SafeMatch: Energy-efficient private matching in mobile social networks
Yi-Hui Lin, Wen-Tsuen Chen, Wen-Chan Shih · 2015
This paper proposes an energy-efficient private matching for secure two-party computation, called SafeMatch, in mobile social networks (MSNs). The proposed SafeMatch utilizes locality sensitive hashing (LSH) to sample the social data and protects social samples with cryptographic hash functions and one-time padding. Given acceptable tolerance rate and error rate, our proposed SafeMatch provides lightweight computation cost regardless the size of social data set. To guarantee the soundness of the proposed SafeMatch, we define its privacy and prove its security. Empirical experiment results on smart phones show that SafeMatch achieves orders of magnitude improvement in energy consumption compared to the homomorphic encryption based protocol.