Paper Info
Title
RPTD: Reliability-enhanced Privacy-preserving Truth Discovery for Mobile Crowdsensing
Abstract
Mobile CrowdSensing (MCS) provides effective data collection through smart devices carried by users. However, the data sensed from various devices is privacy-sensitive and not always trustworthy so that the cloud server needs to extract truthful values while protecting the privacy of user personal data. Although some privacy-preserving truth discovery mechanisms have been proposed to address these issues, they ignore the fact that the reliability of truth discovery algorithms may be considerably degraded by outliers in sensing data, and still cannot guarantee strong privacy. In this article, we propose a Reliability-enhanced Privacy-preserving Truth Discovery scheme (RPTD) for MCS to overcome these shortcomings. First, we design a multi-client inner product functional encryption to fully protect the privacy of sensing data, user weights and inferred truths, while supporting dynamic users. Then a new filtering method is constructed to accurately identify outliers in encrypted sensing data submitted by users, which eliminates the disturbance of outliers on the reliability of truth discovery. Theoretical analysis shows that RPTD ensures practical efficiency in computing and communication overhead while ensuring strong privacy and outliers filtering. Experimental results validate feasibility and effectiveness of the proposed RPTD scheme.
Year
DOI
Venue
2022
10.1016/j.jnca.2022.103484
Journal of Network and Computer Applications
Keywords
DocType
Volume
Mobile crowdsensing,Privacy,Truth discovery
Journal
207
ISSN
Citations 
PageRank 
1084-8045
0
0.34
References 
Authors
0
8
Name
Order
Citations
PageRank
Yuxian Liu100.34
Fagui Liu2236.06
Hao-Tian Wu300.68
Jingfeng Yang400.34
Kaihong Zheng500.34
Lingling Xu600.34
Xingfu Yan721.71
Jiankun Hu81976150.35