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.

Abstract

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 effectiveness 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.

Promotions

  • 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>

Shining the Floodlights on Mobile Web Tracking — A Privacy Survey | Eubank, Melara, Perez-Botero, Narayanan

Christian Eubank, Marcela Melara, Diego Perez-Botero, Arvind Narayanan; Shining the Floodlights on Mobile Web Tracking — A Privacy Survey; In Proceedings of Web 2.0 Security & Privacy (W2SP); 2013-05-24; 9 pages.

Abstract

We present the first published large-scale study of mobile web tracking. We compare tracking across five physical and emulated mobile devices with one desktop device as a benchmark. Our crawler is based on FourthParty; however, our architecture avoids clearing state which has the benefit of continual observation of (and by) third-parties. We confirm many intuitive predictions and report a few surprises. The lists of top third-party domains across different categories devices are substantially similar; we found surprisingly few mobile-specific ad networks. The use of JavaScript by tracking domains increases gradually as we consider more powerful devices. We also analyze cookie longevity by device. Finally, we analyze a curious phenomenon of cookies that are used to store information about the user’s browsing history on the client. Mobile tracking appears to be an under-researched area, and this paper is only a first step. We have made our code and data available at http://webtransparency.org/ for others to build on.