Location and Reward Privacy-Preserving based Secure Task Allocation in Mobile Crowdsensing

Abstract

Online multi-task allocation has become an essential research topic in Mobile Crowdsensing (MCS). Most existing studies merely focus on minimizing the total distance that workers need to travel, but ignore considering the total task rewards, which could lead to a reduction in the willingness of workers to complete tasks. In this paper, to incentivize workers to participate in tasks and protect their privacy, we propose a Location and Reward Privacy-Preserving based Secure Task Allocation(LRPP-STA) scheme. First, we design a secure distance computation method to obtain the distance from the workers to the tasks under location privacy preserving. Second, considering fixed reward for the task, we propose a Fixed Rewarding Secure Task Allocation(FR-STA) scheme, where a secure utility calculation method is proposed to calculate the encrypted utility of the worker upon completing tasks under rewards privacy preserving, along with the path planning for workers to maximize the total utility of the system through an Extended Maximum-Utility Flow model(EMUF). Third, considering the situation of dynamic task reward adjusted by requesters based on the supply and demand relationship as well as the urgency of the task, we propose a Dynamic Rewarding Secure Task Allocation(DR-STA) scheme to optimize the task allocation for workers while improving requesters satisfaction. Finally, we theoretically analyze the security of location and reward privacy-preserving scheme, and conduct extensive experiments with real-world datasets to verify that the secure task allocation scheme is effective in improving the total utility of workers compared to other baseline online tasking schemes.