Towards privacy-preserving spatial distribution crowdsensing: A game theoretic approach
Publication Type
Journal Article
Publication Date
2-2022
Abstract
Acquiring the spatial distribution of users in mobile crowdsensing (MCS) brings many benefits to users (e.g., avoiding crowded areas during the COVID-19 pandemic). Although the leakage of users' location privacy has received a lot of research attention, existing works still ignore the rationality of users, resulting that users may not obtain satisfactory spatial distribution even if they provide true location information. To solve the problem, we employ game theory with incomplete information to model the interactions among users and seek an equilibrium state through learning approaches of the game. Specifically, we first model the service as a game in the satisfaction form and define the equilibrium for this service. Then, we design a LEFS algorithm for the privacy strategy learning of users when their satisfaction expectations are fixed, and further design LSRE that allows users to have dynamic satisfaction expectations. We theoretically analyze the convergence conditions and characteristics of the proposed algorithms, along with the privacy protection level obtained by our solution. We conduct extensive experiments to show the superiority and various performances of our proposal, which illustrates that our proposal can get more than 85% advantage in terms of the sensing distribution availability compared to the traditional spatial cloaking based solutions.
Keywords
Privacy, Graphical models, Distribution functions, Sensors, Games, Differential privacy, Servers, Mobile crowdsensing, spatial distribution, location privacy, game theory, satisfaction form
Discipline
Databases and Information Systems | Information Security
Research Areas
Data Science and Engineering
Publication
IEEE Transactions on Information Forensics and Security
Volume
17
First Page
804
Last Page
818
ISSN
1556-6013
Identifier
10.1109/TIFS.2022.3152409
Publisher
Institute of Electrical and Electronics Engineers
Citation
REN, Yanbing; LI, Xinghua; MIAO, Yinbin; LUO, Bin; WENG, Jian; CHOO, Kim-Kwang Rahmond; and DENG, Robert H..
Towards privacy-preserving spatial distribution crowdsensing: A game theoretic approach. (2022). IEEE Transactions on Information Forensics and Security. 17, 804-818.
Available at: https://ink.library.smu.edu.sg/sis_research/7230