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Social networking sites such as Facebook, LinkedIn, and Xing have been reporting exponential growth rates and have millions of registered users. In this paper, the authors introduce a novel de-anonymization attack that exploits group membership information that is available on social networking sites. More precisely, they show that information about the group memberships of a user (i.e., the groups of a social network to which a user belongs) is sufficient to uniquely identify this person, or, at least, to significantly reduce the set of possible candidates. That is, rather than tracking a user's browser as with cookies, it is possible to track a person. To determine the group membership of a user, they leverage well-known web browser history stealing attacks.
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