@Article{cmc.2020.09815,
AUTHOR = {Wenxi Han, Mingzhi Cheng, Min Lei, Hanwen Xu, Yu Yang, Lei Qian},
TITLE = {Privacy Protection Algorithm for the Internet of Vehicles Based on Local Differential Privacy and Game Model},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {64},
YEAR = {2020},
NUMBER = {2},
PAGES = {1025--1038},
URL = {http://www.techscience.com/cmc/v64n2/39343},
ISSN = {1546-2226},
ABSTRACT = {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 
<i>k</i>-location set while maximizing the availability of the service. On this basis, a statistical 
method is designed, which satisfies the local differential privacy of <i>k</i>-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.},
DOI = {10.32604/cmc.2020.09815}
}