Two-Stage Privacy-Preserving Mechanism for a Crowdsensing-Based VSN

Abstract

Vehicular social networks (VSNs) are moving from infancy to maturity. However, the characteristics of wireless channel availability and the high mobility of vehicles in the VSNs create new challenges for privacy preservation; the corresponding solutions require a combination of technologies. A common theme in this paper is the combination of crowdsensing (CS) and the VSNs. We first design a three-layer CS-based VSN architecture that includes a CS layer, mobile management layer, and core control layer. Then, we propose a two-stage privacy-preserving (TSPP) algorithm to protect identity and location privacy for the CS-based VSN. In TSPP algorithm, to protect identity privacy, we design a way of identity management. Simultaneously, by correlation analysis, we design a suppression strategy to satisfy $\varepsilon $ -privacy with no information loss for location privacy protection. The simulation results show that our proposed TSPP algorithm better protects identity and location privacy than current algorithms. The results are based on four metrics: average processing time, identity privacy, location privacy, and information loss.