning and statistical techniques such as clustering, density estimation,  or tests of hypotheses, have model-free, data-driven, robust versions designed for automated processing (as in machine-to-machine communications), and thus also belong to deep data science. However, these techniques are not starred here, as the standard versions of these techniques are more well known (and unfortunately more used) than the deep data science equivalent.

To learn more about deep data science,  click here. Note that unlike deep learning, deep data science is not the intersection of data science and artificial intelligence; however, the analogy between deep data science and deep learning is not completely meaningless, in the sense that both deal with automation.

Also, to discover in which contexts and applications the 40 techniques below are used, I invite you to read the following articles:

Finally, when using a technique, you need to test its performance. Read this article about 11 Important Model Evaluation Techniques Everyone Should Know.

The 40 data science techniques

  1. Linear Regression
  2. Logistic Regression
  3. Jackknife Regression *
  4. Density Estimation
  5. Confidence Interval
  6. Test of Hypotheses
  7. Pattern Recognition
  8. Clustering – (aka Unsupervised Learning)
  9. Supervised Learning
  10. Time Series
  11. Decision Trees
  12. Random Numbers
  13. Monte-Carlo Simulation
  14. Bayesian Statistics
  15. Naive Bayes
  16. Principal Component Analysis – (PCA)
  17. Ensembles
  18. Neural Networks
  19. Support Vector Machine – (SVM)
  20. Nearest Neighbors – (k-NN)
  21. Feature Selection – (aka Variable Reduction)
  22. Indexation / Cataloguing *
  23. (Geo-) Spatial Modeling
  24. Recommendation Engine *
  25. Search Engine *
  26. Attribution Modeling *
  27. Collaborative Filtering *
  28. Rule System
  29. Linkage Analysis
  30. Association Rules
  31. Scoring Engine
  32. Segmentation
  33. Predictive Modeling
  34. Graphs
  35. Deep Learning
  36. Game Theory
  37. Imputation
  38. Survival Analysis
  39. Arbitrage
  40. Lift Modeling
  41. Yield Optimization
  42. Cross-Validation
  43. Model Fitting
  44. Relevancy Algorithm *
  45. Experimental Design

Source: https://www.datasciencecentral.com/profiles/blogs/40-techniques-used-by-data-scientists

Alexa do you work for the NSA ;-)

Tens of millions of people use smart speakers and their voice software to play games, find music or trawl for trivia. Millions more are reluctant to invite the devices and their powerful microphones into their homes out of concern that someone might be listening.

Sometimes, someone is.

Amazon.com Inc. employs thousands of people around the world to help improve the Alexa digital assistant powering its line of Echo speakers. The team listens to voice recordings captured in Echo owners’ homes and offices. The recordings are transcribed, annotated and then fed back into the software as part of an effort to eliminate gaps in Alexa’s understanding of human speech and help it better respond to commands.

The Alexa voice review process, described by seven people who have worked on the program, highlights the often-overlooked human role in training software algorithms. In marketing materials Amazon says Alexa “lives in the cloud and is always getting smarter.” But like many software tools built to learn from experience, humans are doing some of the teaching.

The team comprises a mix of contractors and full-time Amazon employees who work in outposts from Boston to Costa Rica, India and Romania, according to the people, who signed nondisclosure agreements barring them from speaking publicly about the program. They work nine hours a day, with each reviewer parsing as many as 1,000 audio clips per shift, according to two workers based at Amazon’s Bucharest office, which takes up the top three floors of the Globalworth building in the Romanian capital’s up-and-coming Pipera district. The modern facility stands out amid the crumbling infrastructure and bears no exterior sign advertising Amazon’s presence.

The work is mostly mundane. One worker in Boston said he mined accumulated voice data for specific utterances such as “Taylor Swift” and annotated them to indicate the searcher meant the musical artist. Occasionally the listeners pick up things Echo owners likely would rather stay private: a woman singing badly off key in the shower, say, or a child screaming for help. The teams use internal chat rooms to share files when they need help parsing a muddled word—or come across an amusing recording.

 Amazon in Bucharest
Amazon has offices in this Bucharest building.
Photographer: Irina Vilcu/Bloomberg

Sometimes they hear recordings they find upsetting, or possibly criminal. Two of the workers said they picked up what they believe was a sexual assault. When something like that happens, they may share the experience in the internal chat room as a way of relieving stress. Amazon says it has procedures in place for workers to follow when they hear something distressing, but two Romania-based employees said that, after requesting guidance for such cases, they were told it wasn’t Amazon’s job to interfere.

“We take the security and privacy of our customers’ personal information seriously,” an Amazon spokesman said in an emailed statement. “We only annotate an extremely small sample of Alexa voice recordings in order [to] improve the customer experience. For example, this information helps us train our speech recognition and natural language understanding systems, so Alexa can better understand your requests, and ensure the service works well for everyone.

“We have strict technical and operational safeguards, and have a zero tolerance policy for the abuse of our system. Employees do not have direct access to information that can identify the person or account as part of this workflow. All information is treated with high confidentiality and we use multi-factor authentication to restrict access, service encryption and audits of our control environment to protect it.”

Amazon, in its marketing and privacy policy materials, doesn’t explicitly say humans are listening to recordings of some conversations picked up by Alexa. “We use your requests to Alexa to train our speech recognition and natural language understanding systems,” the company says in a list of frequently asked questions.

In Alexa’s privacy settings, Amazon gives users the option of disabling the use of their voice recordings for the development of new features. The company says people who opt out of that program might still have their recordings analyzed by hand over the regular course of the review process. A screenshot reviewed by Bloomberg shows that the recordings sent to the Alexa reviewers don’t provide a user’s full name and address but are associated with an account number, as well as the user’s first name and the device’s serial number.

The Intercept reported earlier this year that employees of Amazon-owned Ring manually identify vehicles and people in videos captured by the company’s doorbell cameras, an effort to better train the software to do that work itself.

