New Developments in Deanonymization

December 17, 2012 at 8:59 am 1 comment

This post is a roundup of developments in deanonymization in the last few months. Let’s start with two stories relating to how a malicious website can silently discover the identity of a visitor, which is an insidious type of privacy breach that I’ve written about quite a bit (1, 2, 3, 4, 5, 6).

Firefox bug exposed your identity. The first is a vulnerability resulting from a Firefox bug in the implementation of functions like exec and test. The bug allows a website to learn the URL of an embedded iframe from some other domain. How can this lead to uncovering the visitor’s identity? Because redirects to<username>/lists. This allows a malicious website to open a hidden iframe pointing to, query the URL after redirection, and learn the visitor’s Twitter handle (if they are logged in). [1,2]

This is very similar to a previous bug in Firefox that led to the same type of vulnerability. The URL redirect that was exploited there was  → user-specific URL. It would be interesting to find and document all such generic-URL → user-specific-URL redirects in major websites. I have a feeling this won’t be the last time such redirection will be exploited.

Visitor deanonymization in the wild. The second story is an example of visitor deanonymization happening in the wild. It appears that the technique utilizes a tracking cookie from a third-party domain to which the visitor previously gave their email and other info., in other words, #3 in my five-fold categorization of ways in which identity can be attached to browsing logs.

I don’t consider this instance to be particularly significant — I’m sure there are other implementations in the wild — and it’s not technically novel, but this is the first time as far as I know that it’s gotten significant attention from the public, even if only in tech circles. I see this as a first step in a feedback loop of changing expectations about online anonymity emboldening more sites to deanonymize visitors, thus further lowering the expectation of privacy.

Deanonymization of mobility traces. Let’s move on to the more traditional scenario of deanonymization of a dataset by combining it with an auxiliary, public dataset which has users’ identities. Srivatsa and Hicks have a new paper with demonstrations of deanonymization of mobility traces, i.e., logs of users’ locations over time. They use public social networks as auxiliary information, based on the insight that pairs of people who are friends are more likely to meet with each other physically. The deanonymization of Bluetooth contact traces of attendees of a conference based on their DBLP co-authorship graph is cute.

This paper adds to the growing body of evidence that anonymization of location traces can be reversed, even if the data is obfuscated by introducing errors (noise).

So many datasets, so little time. Speaking of mobility traces, Jason Baldridge points me to a dataset containing mobility traces (among other things) of 5 million “anonymous” users in the Ivory Coast recently released by telecom operator Orange. A 250-word research proposal is required to get access to the data, which is much better from a privacy perspective than a 1-click download. It introduces some accountability without making it too onerous to get the data.

In general, the incentive for computer science researchers to perform practical demonstrations of deanonymization has diminished drastically. Our goal has always been to showcase new techniques and improve our understanding of what’s possible, and not to name and shame. Even if the Orange dataset were more easily downloadable, I would think that the incentive for deanonymization researchers would be low, now that the Srivatsa and Hicks paper exists and we know for sure that mobility traces can be deanonymized, even though the experiments in the paper are on a far smaller scale.

Head in the sand: rational?! I gave a talk at a privacy workshop recently taking a look back at how companies have reacted to deanonymization research. My main point was that there’s a split between the take-your-data-and-go-home approach (not releasing data because of privacy concerns) and the head-in-the-sand approach (pretending the problem doesn’t exist). Unfortunately but perhaps unsurprisingly, there has been very little willingness to take a middle ground, engaging with data privacy researchers and trying to adopt technically sophisticated solutions.

Interestingly, head-in-the-sand might be rational from companies’ point of view. On the one hand, researchers don’t have the incentive for deanonymization anymore. On the other hand, if malicious entities do it, naturally they won’t talk about it in public, so there will be no PR fallout. Regulators have not been very aggressive in investigating anonymized data releases in the absence of a public outcry, so that may be a negligible risk.

Some have questioned whether deanonymization in the wild is actually happening. I think it’s a bit silly to assume that it isn’t, given the economic incentives. Of course, I can’t prove this and probably never can. No company doing it will publicly talk about it, and the privacy harms are so indirect that tying them to a specific data release is next to impossible. I can only offer anecdotes to explain my position: I have been approached multiple times by organizations who wanted me to deanonymize a database they’d acquired, and I’ve had friends in different industries mention casually that what they do on a daily basis to combine different databases together is essentially deanonymization.

[1] For a discussion of why a social network profile is essentially equivalent to an identity, see here and the epilog here.
[2] Mozilla pulled Firefox 16 as a result and quickly fixed the bug.

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1 Comment Add your own

  • 1. Álvaro Del Hoyotochowsky  |  April 28, 2013 at 10:04 pm

    Hi, Arvind

    It is a long time since I sent my last comment/question :-)

    Now that all major telco operators are starting to sell our profiles jointly with location data anonymized and aggregated I was wondering if you have research a bit on the issue

    Any idea about anonymization combined with aggregation approaches they are using?

    Dynamic or persistent data masking?

    What can you tell us about data masking techniques are they really efficient?

    Thank you so much for all your posts. Keep on


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I'm an assistant professor of computer science at Princeton. I research (and teach) information privacy and security, and moonlight in technology policy.

This is a blog about my research on breaking data anonymization, and more broadly about information privacy, law and policy.

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