Efficient Weight-based Private Matching for proximity-based mobile social networks

Proximity-based mobile social networks (PMSNs) are becoming increasingly popular in recent years with the explosive growth of mobile devices, where a user can find a best matching friend in vicinity through profile matching. However, the matching process calls for the publication of users´ personal information, which conflicts with users´ growing privacy concerns about revealing their profiles to strangers. To achieve privacy-preserving friend discovery, many schemes are proposed based on traditional cryptographic methods, such as homomorphic and commutative encryption, which inevitably introduce tremendous overhead to the system and are not practical for the resource-limited mobile devices. In this paper, we propose an Efficient Weight-based Private Matching (EWPM) protocol, which provides a realistic matching approach considering both the number of common interests and the corresponding weights on them. In EWPM, we utilize the Confusion Matrix Transformation (CMT) algorithm to achieve a secure and efficient matching. Security analysis and detailed simulations show that our proposed scheme can realize privacy-preserving friend discovery with higher efficiency.