Blockchain-Based Data Management and Edge-Assisted Trusted Cloaking Area Construction for Location Privacy Protection in Vehicular Networks

With the tremendous pervasion of location-based services (LBSs) in vehicular networks, the location privacy of vehicles has become an utmost concern. K-anonymity is one of the most popular privacy protection solutions, in which the real location of the request vehicle (RV) can be covered by a cloaking area including the location of k-1 cooperative vehicles (CVs). However, K-anonymity assumes all CVs are always honest and thus offering opportunities for dishonest CVs to provide false location information. To combat such threats, we propose a trusted cloaking area construction (TCAC) scheme based on the trust mechanism to protect the location privacy of vehicles. In this article, the trust value is not only used to identify dishonest CVs, but also utilized to decide the LBS request of RV. A low trust value will make the LBS request be rejected due to dishonest and selfish cooperative behaviors when the RV has played the role of CV. To deal with the massive trust requirements caused by frequent vehicle movement, edge computing is employed to assist trust value evaluation. Moreover, traditional central and distributed trust data management may be unsuitable for vehicular networks. We also propose a blockchain-based trust data management method by combining vehicular regions partition, so as to rapidly evaluate trust value during the cloaking area construction. The security analysis and simulation results indicate that our proposed scheme is resilient to suppress dishonest CVs, inspire selfish CVs, and protect the location privacy of vehicles effectively, whereas the required computation time and communication cost are both limited.