“You don’t necessarily think of another human listening to what you’re telling your smart speaker in the intimacy of your home,” said Florian Schaub, a professor at the University of Michigan who has researched privacy issues related to smart speakers. “I think we’ve been conditioned to the [assumption] that these machines are just doing magic machine learning. But the fact is there is still manual processing involved.”

“Whether that’s a privacy concern or not depends on how cautious Amazon and other companies are in what type of information they have manually annotated, and how they present that information to someone,” he added.

When the Echo debuted in 2014, Amazon’s cylindrical smart speaker quickly popularized the use of voice software in the home. Before long, Alphabet Inc. launched its own version, called Google Home, followed by Apple Inc.’s HomePod. Various companies also sell their own devices in China. Globally, consumers bought 78 million smart speakers last year, according to researcher Canalys. Millions more use voice software to interact with digital assistants on their smartphones.

Alexa software is designed to continuously record snatches of audio, listening for a wake word. That’s “Alexa” by default, but people can change it to “Echo” or “computer.” When the wake word is detected, the light ring at the top of the Echo turns blue, indicating the device is recording and beaming a command to Amazon servers.

 Inside An Amazon 4-Star Store
An Echo smart speaker inside an Amazon 4-star store in Berkeley, California.
Photographer: Cayce Clifford/Bloomberg

Most modern speech-recognition systems rely on neural networks patterned on the human brain. The software learns as it goes, by spotting patterns amid vast amounts of data. The algorithms powering the Echo and other smart speakers use models of probability to make educated guesses. If someone asks Alexa if there’s a Greek place nearby, the algorithms know the user is probably looking for a restaurant, not a church or community center.

But sometimes Alexa gets it wrong—especially when grappling with new slang, regional colloquialisms or languages other than English. In French, avec sa, “with his” or “with her,” can confuse the software into thinking someone is using the Alexa wake word. Hecho, Spanish for a fact or deed, is sometimes misinterpreted as Echo. And so on. That’s why Amazon recruited human helpers to fill in the gaps missed by the algorithms.

Apple’s Siri also has human helpers, who work to gauge whether the digital assistant’s interpretation of requests lines up with what the person said. The recordings they review lack personally identifiable information and are stored for six months tied to a random identifier, according to an Apple security white paper. After that, the data is stripped of its random identification information but may be stored for longer periods to improve Siri’s voice recognition.

At Google, some reviewers can access some audio snippets from its Assistant to help train and improve the product, but it’s not associated with any personally identifiable information and the audio is distorted, the company says.

A recent Amazon job posting, seeking a quality assurance manager for Alexa Data Services in Bucharest, describes the role humans play: “Every day she [Alexa] listens to thousands of people talking to her about different topics and different languages, and she needs our help to make sense of it all.” The want ad continues: “This is big data handling like you’ve never seen it. We’re creating, labeling, curating and analyzing vast quantities of speech on a daily basis.”

Amazon’s review process for speech data begins when Alexa pulls a random, small sampling of customer voice recordings and sends the audio files to the far-flung employees and contractors, according to a person familiar with the program’s design.

 Amazon.com Inc. Holds Product Reveal Launch
The Echo Spot
Photographer: Daniel Berman/Bloomberg

Some Alexa reviewers are tasked with transcribing users’ commands, comparing the recordings to Alexa’s automated transcript, say, or annotating the interaction between user and machine. What did the person ask? Did Alexa provide an effective response?

Others note everything the speaker picks up, including background conversations—even when children are speaking. Sometimes listeners hear users discussing private details such as names or bank details; in such cases, they’re supposed to tick a dialog box denoting “critical data.” They then move on to the next audio file.

According to Amazon’s website, no audio is stored unless Echo detects the wake word or is activated by pressing a button. But sometimes Alexa appears to begin recording without any prompt at all, and the audio files start with a blaring television or unintelligible noise. Whether or not the activation is mistaken, the reviewers are required to transcribe it. One of the people said the auditors each transcribe as many as 100 recordings a day when Alexa receives no wake command or is triggered by accident.

In homes around the world, Echo owners frequently speculate about who might be listening, according to two of the reviewers. “Do you work for the NSA?” they ask. “Alexa, is someone else listening to us?”

— With assistance by Gerrit De Vynck, Mark Gurman, and Irina Vilcu

Source: https://www.bloomberg.com/news/articles/2019-04-10/is-anyone-listening-to-you-on-alexa-a-global-team-reviews-audio

How Companies Learn Your Secrets

Andrew Pole had just started working as a statistician for Target in 2002, when two colleagues from the marketing department stopped by his desk to ask an odd question: “If we wanted to figure out if a customer is pregnant, even if she didn’t want us to know, can you do that? ”

Pole has a master’s degree in statistics and another in economics, and has been obsessed with the intersection of data and human behavior most of his life. His parents were teachers in North Dakota, and while other kids were going to 4-H, Pole was doing algebra and writing computer programs. “The stereotype of a math nerd is true,” he told me when I spoke with him last year. “I kind of like going out and evangelizing analytics.”

As the marketers explained to Pole — and as Pole later explained to me, back when we were still speaking and before Target told him to stop — new parents are a retailer’s holy grail. Most shoppers don’t buy everything they need at one store. Instead, they buy groceries at the grocery store and toys at the toy store, and they visit Target only when they need certain items they associate with Target — cleaning supplies, say, or new socks or a six-month supply of toilet paper. But Target sells everything from milk to stuffed animals to lawn furniture to electronics, so one of the company’s primary goals is convincing customers that the only store they need is Target. But it’s a tough message to get across, even with the most ingenious ad campaigns, because once consumers’ shopping habits are ingrained, it’s incredibly difficult to change them.

