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Digital traces left by users of online social networking services, even after anonymization, are susceptible to privacy breaches. This is exacerbated by the increasing overlap in user-bases among various services. To alert fellow researchers in both the academia and the industry to the feasibility of such an attack, we propose an algorithm, Seed-and-Grow, to identify users from an anonymized social...
In the real world, companies would publish social networks to a third party, e.g., a cloud service provider, for marketing reasons. Preserving privacy when publishing social network data becomes an important issue. In this paper, we identify a novel type of privacy attack, termed 1∗-neighborhood attack. We assume that an attacker has knowledge about the degrees of a target's one-hop neighbors, in...
Digital traces left by a user of an online social networking service can be abused by a malicious party to compromise the person's privacy. This is exacerbated by the increasing overlap in user-bases among various services. In this paper, we propose an algorithm, Seed and Grow, to identify users from an anonymized social graph based solely on graph structure. The algorithm first identifies a seed...
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