Preserving adjustable path privacy for task acquisition in Mobile Crowdsensing Systems

Abstract

Mobile Crowdsensing is an emerging and promising sensing paradigm in which sensor data can be collected by mobile users equipped with smart devices. In Mobile Crowdsensing Systems (MCS), workers bid for location-based sensing tasks and get rewards from the platform. However, the bidding may leak workers’ path privacy, which means the sensitive locations could be inferred from innocent locations along a path as workers continuously acquire for tasks. This privacy concern may significantly hinder the participation of workers. As a result, this paper designs a novel framework for adjustable path privacy preservation used for task acquisition in MCS. In this framework, workers are allowed to flexibly adjust their privacy preferences on the amount, sensitivity, and cost of private locations. Two algorithms are proposed to determine the set of bidding tasks for workers that jointly consider the privacy concerns and profits. The first algorithm processes in a centralized approach, which is proved to be rational, truthful and efficient. The second algorithm allows workers to decide their task acquisition locally, and guarantees the Nash equilibrium among workers. Both algorithms are validated via real-world dataset. The evaluation results demonstrate that the two proposed algorithms outperform baseline algorithms on both platform and worker sides.