There are, however, some brief periods in a person’s life when old routines fall apart and buying habits are suddenly in flux. One of those moments — the moment, really — is right around the birth of a child, when parents are exhausted and overwhelmed and their shopping patterns and brand loyalties are up for grabs. But as Target’s marketers explained to Pole, timing is everything. Because birth records are usually public, the moment a couple have a new baby, they are almost instantaneously barraged with offers and incentives and advertisements from all sorts of companies. Which means that the key is to reach them earlier, before any other retailers know a baby is on the way. Specifically, the marketers said they wanted to send specially designed ads to women in their second trimester, which is when most expectant mothers begin buying all sorts of new things, like prenatal vitamins and maternity clothing. “Can you give us a list?” the marketers asked.

“We knew that if we could identify them in their second trimester, there’s a good chance we could capture them for years,” Pole told me. “As soon as we get them buying diapers from us, they’re going to start buying everything else too. If you’re rushing through the store, looking for bottles, and you pass orange juice, you’ll grab a carton. Oh, and there’s that new DVD I want. Soon, you’ll be buying cereal and paper towels from us, and keep coming back.”

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The desire to collect information on customers is not new for Target or any other large retailer, of course. For decades, Target has collected vast amounts of data on every person who regularly walks into one of its stores. Whenever possible, Target assigns each shopper a unique code — known internally as the Guest ID number — that keeps tabs on everything they buy. “If you use a credit card or a coupon, or fill out a survey, or mail in a refund, or call the customer help line, or open an e-mail we’ve sent you or visit our Web site, we’ll record it and link it to your Guest ID,” Pole said. “We want to know everything we can.”

Also linked to your Guest ID is demographic information like your age, whether you are married and have kids, which part of town you live in, how long it takes you to drive to the store, your estimated salary, whether you’ve moved recently, what credit cards you carry in your wallet and what Web sites you visit. Target can buy data about your ethnicity, job history, the magazines you read, if you’ve ever declared bankruptcy or got divorced, the year you bought (or lost) your house, where you went to college, what kinds of topics you talk about online, whether you prefer certain brands of coffee, paper towels, cereal or applesauce, your political leanings, reading habits, charitable giving and the number of cars you own. (In a statement, Target declined to identify what demographic information it collects or purchases.) All that information is meaningless, however, without someone to analyze and make sense of it. That’s where Andrew Pole and the dozens of other members of Target’s Guest Marketing Analytics department come in.

Almost every major retailer, from grocery chains to investmentbanks to the U.S. Postal Service, has a “predictive analytics” department devoted to understanding not just consumers’ shopping habits but also their personal habits, so as to more efficiently market to them. “But Target has always been one of the smartest at this,” says Eric Siegel, a consultant and the chairman of a conference called Predictive Analytics World. “We’re living through a golden age of behavioral research. It’s amazing how much we can figure out about how people think now.”

The reason Target can snoop on our shopping habits is that, over the past two decades, the science of habit formation has become a major field of research in neurology and psychology departments at hundreds of major medical centers and universities, as well as inside extremely well financed corporate labs. “It’s like an arms race to hire statisticians nowadays,” said Andreas Weigend, the former chief scientist at Amazon.com. “Mathematicians are suddenly sexy.” As the ability to analyze data has grown more and more fine-grained, the push to understand how daily habits influence our decisions has become one of the most exciting topics in clinical research, even though most of us are hardly aware those patterns exist. One study from Duke University estimated that habits, rather than conscious decision-making, shape 45 percent of the choices we make every day, and recent discoveries have begun to change everything from the way we think about dieting to how doctors conceive treatments for anxiety, depression and addictions.

This research is also transforming our understanding of how habits function across organizations and societies. A football coach named Tony Dungy propelled one of the worst teams in the N.F.L. to the Super Bowl by focusing on how his players habitually reacted to on-field cues. Before he became Treasury secretary, Paul O’Neill overhauled a stumbling conglomerate, Alcoa, and turned it into a top performer in the Dow Jones by relentlessly attacking one habit — a specific approach to worker safety — which in turn caused a companywide transformation. The Obama campaign has hired a habit specialist as its “chief scientist” to figure out how to trigger new voting patterns among different constituencies.

Researchers have figured out how to stop people from habitually overeating and biting their nails. They can explain why some of us automatically go for a jog every morning and are more productive at work, while others oversleep and procrastinate. There is a calculus, it turns out, for mastering our subconscious urges. For companies like Target, the exhaustive rendering of our conscious and unconscious patterns into data sets and algorithms has revolutionized what they know about us and, therefore, how precisely they can sell.

Inside the brain-and-cognitive-sciences department of the Massachusetts Institute of Technology are what, to the casual observer, look like dollhouse versions of surgical theaters. There are rooms with tiny scalpels, small drills and miniature saws. Even the operating tables are petite, as if prepared for 7-year-old surgeons. Inside those shrunken O.R.’s, neurologists cut into the skulls of anesthetized rats, implanting tiny sensors that record the smallest changes in the activity of their brains.

An M.I.T. neuroscientist named Ann Graybiel told me that she and her colleagues began exploring habits more than a decade ago by putting their wired rats into a T-shaped maze with chocolate at one end. The maze was structured so that each animal was positioned behind a barrier that opened after a loud click. The first time a rat was placed in the maze, it would usually wander slowly up and down the center aisle after the barrier slid away, sniffing in corners and scratching at walls. It appeared to smell the chocolate but couldn’t figure out how to find it. There was no discernible pattern in the rat’s meanderings and no indication it was working hard to find the treat.

The probes in the rats’ heads, however, told a different story. While each animal wandered through the maze, its brain was working furiously. Every time a rat sniffed the air or scratched a wall, the neurosensors inside the animal’s head exploded with activity. As the scientists repeated the experiment, again and again, the rats eventually stopped sniffing corners and making wrong turns and began to zip through the maze with more and more speed. And within their brains, something unexpected occurred: as each rat learned how to complete the maze more quickly, its mental activity decreased. As the path became more and more automatic — as it became a habit — the rats started thinking less and less.

