De-Anonymizing Web Browsing Data with Social Networks | Su, Shukla, Goel, Narayanan

Jessica Su, Ansh Shukla, Sharad Goel, Arvind Narayanan; De-Anonymizing Web Browsing Data with Social Networks; draft; In Some Venue Surely (they will publish this somewhere, it is so very nicely formatted); 2017-05; 9 pages.


Can online trackers and network adversaries de-anonymize web browsing data readily available to them? We show—theoretically, via simulation, and through experiments on real user data—that de-identified web browsing histories can be linked to social media profiles using only publicly available data. Our approach is based on a simple observation: each person has a distinctive social network, and thus the set of links appearing in one’s feed is unique. Assuming users visit links in their feed with higher probability than a random user, browsing histories contain tell-tale marks of identity. We formalize this intuition by specifying a model of web browsing behavior and then deriving the maximum likelihood estimate of a user’s social profile. We evaluate this strategy on simulated browsing histories, and show that given a history with 30 links originating from Twitter, we can deduce the corresponding Twitter profile more than 50% of the time. To gauge the real-world e↵ectiveness of this approach, we recruited nearly 400 people to donate their web browsing histories, and we were able to correctly identify more than 70% of them. We further show that several online trackers are embedded on sufficiently many websites to carry out this attack with high accuracy. Our theoretical contribution applies to any type of transactional data and is robust to noisy observations, generalizing a wide range of previous de-anonymization attacks. Finally, since our attack attempts to find the correct Twitter profile out of over 300 million candidates, it is—to our knowledge—the largest-scale demonstrated de-anonymization to date.




  • <quote>Network adversaries—including government surveillance agencies, Internet service providers, and co↵ee shop eavesdroppers—also see URLs of unencrypted web traffic. The adversary may also be a cross-device tracking company aiming to link two di↵erent browsing histories (e.g., histories generated by the same user on di↵erent devices). For such an adversary, linking to social media profiles is a stepping stone.</quote>


374 people confirmed the accuracy of our deanonymization attempt.
268 people (72%) were the top candidate generated by the MLE when using links.
303 people (81%) were among the top 15 candidates generated by the MLE when using links.
Yet only 49% de-anonymization when using fully expanded links (the redirect target of the link)

<paraphrasing>We recruited participants by advertising the experiment on a variety of websites, including

  • Twitter,
  • Facebook,
  • Quora,
  • Hacker News,
  • Freedom to Tinker
Story Line
people submitted web browsing histories.
119 cases (18%)
the application encountered a fatal error (e.g., because the Twitter API was temporarily unavailable), and it was unable to run the de-anonymization algorithm.
530 cases
remaining are useful.

87 users (16%)
had fewer than four informative links, and so no attempt to de-anonymize them was made.
443 users
remaining are useful.

374 users (84%)
confirmed whether or not our de-anonymization attempt was successful.
77 users (21%),/dt>
additionally disclosed their identity by signing into Twitter.

Apology: noted that the users who participated in our experiment are not representative of the Twitter population. In particular, they are quite active: the users who reported their identity had a median number of 378 followers and posted a median number of 2,041 total tweets.


Framing (Environment)

  • TargetConsumer is a Registered Twitter User,
    with activity and warm content selection algo in operation at Twitter HQ
  • Twitter algo selects snippets for presentation to TargetConsumer.
  • TargetConsumer either elects to read or discards the linked page.
  • An URL trail is recorded by The Panopticon Surveillance Machinery in The Record
  • Adversary has access to The Record across long spans of time and large numbers of TargetConsumers.

Problem Statement

  • Can one or many TargetConsumers be distinguished solely by URL traces in The Record?

Algorithm (Conceptual)

See C. Y. Ma, D. K. Yau, N. K. Yip, N. S. Rao. “Privacy vulnerability of published anonymous mobility traces,” In IEEE/ACM Transactions on Networking, 21(3):720–733, 2013.

  1. The simple model of web browsing behavior in which a user’s likelihood of visiting a URL is governed by the URL’s overall popularity and whether the URL appeared in the TargetConsumer’s Twitter feed.
  2. For each TargetConsumer, we compute their likelihood (under the model) of generating a given anonymous browsing history.
  3. Identify the TargetConsumer most likely to have generated that history.



  • Cookie Syncing
  • E-Tag
  • HTML5 localStorage
  • Jaccard Similarity
  • Maximum Liklihood Estimate (MLE)
  • URL (URL)


  • Ad Networks Can Personally Identify Web Users; Wendy Davis; In MediaPost; 2017-01-20.
    <quote> The authors tested their theory by recruiting 400 people who allowed their Web browsing histories to be tracked, and then comparing the sites they visited to sites mentioned in Twitter accounts they followed. The researchers say they were able to use that method to identify more than 70% of the volunteers.</quote>


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