Security and privacy preservation in fog-based crowd sensing on the internet of vehicles

Abstract

The emergence of fog computing enables fog-based vehicle crowd sensing (FBVC) to be utilized in various fields. However, existing privacy issues represent a primary challenge that limits the degree of participation by vehicles. To meet the demands of both data providers and users for privacy preservation and data validity, our work introduces a means for smart vehicles to partake in data crowd sensing while maintaining security and privacy, which includes privacy preservation, data aggregation, and traceability in a proposed data collection approach based on a heterogeneous two-tier fog architecture. These are three properties that prior attempts cannot all achieve. Moreover, a new scheme for trust authority (TA) security queries in fog computing to obtain outsourced encrypted map lists (MPLs) of the participants to achieve online traceability and identity retrieval for malicious participants is proposed in our study, which can reduce the storage burden of TA. Finally, the simulation results demonstrate the efficiency of our approach both in computation and communication.