Outsourcing privacy-preserving social networks to a cloud

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 addition to the target's 1-neighborhood graph, which consists of the one-hop neighbors of the target and the relationships among these neighbors. With this information, an attacker may re-identify the target from a k-anonymity social network with a probability higher than 1/k, where any node's 1-neighborhood graph is isomorphic with k-1 other nodes' graphs. To resist the 1∗-neighborhood attack, we define a key privacy property, probability indistinguishability, for an outsourced social network, and propose a heuristic indistinguishable group anonymization (HIGA) scheme to generate an anonymized social network with this privacy property. The empirical study indicates that the anonymized social networks can still be used to answer aggregate queries with high accuracy.