Publication Type

Journal Article

Version

acceptedVersion

Publication Date

5-2019

Abstract

Benefiting from the fast development of human-carried mobile devices, crowd sensing has become an emerging paradigm to sense and collect data. However, reliability of sensory data provided by participating users is still a major concern. To address this reliability challenge, truth discovery is an effective technology to improve data accuracy, and has garnered significant attention. Nevertheless, many of state of art works in truth discovery, either failed to address the protection of participants' privacy or incurred tremendous overhead on the user side. In this paper, we first propose a privacy-preserving truth discovery scheme, named PPTDS-I, which is implemented on two non-colluding cloud platforms. By capitalizing on properties of modular arithmetic, this scheme is able to protect both users' sensory data and reliability information, and simultaneously achieve high efficiency and fault-tolerance. Additionally, for the scenarios with resource constrained devices, an efficient truth discovery scheme, named PPTDS-II, is presented. It can not only protect users' sensory data, but also avoids user participation in the iterative truth discovery procedure. Detailed security analysis shows that the proposed schemes are secure under a comprehensive threat model. Furthermore, extensive experimental analysis has been conducted, which proves the efficiency of the proposed schemes. (C) 2019 Elsevier Inc. All rights reserved.

Keywords

Crowd sensing, Truth discovery, Privacy-preserving, Efficiency

Discipline

Information Security | Numerical Analysis and Scientific Computing

Research Areas

Cybersecurity

Publication

Information Sciences

Volume

484

First Page

183

Last Page

196

ISSN

0020-0255

Identifier

10.1016/j.ins.2019.01.068

Publisher

Elsevier

Copyright Owner and License

Authors

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