PUMTD: Privacy-preserving user-profile matching protocol in social networks

Abstract

User profile matching can establish social relationships between different users in the social network. If the user profile is matched in plaintext, the user's privacy might face a security challenge. Although there exist some schemes realizing privacy-preserving user profile matching, the resource-limited users or social service providers in these schemes need to take higher computational complexity to ensure the privacy or matching of the data. To overcome the problems, a novel privacy-preserving user profile matching protocol in social networks is proposed by using t-out-of n servers and the bloom filter technique, in which the computational complexity of a user is reduced by applying the Chinese Remainder Theorem, the matching users can be found with the help of any t matching servers, and the privacy of the user profile is not compromised. Furthermore, if at most t − 1 servers are allowed to collude, our scheme can still fulfill user profile privacy and user query privacy. Finally, the performance of the proposed scheme is compared with the other two schemes, and the results show that our scheme is superior to them.