Privacy Protection-Oriented Mobile Crowdsensing Analysis Based on Game Theory

A formal definition of the sensing user's contribution based on the accuracy in data analysis is given, and a reputation incentive mechanism based on this contribution is proposed, which considers the privacy protection of the Sensing user's data and encourages more sensing users to continually provide the highquality data to participate in the mobile crowdsensing.

Abstract

As a new method of the Internet of Things (IoT), the mobile crowdsensing provides a novel way to realize the ubiquitous social perception. From the point of the game theory, this paper addresses the reputation incentive mechanism and discusses the prisoner's dilemma in the mobile crowdsensing. Firstly, we give a formal definition of the sensing user's contribution based on the accuracy in data analysis, and propose a reputation incentive mechanism based on this contribution, which considers the privacy protection of the sensing data and encourages more sensing users to continually provide the highquality data to participate in the mobile crowdsensing. Furthermore, we observe that the sensing user's benefits not only depend on their own contribution, but also rely on the outcome of the final data transaction between the service provider and the mediator. However, this data transaction is vulnerable to the prisoner's dilemma due to the selfish choice of the both parties. Therefore, we analyze and discuss the prisoner's dilemma in the above data transactionsand give the corresponding solutions. Finally, we point outsome future research directions about privacy protection ofthe mobile crowdsensing.