Empirical Comparisons of Attack and Protection Algorithms for Online Social Networks

Abstract

Online social networks, like Facebook, are popular social networking websites, on which hundreds of millions of users make friends and interact with people. There is a large amount of personal information in these networking websites and their security is rather concerned by both users and researchers, because valuable private information will bring great profit to some people or groups. In the real world, profits motivate people and groups to obtain the personal private data lawlessly and many attacks are launched on the social networks. Facing various attacks, distinct protective strategies are proposed by researches to reduce the negative effect of attacks. However, the practical performance of protections is unknown when they are battling with the real attacks. Moreover, we also understand little about how strong attacks would be when they are facing protections. Therefore, this paper proposes an Attack-Protect-Attack (APA) comparison scheme to explore the performance and bias of various attack algorithms and protective strategies for online social networks. By this way, the comparison results are valuable and meaningful for further protection of private information. We apply several attacking and protective approaches on a real-world dataset from Facebook, then evaluate them by the accuracy of attack algorithms. Following the comparison scheme, the experiments demonstrate that the performance of protective strategies is not satisfactory in the complex and practical case.