PROJECT TITLE :

Asymmetric Social Proximity Based Private Matching Protocols for Online Social Networks

ABSTRACT:

The explosive growth of Online Social Networks (OSNs) over the past few years has redefined the way individuals interact with existing friends and particularly build new friends. Some works propose to let folks become friends if they need similar profile attributes. However, profile matching involves an inherent privacy risk of exposing personal profile info to strangers within the cyberspace. The existing solutions to the problem attempt to guard users’ privacy by privately computing the intersection or intersection cardinality of the profile attribute sets of 2 users. These schemes have some limitations and will still reveal users’ privacy. In this paper, we leverage community structures to redefine the OSN model and propose a practical asymmetric social proximity live between two users. Then, based on the proposed asymmetric social proximity, we have a tendency to design 3 private matching protocols, which offer totally different privacy levels and can defend users’ privacy better than the previous works. We tend to additionally analyze the computation and Communication price of those protocols. Finally, we validate our proposed asymmetric proximity live using real social network knowledge and conduct in depth simulations to judge 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 live and better performance of our protocols over the state-of-the-art protocols.