Social networking sites such as Facebook, LinkedIn, and Xing have been reporting exponential growth rates. These sites have millions of registered users, and they are interesting from a security and privacy point of view because they store large amounts of sensitive personal user data. In this paper, we introduce a novel de-anonymization attack that exploits group membership information that is available on social networking sites. More precisely, we show that information about the group memberships of a user (i.e., the groups of a social network to which a user belongs) is often sufficient to uniquely identify this user, or, at least, to significantly reduce the set of possible candidates. To determine the group membership of a user, we leverage well-known web browser history stealing attacks. Thus, whenever a social network user visits a malicious website, this website can launch our de-anonymization attack and learn the identity of its visitors.

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