Name
Title | ||
|---|---|---|
Dynamic Pricing for Privacy-Preserving Mobile Crowdsensing: A Reinforcement Learning Approach |
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/MNET.2018.1700468 | IEEE Network |
Keywords | Field | DocType |
Sensors,Pricing,Privacy,Mobile communication,Task analysis,Perturbation methods,Crowdsourcing | Task analysis,Dynamic pricing,Computer science,Crowdsourcing,Markov decision process,Exploit,Mobile device,Mobile telephony,Reinforcement learning,Distributed computing | Journal |
Volume | Issue | ISSN |
33 | 2 | 0890-8044 |
Citations | PageRank | References |
1 | 0.35 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mengyuan Zhang | 1 | 44 | 4.66 |
| Jiming Chen | 2 | 4389 | 238.91 |
| Lei Yang | 3 | 194 | 37.52 |
| Junshan Zhang | 4 | 2905 | 220.99 |