PPTDS: A privacy-preserving truth discovery scheme in crowd sensing systems

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 e...

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Main Authors: ZHANG, Chuan, ZHU, Liehuang, XU, Chang, SHARIF, Kashif, LIU, Ximeng
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Language:English
Published: Institutional Knowledge at Singapore Management University 2019
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Online Access:https://ink.library.smu.edu.sg/sis_research/5152
https://ink.library.smu.edu.sg/context/sis_research/article/6155/viewcontent/PPTDS_av.pdf
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spelling sg-smu-ink.sis_research-61552020-07-09T04:18:03Z PPTDS: A privacy-preserving truth discovery scheme in crowd sensing systems ZHANG, Chuan ZHU, Liehuang XU, Chang SHARIF, Kashif LIU, Ximeng 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. 2019-05-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/5152 info:doi/10.1016/j.ins.2019.01.068 https://ink.library.smu.edu.sg/context/sis_research/article/6155/viewcontent/PPTDS_av.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Crowd sensing Truth discovery Privacy-preserving Efficiency Information Security Numerical Analysis and Scientific Computing
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Crowd sensing
Truth discovery
Privacy-preserving
Efficiency
Information Security
Numerical Analysis and Scientific Computing
spellingShingle Crowd sensing
Truth discovery
Privacy-preserving
Efficiency
Information Security
Numerical Analysis and Scientific Computing
ZHANG, Chuan
ZHU, Liehuang
XU, Chang
SHARIF, Kashif
LIU, Ximeng
PPTDS: A privacy-preserving truth discovery scheme in crowd sensing systems
description 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.
format text
author ZHANG, Chuan
ZHU, Liehuang
XU, Chang
SHARIF, Kashif
LIU, Ximeng
author_facet ZHANG, Chuan
ZHU, Liehuang
XU, Chang
SHARIF, Kashif
LIU, Ximeng
author_sort ZHANG, Chuan
title PPTDS: A privacy-preserving truth discovery scheme in crowd sensing systems
title_short PPTDS: A privacy-preserving truth discovery scheme in crowd sensing systems
title_full PPTDS: A privacy-preserving truth discovery scheme in crowd sensing systems
title_fullStr PPTDS: A privacy-preserving truth discovery scheme in crowd sensing systems
title_full_unstemmed PPTDS: A privacy-preserving truth discovery scheme in crowd sensing systems
title_sort pptds: a privacy-preserving truth discovery scheme in crowd sensing systems
publisher Institutional Knowledge at Singapore Management University
publishDate 2019
url https://ink.library.smu.edu.sg/sis_research/5152
https://ink.library.smu.edu.sg/context/sis_research/article/6155/viewcontent/PPTDS_av.pdf
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