Privacy protection-based incentive mechanism for Mobile Crowdsensing

Abstract

Mobile crowdsensing (MCS) has been an emerging technology thanks to the smart devices which are capable of sensing and computing to achieve large-scale, complex sensing tasks by cooperation. However, large-scale deployment might be impeded due to that fact that the participant may face the risk of privacy leakage, and if they are not compensated favorably, they may not be willing to contribute sensing capability. To overcome the above challenges, we propose an incentive mechanism for privacy-preserving mobile crowdsensing. More specifically, we introduce a trusted third party and combine partially blind signature, which can effectively reduce the correlation between participants and data and the number of interactions between users and task platform, so as to achieve high level participant privacy. In addition, considering data quality, we define some concepts including data quality relevance, user credit, location relevance and user utility, and design a Credit-based Incentive Mechanism (CIM) based on marginal benefit density and credit, in order to obtain the maximum benefit of a task platform under given budget. Extensive simulations are carried out to show that the proposed incentive mechanism achieves superior performance compared with state-of-the-art solutions. To the existing multi-stage incentive solutions, our proposed solution can achieve higher-quality data at the expense of less time efficiency.