Privacy-Preserving Incentive Mechanism for Mobile Crowdsensing

Abstract

Incentive mechanisms are crucial for motivating adequate users to provide reliable data in mobile crowdsensing (MCS) systems. However, the privacy leakage of most existing incentive mechanisms leads to users unwilling to participate in sensing tasks. In this paper, we propose a privacy-preserving incentive mechanism based on truth discovery. Specifically, we use the secure truth discovery scheme to calculate ground truth and the weight of users’ data while protecting their privacy. Besides, to ensure the accuracy of the MCS results, a data eligibility assessment protocol is proposed to remove the sensing data of unreliable users before performing the truth discovery scheme. Finally, we distribute rewards to users based on their data quality. The analysis shows that our model can protect users’ privacy and prevent the malicious behavior of users and task publishers. In addition, the experimental results demonstrate that our model has high performance, reasonable reward distribution, and robustness to users dropping out.