Privacy-Preserving Spatiotemporal Matching for Secure Device-to-Device Communications
Jingchao Sun, Rui Zhang, Yanchao Zhang · IEEE Internet of Things Journal · 2016
Device-to-device (D2D) communications are emerging due to the explosive growth of smartphones and tablets. Given the possible presence of attackers, a fundamental challenge in secure D2D communications is to develop sound mobile authentication techniques whereby mobile users can select the most trustworthy D2D communication partners from possibly many candidates. This paper tackles this open challenge and proposes spatiotemporal matching as a promising enabler for secure D2D communications. Spatiotemporal matching is built upon the location-aware capability of D2D devices. In particular, a mobile user could very easily maintain his spatiotemporal profile recording his continuous whereabouts in time, and the level of his spatiotemporal profile matching that of the other user can be translated into the level of trust they two can have in each other. Since spatiotemporal profiles contain very sensitive personal information, privacy-preserving spatiotemporal matching is needed to ensure that as little information as possible about the spatiotemporal profile of either matching participant is disclosed beyond the matching result. Toward this end, we propose two novel privacy-preserving spatiotemporal matching protocols, which are thoroughly analyzed and evaluated through detailed simulation studies driven by experimental data.