Combining blockchain and crowd-sensing for location privacy protection in Internet of vehicles

Abstract

With the rapid development of Internet of Vehicles (IoV), crowd-sensing based on IoV is widely used in various fields. Traditional crowd-sensing uses a third-party service platform for information interaction, which has problems of worker location privacy leakage and imbalanced participation task fairness. To solve these problems, this paper proposes a combination of blockchain and crowd-sensing for location privacy protection (BCS-LPP) method in IoV. First, blockchain is introduced into BCS-LPP to prevent the leakage of user information by third-party service platforms. Second, providing workers with personalized location privacy level options, combined with Geohash encoding and order-preserving encryption to safeguard the confidentiality of workers' location privacy information. Finally, the fairness of worker participation in tasks and the quality of sensing data are guaranteed by verifying the sensing locations submitted by workers. Using real datasets, BCS-LPP is compared with existing schemes through experimental simulation. BCS-LPP can better ensure the quality of sensing data, protect workers' location privacy information, and enhance the fairness of user participation in tasks.