Open Access
ARTICLE
Privacy Protection Algorithm for the Internet of Vehicles Based on Local Differential Privacy and Game Model
Wenxi Han1, 2, Mingzhi Cheng3, *, Min Lei1, 2, Hanwen Xu2, Yu Yang1, 2, Lei Qian4
1 Guizhou Provincial Key Laboratory of Public Big Data, Guizhou University, Guiyang, 550025, China.
2 School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing, 100876, China.
3 College of New Media, Beijing Institute of Graphic Communication, Beijing, 102600, China.
4 School of Computer Science, The University of Auckland, Auckland, New Zealand.
* Corresponding Author: Mingzhi Cheng. Email: .
Computers, Materials & Continua 2020, 64(2), 1025-1038. https://doi.org/10.32604/cmc.2020.09815
Received 20 January 2020; Accepted 09 March 2020; Issue published 10 June 2020
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
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.
Keywords
Cite This Article
W. Han, M. Cheng, M. Lei, H. Xu, Y. Yang
et al., "Privacy protection algorithm for the internet of vehicles based on local differential privacy and game model,"
Computers, Materials & Continua, vol. 64, no.2, pp. 1025–1038, 2020. https://doi.org/10.32604/cmc.2020.09815
Citations