Achieving secure friend discovery in social strength-aware PMSNs

With Proximity-based Mobile Social Networks (PMSNs), mobile users can discover and make new social interactions easily with physical-proximate others through WiFi/Bluetooth interfaces embedded in the smartphones. Unfortunately, users enjoy these conveniences at the cost of revealing their personal data. Furthermore, an important factor called social strength, which impacts the discovering result a lot, is always ignored. To address these problems, we propose S-match, which aims to achieve secure friend discovery in social strength-aware PMSNs. Specifically, S-match exploits a two-dimensional similarity vector with considering both priority-aware similarity coefficient and social strength-aware similarity coefficient. We construct a similarity evaluation model based on entropy method to measure profile similarity, as well as a novel priority-aware similarity coefficient by improving Jaccard similarity function. Finally, the combination of the homomorphic property and the priority-aware similarity coefficient guarantee the avoidance on the insider attacks. The security and performance are thoroughly analyzed and evaluated via detailed simulations.