Privacy-Preserving Sketching for Online Social Network Data Publication

Releasing private data can cause panic to both Online Social Network (OSN) users and service providers. Therefore, anonymization mechanisms are proposed to protect data before sharing it. However, some of these mechanisms set unrealistic privacy demands but cannot defend against real-world de-anonymization attacks.In this paper, we introduce an anonymization algorithm based on All-Distance Sketch (ADS). Sketching can significantly limit attackers’ confidence, as well as provide accurate estimation about shortest path length and other utility metrics. Because sketching removes large amounts of edges, it is invulnerable to seed-based and subgraph-based de-anonymization attacks. However, existing sketching algorithms do not add dummy edges and paths. Adversaries have low false positive in extracting linking information, which challenges the privacy performance. We propose the novel bottom-(l, k) sketch to defend against these advanced attacks. We develop a scheme to add and delete enough edges to satisfy our privacy demand. The experiment results show that our published graphs are closely matched with the original graphs under some metrics, preserving utility, while 80% edges are removed, ensuring privacy.