Making new connections according to personal preferences is a crucial service in mobile social networking, where an initiating user can find matching users within physical proximity of him/her. In existing systems for such services, usually all the users directly publish their complete profiles for others to search. However, in many applications, the users’ personal profiles may contain sensitive information that they do not want to make public. In this paper, we propose FindU, a set of privacy-preserving profile matching schemes for proximity-based mobile social networks. In FindU, an initiating user can find from a group of users the one whose profile best matches with his/her; to limit the risk of privacy exposure, only necessary and minimal information about the private attributes of the participating users is exchanged. Two increasing levels of user privacy are defined, with decreasing amounts of revealed profile information. Leveraging secure multi-party computation (SMC) techniques, we propose novel protocols that realize each of the user privacy levels, which can also be personalized by the users. We provide formal security proofs and performance evaluation on our schemes, and show their advantages in both security and efficiency over state-of-the-art schemes. Privacy-Preserving Distributed Profile Matching in Proximity-Based Mobile Social Networks
In existing systems for such services, usually all the users directly publish their complete profiles for others to search. However, in many applications, the users’ personal profiles may contain sensitive information that they do not want to make public.
In this paper, we overcome the above challenges and make the following main contributions.
(1) We formulate the privacy preservation problem of profile matching in MSN. Two levels of privacy are defined along with their threat models, where the higher privacy level leaks less profile information to the adversary than the lower level.
(2) We propose two fully distributed privacy-preserving profile matching schemes, one of them being a private setintersection protocol and the other is a private cardinality of set-intersection protocol. However, solutions based on existing PSI schemes are far from efficient. We leverage secure multi-party computation based on polynomial secret sharing, and propose several key enhancements to improve the computation and communication efficiency.