An Evolutionary Game Theory-Based Cooperation Framework for Countering Privacy Inference Attacks

Privacy inference poses a significant threat to users of online social networks (OSNs). To deal with this issue, a number of privacy-enhancing technologies have been proposed with the goal of achieving a balance between the protection of privacy and the utility of data. Previous studies, however, failed to take into consideration the impact of the interdependency of privacy (IoP), which dictates that privacy decisions made by some users may affect the privacy of some other users. The implication of IoP is that too much privacy may be disclosed when multiple individuals share data with the same data accessor because privacy conflicts resulting from independent privacy decisions would make it possible for adversaries to infer the privacy of the target user. Ideally, cooperation that preserves privacy should allow OSN users to respect each other's privacy specifications so as to resolve such privacy conflicts caused by independent privacy decisions of individuals. To facilitate the design, we propose a privacy-preserving cooperation framework based on the evolutionary game theory to facilitate such cooperation. Based on the framework, the dynamics of user strategies regarding whether to participate in the cooperation are analyzed and an evolutionary stable state is derived to serve as the basis for incentivizing users to participate in cooperative privacy protection. Experiments based on real OSN data show that the proposed cooperation framework is effective in modeling the behaviors of users and that the proposed incentive allocation method can incentivize users to participate in the cooperation. The proposed cooperation framework can not only helps lower the threat to user privacy resulting from privacy inference by data accessors but also allows OSN service providers to design effective privacy protection policies.