Abstract
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.
Original language | English (US) |
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Article number | 8645060 |
Pages (from-to) | 160-165 |
Number of pages | 6 |
Journal | IEEE Network |
Volume | 33 |
Issue number | 2 |
DOIs | |
State | Published - Mar 1 2019 |
ASJC Scopus subject areas
- Software
- Information Systems
- Hardware and Architecture
- Computer Networks and Communications