Privacy Protection Algorithm for the Internet of Vehicles Based on Local Differential Privacy and Game Model

In recent years, with the continuous advancement of the intelligent process of the Internet of Vehicles (IoV), the problem of privacy leakage in IoV has become increasingly prominent. The research on the privacy protection of the IoV has become the focus of the society. This paper analyzes the advantages and disadvantages of the existing location privacy protection system structure and algorithms, proposes a privacy protection system structure based on untrusted data collection server, and designs a vehicle location acquisition algorithm based on a local differential privacy and game model. The algorithm first meshes the road network space. Then, the dynamic game model is introduced into the game user location privacy protection model and the attacker location semantic inference model, thereby minimizing the possibility of exposing the regional semantic privacy of the k-location set while maximizing the availability of the service. On this basis, a statistical method is designed, which satisfies the local differential privacy of k-location sets and obtains unbiased estimation of traffic density in different regions. Finally, this paper verifies the algorithm based on the data set of mobile vehicles in Shanghai. The experimental results show that the algorithm can guarantee the user’s location privacy and location semantic privacy while satisfying the service quality requirements, and provide better privacy protection and service for the users of the IoV.