User self-controllable profile matching for privacy-preserving mobile social networks

Personal profiles usually contain sensitive information of people, while the emerging requirement of profile matching in mobile social networks may occasionally leak the sensitive information and hence violate people´ privacy. In this paper we propose a user self-controllable profile matching protocol in privacy-preserving mobile social networks. By using our protocol, users can customize the matching metrics to involve their own matching preference and to make the matching results more precise. In addition, detailed security analysis demonstrates that our protocol can protect the privacy of both users´ profile item names and profile item values during the matching process. Moreover, extensive performance evaluation are conducted to illustrate that our protocol is more efficient than a relevant protocol in terms of computation and communication overhead, especially when the maximum value of profile item is large.