Dynamic Pricing for Privacy Preserving Mobile Crowdsensing: A Reinforcement Learning Approach
MCS is an emerging technology that exploits the enormous sensing power of widely used mobile devices to complete sensing tasks in a cost efficient manner. Among all outstanding issues of current MCS systems, the concern about a lack of privacy protection for the sensing data of participants has drawn increasing attention recently. Various privacy preserving MCS mechanisms have been proposed for the static scenario where users' privacy protection requirements remain unchanged. In practice, however, users' requirements for privacy protection can be time varying, which further complicates the design of privacy preserving MCS. In this article, we first give an overview of multiple promising approaches for privacy preserving MCS, based on which we make a first attempt to explore privacy preserving MCS in a dynamic scenario, which is cast as a Markov Decision Process. Specifically, we develop a reinforcement learning based approach, by which the platform can dynamically adapt its pricing policy catering to the varying privacy preserving levels of participating users. We further use a case study to evaluate the performance of our proposed approach.
IEEE Network