Seed and Grow: An attack against anonymized social networks

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 sub-graph, either planted by an attacker or divulged by collusion of a small group of users, and then grows the seed larger based on the attacker´s existing knowledge of the users´ social relations. Our work identifies and relaxes implicit assumptions taken by previous works, eliminates arbitrary parameters, and improves identification effectiveness and accuracy. Experiments on real-world collected datasets further corroborate our expectation and claim.