This process, in which the brain converts a sequence of actions into an automatic routine, is called “chunking.” There are dozens, if not hundreds, of behavioral chunks we rely on every day. Some are simple: you automatically put toothpaste on your toothbrush before sticking it in your mouth. Some, like making the kids’ lunch, are a little more complex. Still others are so complicated that it’s remarkable to realize that a habit could have emerged at all.

Take backing your car out of the driveway. When you first learned to drive, that act required a major dose of concentration, and for good reason: it involves peering into the rearview and side mirrors and checking for obstacles, putting your foot on the brake, moving the gearshift into reverse, removing your foot from the brake, estimating the distance between the garage and the street while keeping the wheels aligned, calculating how images in the mirrors translate into actual distances, all while applying differing amounts of pressure to the gas pedal and brake.

Now, you perform that series of actions every time you pull into the street without thinking very much. Your brain has chunked large parts of it. Left to its own devices, the brain will try to make almost any repeated behavior into a habit, because habits allow our minds to conserve effort. But conserving mental energy is tricky, because if our brains power down at the wrong moment, we might fail to notice something important, like a child riding her bike down the sidewalk or a speeding car coming down the street. So we’ve devised a clever system to determine when to let a habit take over. It’s something that happens whenever a chunk of behavior starts or ends — and it helps to explain why habits are so difficult to change once they’re formed, despite our best intentions.

To understand this a little more clearly, consider again the chocolate-seeking rats. What Graybiel and her colleagues found was that, as the ability to navigate the maze became habitual, there were two spikes in the rats’ brain activity — once at the beginning of the maze, when the rat heard the click right before the barrier slid away, and once at the end, when the rat found the chocolate. Those spikes show when the rats’ brains were fully engaged, and the dip in neural activity between the spikes showed when the habit took over. From behind the partition, the rat wasn’t sure what waited on the other side, until it heard the click, which it had come to associate with the maze. Once it heard that sound, it knew to use the “maze habit,” and its brain activity decreased. Then at the end of the routine, when the reward appeared, the brain shook itself awake again and the chocolate signaled to the rat that this particular habit was worth remembering, and the neurological pathway was carved that much deeper.

The process within our brains that creates habits is a three-step loop. First, there is a cue, a trigger that tells your brain to go into automatic mode and which habit to use. Then there is the routine, which can be physical or mental or emotional. Finally, there is a reward, which helps your brain figure out if this particular loop is worth remembering for the future. Over time, this loop — cue, routine, reward; cue, routine, reward — becomes more and more automatic. The cue and reward become neurologically intertwined until a sense of craving emerges. What’s unique about cues and rewards, however, is how subtle they can be. Neurological studies like the ones in Graybiel’s lab have revealed that some cues span just milliseconds. And rewards can range from the obvious (like the sugar rush that a morning doughnut habit provides) to the infinitesimal (like the barely noticeable — but measurable — sense of relief the brain experiences after successfully navigating the driveway). Most cues and rewards, in fact, happen so quickly and are so slight that we are hardly aware of them at all. But our neural systems notice and use them to build automatic behaviors.

Habits aren’t destiny — they can be ignored, changed or replaced. But it’s also true that once the loop is established and a habit emerges, your brain stops fully participating in decision-making. So unless you deliberately fight a habit — unless you find new cues and rewards — the old pattern will unfold automatically.

“We’ve done experiments where we trained rats to run down a maze until it was a habit, and then we extinguished the habit by changing the placement of the reward,” Graybiel told me. “Then one day, we’ll put the reward in the old place and put in the rat and, by golly, the old habit will re-emerge right away. Habits never really disappear.”

Luckily, simply understanding how habits work makes them easier to control. Take, for instance, a series of studies conducted a few years ago at Columbia University and the University of Alberta. Researchers wanted to understand how exercise habits emerge. In one project, 256 members of a health-insurance plan were invited to classes stressing the importance of exercise. Half the participants received an extra lesson on the theories of habit formation (the structure of the habit loop) and were asked to identify cues and rewards that might help them develop exercise routines.

The results were dramatic. Over the next four months, those participants who deliberately identified cues and rewards spent twice as much time exercising as their peers. Other studies have yielded similar results. According to another recent paper, if you want to start running in the morning, it’s essential that you choose a simple cue (like always putting on your sneakers before breakfast or leaving your running clothes next to your bed) and a clear reward (like a midday treat or even the sense of accomplishment that comes from ritually recording your miles in a log book). After a while, your brain will start anticipating that reward — craving the treat or the feeling of accomplishment — and there will be a measurable neurological impulse to lace up your jogging shoes each morning.

Our relationship to e-mail operates on the same principle. When a computer chimes or a smartphone vibrates with a new message, the brain starts anticipating the neurological “pleasure” (even if we don’t recognize it as such) that clicking on the e-mail and reading it provides. That expectation, if unsatisfied, can build until you find yourself moved to distraction by the thought of an e-mail sitting there unread — even if you know, rationally, it’s most likely not important. On the other hand, once you remove the cue by disabling the buzzing of your phone or the chiming of your computer, the craving is never triggered, and you’ll find, over time, that you’re able to work productively for long stretches without checking your in-box.

Some of the most ambitious habit experiments have been conducted by corporate America. To understand why executives are so entranced by this science, consider how one of the world’s largest companies, Procter & Gamble, used habit insights to turn a failing product into one of its biggest sellers. P.& G. is the corporate behemoth behind a whole range of products, from Downy fabric softener to Bounty paper towels to Duracell batteries and dozens of other household brands. In the mid-1990s, P.& G.’s executives began a secret project to create a new product that could eradicate bad smells. P.& G. spent millions developing a colorless, cheap-to-manufacture liquid that could be sprayed on a smoky blouse, stinky couch, old jacket or stained car interior and make it odorless. In order to market the product — Febreze — the company formed a team that included a former Wall Street mathematician named Drake Stimson and habit specialists, whose job was to make sure the television commercials, which they tested in Phoenix, Salt Lake City and Boise, Idaho, accentuated the product’s cues and rewards just right.

