期刊
IEEE NETWORK
卷 33, 期 2, 页码 160-165出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/MNET.2018.1700468
关键词
-
类别
资金
- U.S. NSF [CNS-1559696, IIA-1301726]
- NSFC [61429301]
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据