Reliable and privacy-preserving truth discovery for mobile crowdsensing systems

Publication Type

Journal Article

Publication Date

1-2019

Abstract

Truth discovery has received considerable attention in mobile crowdsensing systems. In real practice, it is vital to resolve conflicts among a large amount of sensory data and estimate the truthful information. Although truth discovery has been widely explored to improve aggregation accuracy, numerous security and privacy issues still need to be addressed. Existing schemes either do not guarantee the privacy of each participating user, or fail to consider practical needs in crowdsensing systems. In this paper, we present two reliable and privacy-preserving truth discovery schemes for different scenarios. Our first design is fit for applications where users are relatively stable. By employing the homomorphic Paillier encryption, one-way hash chain, and super-increasing sequence techniques, this approach not only guarantees strong privacy, but also is highly efficient and practical. Our second design suits applications where users are frequently moving. In such an application, we explore data perturbation and homomorphic Paillier encryption to shift all user workloads to the server side, without compromising users' privacy. Through detailed security analysis, we demonstrate that both schemes are secure, practical, and privacy-preserving. Moreover, extensive experiments based on real world and simulated mobile crowdsensing systems, we demonstrate the efficiency of our proposed schemes.

Keywords

Cloud computing, Cryptography, Data aggregation, Data privacy, Mobile crowdsensing, Privacy, Privacy-preserving, Reliability, Reliable, Sensors, Truth discovery

Discipline

Information Security

Publication

IEEE Transactions on Dependable and Secure Computing

ISSN

1545-5971

Identifier

10.1109/TDSC.2019.2919517

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Additional URL

https://doi.org/10.1109/TDSC.2019.2919517

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