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The first ad showed a woman complaining about the smoking section of a restaurant. Whenever she eats there, she says, her jacket smells like smoke. A friend tells her that if she uses Febreze, it will eliminate the odor. The cue in the ad is clear: the harsh smell of cigarette smoke. The reward: odor eliminated from clothes. The second ad featured a woman worrying about her dog, Sophie, who always sits on the couch. “Sophie will always smell like Sophie,” she says, but with Febreze, “now my furniture doesn’t have to.” The ads were put in heavy rotation. Then the marketers sat back, anticipating how they would spend their bonuses. A week passed. Then two. A month. Two months. Sales started small and got smaller. Febreze was a dud.

The panicked marketing team canvassed consumers and conducted in-depth interviews to figure out what was going wrong, Stimson recalled. Their first inkling came when they visited a woman’s home outside Phoenix. The house was clean and organized. She was something of a neat freak, the woman explained. But when P.& G.’s scientists walked into her living room, where her nine cats spent most of their time, the scent was so overpowering that one of them gagged.

According to Stimson, who led the Febreze team, a researcher asked the woman, “What do you do about the cat smell?”

“It’s usually not a problem,” she said.

“Do you smell it now?”

“No,” she said. “Isn’t it wonderful? They hardly smell at all!”

A similar scene played out in dozens of other smelly homes. The reason Febreze wasn’t selling, the marketers realized, was that people couldn’t detect most of the bad smells in their lives. If you live with nine cats, you become desensitized to their scents. If you smoke cigarettes, eventually you don’t smell smoke anymore. Even the strongest odors fade with constant exposure. That’s why Febreze was a failure. The product’s cue — the bad smells that were supposed to trigger daily use — was hidden from the people who needed it the most. And Febreze’s reward (an odorless home) was meaningless to someone who couldn’t smell offensive scents in the first place.

P.& G. employed a Harvard Business School professor to analyze Febreze’s ad campaigns. They collected hours of footage of people cleaning their homes and watched tape after tape, looking for clues that might help them connect Febreze to people’s daily habits. When that didn’t reveal anything, they went into the field and conducted more interviews. A breakthrough came when they visited a woman in a suburb near Scottsdale, Ariz., who was in her 40s with four children. Her house was clean, though not compulsively tidy, and didn’t appear to have any odor problems; there were no pets or smokers. To the surprise of everyone, she loved Febreze.

“I use it every day,” she said.

“What smells are you trying to get rid of?” a researcher asked.

“I don’t really use it for specific smells,” the woman said. “I use it for normal cleaning — a couple of sprays when I’m done in a room.”

The researchers followed her around as she tidied the house. In the bedroom, she made her bed, tightened the sheet’s corners, then sprayed the comforter with Febreze. In the living room, she vacuumed, picked up the children’s shoes, straightened the coffee table, then sprayed Febreze on the freshly cleaned carpet.

“It’s nice, you know?” she said. “Spraying feels like a little minicelebration when I’m done with a room.” At the rate she was going, the team estimated, she would empty a bottle of Febreze every two weeks.

When they got back to P.& G.’s headquarters, the researchers watched their videotapes again. Now they knew what to look for and saw their mistake in scene after scene. Cleaning has its own habit loops that already exist. In one video, when a woman walked into a dirty room (cue), she started sweeping and picking up toys (routine), then she examined the room and smiled when she was done (reward). In another, a woman scowled at her unmade bed (cue), proceeded to straighten the blankets and comforter (routine) and then sighed as she ran her hands over the freshly plumped pillows (reward). P.& G. had been trying to create a whole new habit with Febreze, but what they really needed to do was piggyback on habit loops that were already in place. The marketers needed to position Febreze as something that came at the end of the cleaning ritual, the reward, rather than as a whole new cleaning routine.

The company printed new ads showing open windows and gusts of fresh air. More perfume was added to the Febreze formula, so that instead of merely neutralizing odors, the spray had its own distinct scent. Television commercials were filmed of women, having finished their cleaning routine, using Febreze to spritz freshly made beds and just-laundered clothing. Each ad was designed to appeal to the habit loop: when you see a freshly cleaned room (cue), pull out Febreze (routine) and enjoy a smell that says you’ve done a great job (reward). When you finish making a bed (cue), spritz Febreze (routine) and breathe a sweet, contented sigh (reward). Febreze, the ads implied, was a pleasant treat, not a reminder that your home stinks.

And so Febreze, a product originally conceived as a revolutionary way to destroy odors, became an air freshener used once things are already clean. The Febreze revamp occurred in the summer of 1998. Within two months, sales doubled. A year later, the product brought in $230 million. Since then Febreze has spawned dozens of spinoffs — air fresheners, candles and laundry detergents — that now account for sales of more than $1 billion a year. Eventually, P.& G. began mentioning to customers that, in addition to smelling sweet, Febreze can actually kill bad odors. Today it’s one of the top-selling products in the world.

Andrew Pole was hired by Target to use the same kinds of insights into consumers’ habits to expand Target’s sales. His assignment was to analyze all the cue-routine-reward loops among shoppers and help the company figure out how to exploit them. Much of his department’s work was straightforward: find the customers who have children and send them catalogs that feature toys before Christmas. Look for shoppers who habitually purchase swimsuits in April and send them coupons for sunscreen in July and diet books in December. But Pole’s most important assignment was to identify those unique moments in consumers’ lives when their shopping habits become particularly flexible and the right advertisement or coupon would cause them to begin spending in new ways.

