In OSNs and Mobile Social Networks (MSNs), many distributed solutions to privately finding the social proximity between two users have been proposed in this abstract. The most common way of determining friendship between two people is through profile matching, i.e. finding out if they have common profile attributes, like interests, symptoms, or some other social coordinates. In some cases, the number of common friends also serves as the proximity measure between two users. Such previous works employ various cryptographic tools to protect the privacy of the profile information of the users in the private matching process. 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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