Asymmetric Social Proximity Based Private Matching Protocols for Online Social Networks

The explosive growth of Online Social Networks (OSNs) over the past few years has redefined the way people interact with existing friends and especially make new friends. Some works propose to let people become friends if they have similar profile attributes. However, profile matching involves an inherent privacy risk of exposing private profile information to strangers in the cyberspace. The existing solutions to the problem attempt to protect users´ privacy by privately computing the intersection or intersection cardinality of the profile attribute sets of two users. These schemes have some limitations and can still reveal users´ privacy. In this paper, we leverage community structures to redefine the OSN model and propose a realistic asymmetric social proximity measure between two users. Then, based on the proposed asymmetric social proximity, we design three private matching protocols, which provide different privacy levels and can protect users´ privacy better than the previous works. We also analyze the computation and communication cost of these protocols. Finally, we validate our proposed asymmetric proximity measure using real social network data and conduct extensive simulations to evaluate the performance of the proposed protocols in terms of computation cost, communication cost, total running time, and energy consumption. The results show the efficacy of our proposed proximity measure and better performance of our protocols over the state-of-the-art protocols.