In the 1980s, a team of researchers led by a U.C.L.A. professor named Alan Andreasen undertook a study of peoples’ most mundane purchases, like soap, toothpaste, trash bags and toilet paper. They learned that most shoppers paid almost no attention to how they bought these products, that the purchases occurred habitually, without any complex decision-making. Which meant it was hard for marketers, despite their displays and coupons and product promotions, to persuade shoppers to change.

But when some customers were going through a major life event, like graduating from college or getting a new job or moving to a new town, their shopping habits became flexible in ways that were both predictable and potential gold mines for retailers. The study found that when someone marries, he or she is more likely to start buying a new type of coffee. When a couple move into a new house, they’re more apt to purchase a different kind of cereal. When they divorce, there’s an increased chance they’ll start buying different brands of beer.

Consumers going through major life events often don’t notice, or care, that their shopping habits have shifted, but retailers notice, and they care quite a bit. At those unique moments, Andreasen wrote, customers are “vulnerable to intervention by marketers.” In other words, a precisely timed advertisement, sent to a recent divorcee or new homebuyer, can change someone’s shopping patterns for years.

And among life events, none are more important than the arrival of a baby. At that moment, new parents’ habits are more flexible than at almost any other time in their adult lives. If companies can identify pregnant shoppers, they can earn millions.

The only problem is that identifying pregnant customers is harder than it sounds. Target has a baby-shower registry, and Pole started there, observing how shopping habits changed as a woman approached her due date, which women on the registry had willingly disclosed. He ran test after test, analyzing the data, and before long some useful patterns emerged. Lotions, for example. Lots of people buy lotion, but one of Pole’s colleagues noticed that women on the baby registry were buying larger quantities of unscented lotion around the beginning of their second trimester. Another analyst noted that sometime in the first 20 weeks, pregnant women loaded up on supplements like calcium, magnesium and zinc. Many shoppers purchase soap and cotton balls, but when someone suddenly starts buying lots of scent-free soap and extra-big bags of cotton balls, in addition to hand sanitizers and washcloths, it signals they could be getting close to their delivery date.

As Pole’s computers crawled through the data, he was able to identify about 25 products that, when analyzed together, allowed him to assign each shopper a “pregnancy prediction” score. More important, he could also estimate her due date to within a small window, so Target could send coupons timed to very specific stages of her pregnancy.

One Target employee I spoke to provided a hypothetical example. Take a fictional Target shopper named Jenny Ward, who is 23, lives in Atlanta and in March bought cocoa-butter lotion, a purse large enough to double as a diaper bag, zinc and magnesium supplements and a bright blue rug. There’s, say, an 87 percent chance that she’s pregnant and that her delivery date is sometime in late August. What’s more, because of the data attached to her Guest ID number, Target knows how to trigger Jenny’s habits. They know that if she receives a coupon via e-mail, it will most likely cue her to buy online. They know that if she receives an ad in the mail on Friday, she frequently uses it on a weekend trip to the store. And they know that if they reward her with a printed receipt that entitles her to a free cup of Starbucks coffee, she’ll use it when she comes back again.

In the past, that knowledge had limited value. After all, Jenny purchased only cleaning supplies at Target, and there were only so many psychological buttons the company could push. But now that she is pregnant, everything is up for grabs. In addition to triggering Jenny’s habits to buy more cleaning products, they can also start including offers for an array of products, some more obvious than others, that a woman at her stage of pregnancy might need.

Pole applied his program to every regular female shopper in Target’s national database and soon had a list of tens of thousands of women who were most likely pregnant. If they could entice those women or their husbands to visit Target and buy baby-related products, the company’s cue-routine-reward calculators could kick in and start pushing them to buy groceries, bathing suits, toys and clothing, as well. When Pole shared his list with the marketers, he said, they were ecstatic. Soon, Pole was getting invited to meetings above his paygrade. Eventually his paygrade went up.

At which point someone asked an important question: How are women going to react when they figure out how much Target knows?

“If we send someone a catalog and say, ‘Congratulations on your first child!’ and they’ve never told us they’re pregnant, that’s going to make some people uncomfortable,” Pole told me. “We are very conservative about compliance with all privacy laws. But even if you’re following the law, you can do things where people get queasy.”

About a year after Pole created his pregnancy-prediction model, a man walked into a Target outside Minneapolis and demanded to see the manager. He was clutching coupons that had been sent to his daughter, and he was angry, according to an employee who participated in the conversation.

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“My daughter got this in the mail!” he said. “She’s still in high school, and you’re sending her coupons for baby clothes and cribs? Are you trying to encourage her to get pregnant?”

The manager didn’t have any idea what the man was talking about. He looked at the mailer. Sure enough, it was addressed to the man’s daughter and contained advertisements for maternity clothing, nursery furniture and pictures of smiling infants. The manager apologized and then called a few days later to apologize again.

On the phone, though, the father was somewhat abashed. “I had a talk with my daughter,” he said. “It turns out there’s been some activities in my house I haven’t been completely aware of. She’s due in August. I owe you an apology.”

When I approached Target to discuss Pole’s work, its representatives declined to speak with me. “Our mission is to make Target the preferred shopping destination for our guests by delivering outstanding value, continuous innovation and exceptional guest experience,” the company wrote in a statement. “We’ve developed a number of research tools that allow us to gain insights into trends and preferences within different demographic segments of our guest population.” When I sent Target a complete summary of my reporting, the reply was more terse: “Almost all of your statements contain inaccurate information and publishing them would be misleading to the public. We do not intend to address each statement point by point.” The company declined to identify what was inaccurate. They did add, however, that Target “is in compliance with all federal and state laws, including those related to protected health information.”

When I offered to fly to Target’s headquarters to discuss its concerns, a spokeswoman e-mailed that no one would meet me. When I flew out anyway, I was told I was on a list of prohibited visitors. “I’ve been instructed not to give you access and to ask you to leave,” said a very nice security guard named Alex.

