De-anonymizing Social Networks
Our social networks paper is finally officially out! It will be appearing at this year’s IEEE S&P (Oakland).
Please read the FAQ about the paper.
Operators of online social networks are increasingly sharing potentially sensitive information about users and their relationships with advertisers, application developers, and data-mining researchers. Privacy is typically protected by anonymization, i.e., removing names, addresses, etc.
We present a framework for analyzing privacy and anonymity in social networks and develop a new re-identification algorithm targeting anonymized social-network graphs. To demonstrate its effectiveness on real-world networks, we show that a third of the users who can be verified to have accounts on both Twitter, a popular microblogging service, and Flickr, an online photo-sharing site, can be re-identified in the anonymous Twitter graph with only a 12% error rate.
Our de-anonymization algorithm is based purely on the network topology, does not require creation of a large number of dummy “sybil” nodes, is robust to noise and all existing defenses, and works even when the overlap between the target network and the adversary’s auxiliary information is small.
The HTML version was produced using my Project Luther software, which in my opinion produces much prettier output than anything else (especially math formulas). Another big benefit is the handling of citations: it automatically searches various bibliographic databases and adds abstract/bibtex/download links and even finds and adds links to author homepages in the bib entries.
I have never formally announced or released Luther; it needs more work before it can be generally usable, and my time is limited. Drop me a line if you’re interested in using it.