October 19, 2017

I never signed up for this! Privacy implications of email tracking

In this post I discuss a new paper that will appear at PETS 2018, authored by myself, Jeffrey Han, and Arvind Narayanan.

What happens when you open an email and allow it to display embedded images and pixels? You may expect the sender to learn that you’ve read the email, and which device you used to read it. But in a new paper we find that privacy risks of email tracking extend far beyond senders knowing when emails are viewed. Opening an email can trigger requests to tens of third parties, and many of these requests contain your email address. This allows those third parties to track you across the web and connect your online activities to your email address, rather than just to a pseudonymous cookie.

Illustrative example. Consider an email from the deals website LivingSocial (see details of the example email). When the email is opened, client will make requests to 24 third parties across 29 third-party domains.[1] A total of 10 third parties receive an MD5 hash of the user’s email address, including major data brokers Datalogix and Acxiom. Nearly all of the third parties (22 of the 24) set or receive cookies with their requests. In a webmail client the cookies are the same browser cookies used to track users on the web, and indeed many major web trackers (including domains belonging to Google, comScore, Adobe, and AOL) are loaded when the email is opened. While this example email has a large number of trackers relative to the average email in our corpus, the majority of emails (70%) embed at least one tracker.

How it works. Email tracking is possible because modern graphical email clients allow rendering a subset of HTML. JavaScript is invariably stripped, but embedded images and stylesheets are allowed. These are downloaded and rendered by the email client when the user views the email.[2] Crucially, many email clients, and almost all web browsers, in the case of webmail, send third-party cookies with these requests. The email address is leaked by being encoded as a parameter into these third-party URLs.

Diagram showing the process of tracking with email address

When the user opens the email, a tracking pixel from “tracker.com” is loaded. The user’s email address is included as a parameter within the pixel’s URL. The email client here is a web browser, so it automatically sends the tracking cookies for “tracker.com” along with the request. This allows the tracker to create a link between the user’s cookie and her email address. Later, when the user browses a news website, the browser sends the same cookie, and thus the new activity can be connected back to the email address. Email addresses are generally unique and persistent identifiers. So email-based tracking can be used for targeting online ads based on offline activity (say, to shoppers who used a loyalty card linked to an email address) and for linking different devices belonging to the same user.

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Getting serious about research ethics: Security and Internet Measurement

[This blog post is a continuation of our series about research ethics in computer science that we started last week]

Research projects in the information security and Internet measurement sub-disciplines typically interact with third-party systems or devices to collect a large amounts of data. Scholars engaging in these fields are interested to collect data about technical phenomenon. As a result of the widespread use of the Internet, their experiments can interfere with human use of devices and reveal all sorts of private information, such as their browsing behaviour. As awareness of the unintended impact on Internet users grew, these communities have spent considerable time debating their ethical standards at conferences, dedicated workshops, and in journal publications. Their efforts have culminated in guidelines for topics such as vulnerability disclosure or privacy, whereby the aim is to protect unsuspecting Internet users and human implicated in technical research.


Prof. Nick Feamster, Prof. Prateek Mittal, moderator Prof. Elana Zeide, and I discussed some important considerations for research ethics in a panel dedicated to these sub-disciplines at the recent CITP conference on research ethics in computer science communities. We started by explaining that gathering empirical data is crucial to infer the state of values such as privacy and trust in communication systems. However, as methodological choices in computer science will often have ethical impacts, researchers need to be empowered to reflect on their experimental setup meaningfully.


Prof. Feamster discussed several cases where he had sought advice from ethical oversight bodies, but was left with unsatisfying guidance. For example, when his team conducted Internet censorship measurements (pdf), they were aware that they were initiating requests and creating data flows from devices owned by unsuspecting Internet users. These new information flows were created in realms where adversaries were also operating, for example in the form of a government censors. This may pose a risk to the owners of devices that were implicated in the experimentation and data collection. The ethics board, however, concluded that such measurements did not meet the strict definition of “human subjects research”, which thereby excluded the need for formal review. Prof. Feamster suggests computer scientists reassess how they think about their technologies or newly initiated data flows that can be misused by adversaries, and take that into account in ethical review procedures.


