Proceedings of the 13th EAI International Conference on Mobile Multimedia Communications, Mobimedia 2020, 27-28 August 2020, Cyberspace

Research Article

A Three-party Repeated Game Model for Data Privacy in Mobile Edge Crowdsensing of IoT

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  • @INPROCEEDINGS{10.4108/eai.27-8-2020.2295500,
        author={Mingfeng  Zhao and Lei  Chen and Jinbo  Xiong and Youliang  Tian},
        title={A Three-party Repeated Game Model for Data Privacy in Mobile Edge Crowdsensing of IoT},
        proceedings={Proceedings of the 13th EAI International Conference on Mobile Multimedia Communications, Mobimedia 2020, 27-28 August 2020, Cyberspace},
        publisher={EAI},
        proceedings_a={MOBIMEDIA},
        year={2020},
        month={11},
        keywords={privacy protection mobile edge crowdsensing game theory three-party repeated game model nash equilibrium},
        doi={10.4108/eai.27-8-2020.2295500}
    }
    
  • Mingfeng Zhao
    Lei Chen
    Jinbo Xiong
    Youliang Tian
    Year: 2020
    A Three-party Repeated Game Model for Data Privacy in Mobile Edge Crowdsensing of IoT
    MOBIMEDIA
    EAI
    DOI: 10.4108/eai.27-8-2020.2295500
Mingfeng Zhao1, Lei Chen2, Jinbo Xiong3,*, Youliang Tian4
  • 1: College of Mathematics and Informatics, Fujian Normal University
  • 2: College of Engineering and Computing, Georgia Southern University
  • 3: Fujian Provincial Key Laboratory of Network Security and Cryptology
  • 4: State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University
*Contact email: jbxiong@fjnu.edu.cn

Abstract

The low request response delay of mobile edge crowdsensing (MECS) paradigm allows quick interactions among entities in practical scenarios. However, there often exist dishonest behaviors in such interactions, and the personal information leakage involved seriously threatens the privacy and security of sensing users. To tackle this problem, previously we had proposed a three-party game model, though lacking the consideration of multiple interactions in the actual scenario. Based on game theory, this research therefore proposes a three-party repeated game model. Specifically, we propose the corresponding social norms for different phases of sensing data. It analyzes all possible behaviors deviating from rationality, calculates the change of corresponding payoff function, and explores the influencing factors and constraints of players' honest behaviors based on the premise of maximizing interests. Finally, a significant number of simulations and numerical analysis indicate that the proposed model is feasible and effective in maximizing the benefits of game participants.