The walls have ears: optimize sharing for visibility and privacy in online social networks

With a rapid expansion of online social networks (OSNs), millions of users are tweeting and sharing their personal status daily without being aware of where that information eventually travels to. Likewise, with a huge magnitude of data available on OSNs, it poses a substantial challenge to track how a piece of information leaks to specific targets. In this paper, we study the problem of smartly sharing information to control the propagation of sensitive information in OSNs. In particular, we formulate and investigate the Maximum Circle of Trust problem of which we seek to construct a circle of trust on the fly so that OSN users can safely share their information knowing that it will not be propagated to their unwanted targets (whom they are not willing to share with). Since most of messages in OSNs are propagated within 2 to 5 hops, we first investigate this problem under 2-hop information propagation by showing the hardness of obtaining an optimal solution, along with an algorithm with proven performance guarantee. In a general case where information can be propagated more than two hops, the problem is #P-hard i.e. the problem cannot be solved in a polynomial time. Thus we propose a novel greedy algorithm, hybridizing the handy but costly sampling method with a novel cut-based estimation. The quality of the hybrid algorithm is comparable to that of the sampling method while taking only a tiny fraction of the time. We have validated the effectiveness of our solutions in many real-world traces. Such an extensive experiment also highlights several important observations on information leakage which help to sharpen the security of OSNs in the future.