Using data to predict a woman’s pregnancy, Target realized soon after Pole perfected his model, could be a public-relations disaster. So the question became: how could they get their advertisements into expectant mothers’ hands without making it appear they were spying on them? How do you take advantage of someone’s habits without letting them know you’re studying their lives?

Before I met Andrew Pole, before I even decided to write a book about the science of habit formation, I had another goal: I wanted to lose weight.

I had got into a bad habit of going to the cafeteria every afternoon and eating a chocolate-chip cookie, which contributed to my gaining a few pounds. Eight, to be precise. I put a Post-it note on my computer reading “NO MORE COOKIES.” But every afternoon, I managed to ignore that note, wander to the cafeteria, buy a cookie and eat it while chatting with colleagues. Tomorrow, I always promised myself, I’ll muster the willpower to resist.

Tomorrow, I ate another cookie.

When I started interviewing experts in habit formation, I concluded each interview by asking what I should do. The first step, they said, was to figure out my habit loop. The routine was simple: every afternoon, I walked to the cafeteria, bought a cookie and ate it while chatting with friends.

Next came some less obvious questions: What was the cue? Hunger? Boredom? Low blood sugar? And what was the reward? The taste of the cookie itself? The temporary distraction from my work? The chance to socialize with colleagues?

Rewards are powerful because they satisfy cravings, but we’re often not conscious of the urges driving our habits in the first place. So one day, when I felt a cookie impulse, I went outside and took a walk instead. The next day, I went to the cafeteria and bought a coffee. The next, I bought an apple and ate it while chatting with friends. You get the idea. I wanted to test different theories regarding what reward I was really craving. Was it hunger? (In which case the apple should have worked.) Was it the desire for a quick burst of energy? (If so, the coffee should suffice.) Or, as turned out to be the answer, was it that after several hours spent focused on work, I wanted to socialize, to make sure I was up to speed on office gossip, and the cookie was just a convenient excuse? When I walked to a colleague’s desk and chatted for a few minutes, it turned out, my cookie urge was gone.

All that was left was identifying the cue.

Deciphering cues is hard, however. Our lives often contain too much information to figure out what is triggering a particular behavior. Do you eat breakfast at a certain time because you’re hungry? Or because the morning news is on? Or because your kids have started eating? Experiments have shown that most cues fit into one of five categories: location, time, emotional state, other people or the immediately preceding action. So to figure out the cue for my cookie habit, I wrote down five things the moment the urge hit:

Where are you? (Sitting at my desk.)

What time is it? (3:36 p.m.)

What’s your emotional state? (Bored.)

Who else is around? (No one.)

What action preceded the urge? (Answered an e-mail.)

The next day I did the same thing. And the next. Pretty soon, the cue was clear: I always felt an urge to snack around 3:30.

Once I figured out all the parts of the loop, it seemed fairly easy to change my habit. But the psychologists and neuroscientists warned me that, for my new behavior to stick, I needed to abide by the same principle that guided Procter & Gamble in selling Febreze: To shift the routine — to socialize, rather than eat a cookie — I needed to piggyback on an existing habit. So now, every day around 3:30, I stand up, look around the newsroom for someone to talk to, spend 10 minutes gossiping, then go back to my desk. The cue and reward have stayed the same. Only the routine has shifted. It doesn’t feel like a decision, any more than the M.I.T. rats made a decision to run through the maze. It’s now a habit. I’ve lost 21 pounds since then (12 of them from changing my cookie ritual).

After Andrew Pole built his pregnancy-prediction model, after he identified thousands of female shoppers who were most likely pregnant, after someone pointed out that some of those women might be a little upset if they received an advertisement making it obvious Target was studying their reproductive status, everyone decided to slow things down.

The marketing department conducted a few tests by choosing a small, random sample of women from Pole’s list and mailing them combinations of advertisements to see how they reacted.

“We have the capacity to send every customer an ad booklet, specifically designed for them, that says, ‘Here’s everything you bought last week and a coupon for it,’ ” one Target executive told me. “We do that for grocery products all the time.” But for pregnant women, Target’s goal was selling them baby items they didn’t even know they needed yet.

“With the pregnancy products, though, we learned that some women react badly,” the executive said. “Then we started mixing in all these ads for things we knew pregnant women would never buy, so the baby ads looked random. We’d put an ad for a lawn mower next to diapers. We’d put a coupon for wineglasses next to infant clothes. That way, it looked like all the products were chosen by chance.

“And we found out that as long as a pregnant woman thinks she hasn’t been spied on, she’ll use the coupons. She just assumes that everyone else on her block got the same mailer for diapers and cribs. As long as we don’t spook her, it works.”

In other words, if Target piggybacked on existing habits — the same cues and rewards they already knew got customers to buy cleaning supplies or socks — then they could insert a new routine: buying baby products, as well. There’s a cue (“Oh, a coupon for something I need!”) a routine (“Buy! Buy! Buy!”) and a reward (“I can take that off my list”). And once the shopper is inside the store, Target will hit her with cues and rewards to entice her to purchase everything she normally buys somewhere else. As long as Target camouflaged how much it knew, as long as the habit felt familiar, the new behavior took hold.

Soon after the new ad campaign began, Target’s Mom and Baby sales exploded. The company doesn’t break out figures for specific divisions, but between 2002 — when Pole was hired — and 2010, Target’s revenues grew from $44 billion to $67 billion. In 2005, the company’s president, Gregg Steinhafel, boasted to a room of investors about the company’s “heightened focus on items and categories that appeal to specific guest segments such as mom and baby.”

Pole was promoted. He has been invited to speak at conferences. “I never expected this would become such a big deal,” he told me the last time we spoke.

