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FairCs—Blockchain-Based Fair Crowdsensing Scheme using Trusted Execution Environment

Crowdsensing applications provide platforms for sharing sensing data collected by mobile devices. A blockchain system has the potential to replace a traditional centralized trusted third party for crowdsensing services to perform operations that involve evaluating the quality of sensing data, finish...

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Detalles Bibliográficos
Autores principales: Liang, Yihuai, Li, Yan, Shin, Byeong-Seok
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7309114/
https://www.ncbi.nlm.nih.gov/pubmed/32503191
http://dx.doi.org/10.3390/s20113172
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author Liang, Yihuai
Li, Yan
Shin, Byeong-Seok
author_facet Liang, Yihuai
Li, Yan
Shin, Byeong-Seok
author_sort Liang, Yihuai
collection PubMed
description Crowdsensing applications provide platforms for sharing sensing data collected by mobile devices. A blockchain system has the potential to replace a traditional centralized trusted third party for crowdsensing services to perform operations that involve evaluating the quality of sensing data, finishing payment, and storing sensing data and so forth. The requirements which are codified as smart contracts are executed to evaluate the quality of sensing data in a blockchain. However, regardless of the fact that the quality of sensing data may actually be sufficient, one key challenge is that malicious requesters can deliberately publish abnormal requirements that cause failure to occur in the quality evaluation process. If requesters control a miner node or full node, they can access the data without making payment; this is because of the transparency of data stored in the blockchain. This issue promotes unfair dealing and severely lowers the motivation of workers to participate in crowdsensing tasks. We (i) propose a novel crowdsensing scheme to address this issue using Trusted Execution Environments; (ii) offer a solution for the confidentiality and integrity of sensing data, which is only accessible by the worker and corresponding requester; (iii) and finally, report on the implementation of a prototype and evaluate its performance. Our results demonstrate that the proposed solution can guarantee fairness without a significant increase in overhead.
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spelling pubmed-73091142020-06-25 FairCs—Blockchain-Based Fair Crowdsensing Scheme using Trusted Execution Environment Liang, Yihuai Li, Yan Shin, Byeong-Seok Sensors (Basel) Article Crowdsensing applications provide platforms for sharing sensing data collected by mobile devices. A blockchain system has the potential to replace a traditional centralized trusted third party for crowdsensing services to perform operations that involve evaluating the quality of sensing data, finishing payment, and storing sensing data and so forth. The requirements which are codified as smart contracts are executed to evaluate the quality of sensing data in a blockchain. However, regardless of the fact that the quality of sensing data may actually be sufficient, one key challenge is that malicious requesters can deliberately publish abnormal requirements that cause failure to occur in the quality evaluation process. If requesters control a miner node or full node, they can access the data without making payment; this is because of the transparency of data stored in the blockchain. This issue promotes unfair dealing and severely lowers the motivation of workers to participate in crowdsensing tasks. We (i) propose a novel crowdsensing scheme to address this issue using Trusted Execution Environments; (ii) offer a solution for the confidentiality and integrity of sensing data, which is only accessible by the worker and corresponding requester; (iii) and finally, report on the implementation of a prototype and evaluate its performance. Our results demonstrate that the proposed solution can guarantee fairness without a significant increase in overhead. MDPI 2020-06-03 /pmc/articles/PMC7309114/ /pubmed/32503191 http://dx.doi.org/10.3390/s20113172 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Liang, Yihuai
Li, Yan
Shin, Byeong-Seok
FairCs—Blockchain-Based Fair Crowdsensing Scheme using Trusted Execution Environment
title FairCs—Blockchain-Based Fair Crowdsensing Scheme using Trusted Execution Environment
title_full FairCs—Blockchain-Based Fair Crowdsensing Scheme using Trusted Execution Environment
title_fullStr FairCs—Blockchain-Based Fair Crowdsensing Scheme using Trusted Execution Environment
title_full_unstemmed FairCs—Blockchain-Based Fair Crowdsensing Scheme using Trusted Execution Environment
title_short FairCs—Blockchain-Based Fair Crowdsensing Scheme using Trusted Execution Environment
title_sort faircs—blockchain-based fair crowdsensing scheme using trusted execution environment
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7309114/
https://www.ncbi.nlm.nih.gov/pubmed/32503191
http://dx.doi.org/10.3390/s20113172
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