SPFM : Scalable and Privacy-Preserving Friend Matching in Cloud

Profile data, interest, and mobility matching is more than important for fostering the wide use of social networks. The social networks such as Facebook, Line, or WeChat recommend the friends for the users based on users personal data such as common contact list or mobility traces. Here, users' personal information to the database for friend matching will raise a serious privacy concern due to the potential risk of data abusing. In this paper, we propose a novel scalable and privacy-preserving friend matching protocol, which aims to provide a scalable friend matching and recommendation solutions without revealing the users personal data to the cloud. The various from the previous works which have multiple number of protocols, it presents a scalable solution which can prevent honest and curious cloud from obtaining the original data and support the friend matching of multiple users simultaneously. The detailed feasibility and security analysis on it and its accuracy and security have been well demonstrated via extensive simulations. The result show that our system works even better when original data is large.