Ethical tensions and dilemmas in technical Internet research could be seen as interesting research problems for scholars, argued Prof. Mittal. For example, to reason about privacy and trust in the anonymous Tor network, researchers need to understand to what extent adversaries can exploit vulnerabilities and thus observe Internet traffic of individual users. The obvious, relatively easy, and ethically dubious measurement would be to attack existing Tor nodes and attempt to collect real-time traffic of identifiable users. However, Prof. Mittal gave an insight into his own critical engagement with alternative design choices, which led his team to create a new node within Princeton’s university network that they subsequently attacked. This more lab-based approach eliminates risks for unsuspecting Internet users, but allowed for the same inferences to be done.


I concluded the panel, suggesting that ethics review boards at universities, academic conferences, and scholarly journals engage actively with computer scientists to collect valuable data whilst respecting human values. Currently, a panel on non-experts in either computer science or research ethics are given a single moment to judge the full methodology of a research proposal or the resulting paper. When a thumbs-down is issued, researchers have no or limited opportunity to remedy their ethical shortcomings. I argued that a better approach would be an iterative process with in-person meetings and more in-depth consideration of design alternatives, as demonstrated in a recent paper about Advertising as a Platform for Internet measurements (pdf). This is the approach advocates in the Networked Systems Ethics Guidelines. Cross-disciplinary conversation, rather than one-time decisions, allow for a mutual understanding between the gatekeepers of ethical standards and designers of useful computer science research.


See the video of the panel here.

The Princeton Web Census: a 1-million-site measurement and analysis of web privacy

Web privacy measurement — observing websites and services to detect, characterize, and quantify privacy impacting behaviors — has repeatedly forced companies to improve their privacy practices due to public pressure, press coverage, and regulatory action. In previous blog posts I’ve analyzed why our 2014 collaboration with KU Leuven researchers studying canvas fingerprinting was successful, and discussed why repeated, large-scale measurement is necessary.

Today I’m pleased to release initial analysis results from our monthly, 1-million-site measurement. This is the largest and most detailed measurement of online tracking to date, including measurements for stateful (cookie-based) and stateless (fingerprinting-based) tracking, the effect of browser privacy tools, and “cookie syncing”.  These results represent a snapshot of web tracking, but the analysis is part of an effort to collect data on a monthly basis and analyze the evolution of web tracking and privacy over time.

Our measurement platform used for this study, OpenWPM, is already open source. Today, we’re making the datasets for this analysis available for download by the public. You can find download instructions on our study’s website.

New findings

We provide background information and summary of each of our main findings on our study’s website. The paper goes into even greater detail and provides the methodological details on the measurement and analysis of each finding. One of our more surprising findings was the discovery of two apparent attempts to use the HTML5 Audio API for fingerprinting.

The figure is a visualization of the audio processing executed on users’ browsers by third-party fingerprinting scripts. We found two different AudioNode configurations in use. In both configurations an audio signal is generated by an oscillator and the resulting signal is hashed to create an identifier. Initial testing shows that the techniques may have some limitations when used for fingerprinting, but further analysis is necessary. You can help us with that (and test your own device) by using our demonstration page here.

See the paper for our analysis of a consolidated third-party ecosystem, the effects of third parties on HTTPS adoption, and examine the performance of tracking protection tools. In addition to audio fingerprinting, we show that canvas fingerprint is being used by more third parties, but on less sites; that a WebRTC feature can and is being used for tracking; and how the HTML Canvas is being used to discover user’s fonts.

What’s next? We are exploring ways to share our data and analysis tools in a form that’s useful to a wider and less technical audience. As we continue to collect data, we will also perform longitudinal analyses of web tracking. In other ongoing research, we’re using the data we’ve collected to train machine-learning models to automatically detect tracking and fingerprinting.