Disclose More and Risk Less: Privacy Preserving Online Social Network Data Sharing

Abstract

Many third-party services and applications have integrated the login services of popular Online Social Networks, such as Facebook and Google+, and acquired user information to enrich their services by requesting user's permission. Although users can control the information disclosed to the third parties in a certain granularity, there are still serious privacy risks due to the inference attack. Even if users conceal their sensitive information, attackers can infer their secrets by exploiting the correlations among private and public information with background knowledge. To defend against such attacks, we formulate the social network data sharing problem through an optimization-based approach, which maximizes the users’ self-disclosure utility while preserving their privacy. We propose two privacy-preserving social network data sharing methods to counter the inference attack. One is the efficiency-based privacy-preserving disclosure algorithm (EPPD) targeting the high utility, and the other is to convert the original problem into a multi-dimensional knapsack problem (d-KP) using greedy heuristics with a low computational complexity. We use real-world social network datasets to evaluate the performance. From the results, the proposed methods achieve a better performance when compared with the existing ones.