Poster: Perturbation Based Private Profile Matching in Social Networks

Social networking has become part of our life in recent years, allowing users to converse and connect with people sharing similar interests in real world. However, networking via the social media suffers from serious privacy issues, and one of which is profile attribute leakage in friend discovery. While existing studies mainly focus on leveraging rich cryptographic algorithms to prevent privacy leak, we propose a novel perturbation based private profile matching mechanism by mixing the private data with random noise to preserve privacy in this paper. As no expensive encryption algorithms get involved, our methods are computationally efficient; thus they are more practical for real-world applications.