A few weeks before this article went to press, I flew to Minneapolis to try and speak to Andrew Pole one last time. I hadn’t talked to him in more than a year. Back when we were still friendly, I mentioned that my wife was seven months pregnant. We shop at Target, I told him, and had given the company our address so we could start receiving coupons in the mail. As my wife’s pregnancy progressed, I noticed a subtle upswing in the number of advertisements for diapers and baby clothes arriving at our house.

Pole didn’t answer my e-mails or phone calls when I visited Minneapolis. I drove to his large home in a nice suburb, but no one answered the door. On my way back to the hotel, I stopped at a Target to pick up some deodorant, then also bought some T-shirts and a fancy hair gel. On a whim, I threw in some pacifiers, to see how the computers would react. Besides, our baby is now 9 months old. You can’t have too many pacifiers.

When I paid, I didn’t receive any sudden deals on diapers or formula, to my slight disappointment. It made sense, though: I was shopping in a city I never previously visited, at 9:45 p.m. on a weeknight, buying a random assortment of items. I was using a corporate credit card, and besides the pacifiers, hadn’t purchased any of the things that a parent needs. It was clear to Target’s computers that I was on a business trip. Pole’s prediction calculator took one look at me, ran the numbers and decided to bide its time. Back home, the offers would eventually come. As Pole told me the last time we spoke: “Just wait. We’ll be sending you coupons for things you want before you even know you want them.”

Source: https://www.nytimes.com/2012/02/19/magazine/shopping-habits.html February 2012

Google’s DeepMind AI can accurately detect 50 types of eye disease just by looking at scans

Mustafa Suleyman 1831_preview (1)DeepMind cofounder Mustafa Suleyman.DeepMind
  • Google’s artificial intelligence company DeepMind has published „really significant“ research showing its algorithm can identify around 50 eye diseases by looking at retinal eye scans.
  • DeepMind said its AI was as good as expert clinicians, and that it could help prevent people from losing their sight.
  • DeepMind has been criticised for its practices around medical data, but cofounder Mustafa Suleyman said all the information in this research project was anonymised.
  • The company plans to hand the technology over for free to NHS hospitals for five years, provided it passes the next phase of research.

Google’s artificial intelligence company, DeepMind, has developed an AI which can successfully detect more than 50 types of eye disease just by looking at 3D retinal scans.

DeepMind published on Monday the results of joint research with Moorfields Eye Hospital, a renowned centre for treating eye conditions in London, in Nature Medicine.

The company said its AI was as accurate as expert clinicians when it came to detecting diseases, such as diabetic eye disease and macular degeneration. It could also recommend the best course of action for patients and suggest which needed urgent care.

OCT scanA technician examines an OCT scan.DeepMind

What is especially significant about the research, according to DeepMind cofounder Mustafa Suleyman, is that the AI has a level of „explainability“ that could boost doctors‘ trust in its recommendations.

„It’s possible for the clinician to interpret what the algorithm is thinking,“ he told Business Insider. „[They can] look at the underlying segmentation.“

In other words, the AI looks less like a mysterious black box that’s spitting out results. It labels pixels on the eye scan that corresponds to signs of a particular disease, Suleyman explained, and can calculate its confidence in its own findings with a percentage score. „That’s really significant,“ he said.

DeepMind's algorithm analysing an OCT eye scanDeepMind’s AI analysing an OCT scan.DeepMind

Suleyman described the findings as a „research breakthrough“ and said the next step was to prove the AI works in a clinical setting. That, he said, would take a number of years. Once DeepMind is in a position to deploy its AI across NHS hospitals in the UK, it will provide the service for free for five years.

Patients are at risk of losing their sight because doctors can’t look at their eye scans in time

British eye specialists have been warning for years that patients are at risk of losing their sight because the NHS is overstretched, and because the UK has an ageing population.

Part of the reason DeepMind and Moorfields took up the research project was because clinicians are „overwhelmed“ by the demand for eye scans, Suleyman said.

„If you have a sight-threatening disease, you want treatment as soon as possible,“ he explained. „And unlike in A&E, where a staff nurse will talk to you and make an evaluation of how serious your condition is, then use that evaluation to decide how quickly you are seen. When an [eye] scan is submitted, there isn’t a triage of your scan according to its severity.“

OCT scanA patient having an OCT scan.DeepMind

Putting eye scans through the AI could speed the entire process up.

„In the future, I could envisage a person going into their local high street optician, and have an OCT scan done and this algorithm would identify those patients with sight-threatening disease at the very early stage of the condition,“ said Dr Pearse Keane, consultant ophthalmologist at Moorfields Eye Hospital.

DeepMind’s AI was trained on a database of almost 15,000 eye scans, stripped of any identifying information. DeepMind worked with clinicians to label areas of disease, then ran those labelled images through its system. Suleyman said the two-and-a-half project required „huge investment“ from DeepMind and involved 25 staffers, as well as the researchers from Moorfields.

People are still worried about a Google-linked company having access to medical data

Google acquired DeepMind in 2014 for £400 million ($509 million), and the British AI company is probably most famous for AlphaGo, its algorithm that beat the world champion at the strategy game Go.

While DeepMind has remained UK-based and independent from Google, the relationship has attracted scrutiny. The main question is whether Google, a private US company, should have access to the sensitive medical data required for DeepMind’s health arm.

DeepMind was criticised in 2016 for failing to disclose its access to historical medical data during a project with Royal Free Hospital. Suleyman said the eye scans processed by DeepMind were „completely anonymised.“

„You can’t identify whose scans it was. We’re in quite a different regime, this is very much research, and we’re a number of years from being able to deploy in practice,“ he said.

Suleyman added: „How this has the potential to have transform the NHS is very clear. We’ve been very conscious that this will be a model that’s published, and available to others to implement.

„The labelled dataset is available to other researchers. So this is very much an open and collaborative relationship between equals that we’ve worked hard to foster. I’m proud of that work.“

 

https://www.businessinsider.de/google-deepmind-ai-detects-eye-disease-2018-8?r=US&IR=T