Online Social Networks have redefined the way people interact with existing friends, and more importantly, make new friends. In particular, people can now explore potential friendships via OSNs, by looking for common interests, friends, and symptoms, close geographic proximity, etc., between each other. In this abstract, 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.
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