Differential Privacy Protection and Game Analysis of Intelligent Transportation Data

Abstract

With the informatization and intelligentization of the Internet of Vehicle (IoV), location-based services have brought great convenience to users, but it has also brought about the security problem of privacy exposure. Vehicle users hope that their private data will be protected and at the same time they will be able to obtain good road service quality. In response to the above problems, this paper proposes a location privacy protection method based on local differential privacy. Firstly, we use the Voronoi diagram to divide and number the road network space; then, we perform K-RR random perturbation on the user's location data to make the result meet the local differential privacy. Thirdly, we set up an incentive mechanism, build a game model, and define the user's payment matrix under the verification strategy and the server's cheating strategy is analyzed, and the user's expected revenue is analyzed. The experimental results show that the user's benefit first decreases and then increases with the increase of the privacy protection parameters. The benefit is linear, and the road condition service quality has a relatively large impact on the benefit of users' shared location.