Privacy-Preserving Distributed Profile Matching in Proximity- based Mobile Social Networks

The Profile matching means that 2 users scrutiny their personal profiles and is usually the primary step towards effective PMSN. It, however, conflicts with users’ growing privacy issues regarding revealing their personal profiles to finish strangers before deciding to move with them Our protocols modify 2 users to perform profile matching while not revealing any data regarding their profiles on the far side the comparison resul creating new connections in keeping with personal preferences could be a crucial service in mobile social networking, wherever associate initiating user will notice matching users inside physical proximity of him/her. In existing systems for such services, sometimes all the users directly publish their complete profiles for others to go looking. However, in several applications, the users’ personal profiles might contain sensitive data that they are doing not wish to form public. during this paper, we have a tendency to propose FindU, a group of privacy-preserving profile matching schemes for proximitybased mobile social networks. In FindU, associate initiating user will notice from a bunch of users the one whose profile best matches with his/her; to limit the chance of privacy exposure, solely necessary and stripped-down data regarding the personal attributes of the taking part users is changed. 2 increasing levels of user privacy ar outlined, with decreasing amounts of discovered profile data. investing secure multi-party computation (SMC) techniques, we have a tendency to propose novel protocols that understand every of the user privacy levels, which may even be personalised by the users. we offer formal security proofs and performance analysis on our schemes, and show their benefits in each security and potency over progressive schemes. The social proximity between 2 users because the matching metric, that measures the space between their social coordinates with every being a vector precomputed by a trusty central server to represent the situation of a user in a web social network. By comparison, our work doesn't deem the affiliation of PMSN users with one on-line social network and addresses a additional general personal matching drawback for PMSN by supports fine-grained personal profiles and a large spectrum of matching metrics