SFPM: A Secure and Fine-Grained Privacy-Preserving Matching Protocol for Mobile Social Networking

Abstract

In emerging big data era, mobile social networking (MSN) is an important data source, which provides an attractive proximity-based communication platform for mobile users with similar interests, attributes, or background to communicate with each other. In this kind of proximity-based MSN, profile matching protocol, which enables a mobile user to break the ice and start a conversation with someone attractive, is one of important components for its success. However, profile matching may occasionally leak the sensitive information, hence privacy concerns often hinder users from enabling this functionality. Aiming at this problem, in this paper, we present a new secure and fine-grained privacy-preserving matching protocol, called SFPM. Differently from those previously reported private profile matching schemes, our proposed SFPM can fine-grainedly differentiate users with the same value of matching metrics by two phases of profile matching. In addition to the personal privacy preservation through secure and efficient cryptographic algorithm, SFPM also achieves the flexibility of profiles changing at the same time. Extensive performance evaluations via smartphones with android system are conducted, and experimental results demonstrate the effectiveness of the SFPM protocol.

Keywords: Big data - Fine-grained - Mobile social network - Privacy preservation - Profile matching - Proximity-based