Enabling Fairness-Aware and Privacy-Preserving for Quality Evaluation in Vehicular Crowdsensing: A Decentralized Approach

With the rapid development of vehicular crowdsensing, it becomes easier and more efficient for mobile devices to sense, compute, and measure various data. However, how to address the fair quality evaluation between the platform and participants while preserving the privacy of solutions is still a ch...

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Autores principales:	Zhihong Wang, Yongbiao Li, Dingcheng Li, Ming Li, Bincheng Zhang, Shishi Huang, Wen He
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi-Wiley 2021
Materias:	Technology (General) T1-995 Science (General) Q1-390
Acceso en línea:	https://doaj.org/article/7b0b86a7d2fb4343993d434a884b9c15
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spelling	oai:doaj.org-article:7b0b86a7d2fb4343993d434a884b9c152021-11-22T01:10:55ZEnabling Fairness-Aware and Privacy-Preserving for Quality Evaluation in Vehicular Crowdsensing: A Decentralized Approach1939-012210.1155/2021/9678409https://doaj.org/article/7b0b86a7d2fb4343993d434a884b9c152021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/9678409https://doaj.org/toc/1939-0122With the rapid development of vehicular crowdsensing, it becomes easier and more efficient for mobile devices to sense, compute, and measure various data. However, how to address the fair quality evaluation between the platform and participants while preserving the privacy of solutions is still a challenge. In the work, we present a fairness-aware and privacy-preserving scheme for worker quality evaluation by leveraging the blockchain, trusted execution environment (TEE), and machine learning technologies. Specifically, we build our framework atop the decentralized blockchain which can resist a single point of failure/compromise. The smart contracts paradigm in blockchain enforces correct and automatic program execution for task processing. In addition, machine learning and TEE are utilized to evaluate the quality of data collected by the sensors in a privacy-preserving and fair way, eliminating human subject judgement of the sensing solutions. Finally, a prototype of the proposed scheme is implemented to verify the feasibility and efficiency with a benchmark dataset.Zhihong WangYongbiao LiDingcheng LiMing LiBincheng ZhangShishi HuangWen HeHindawi-WileyarticleTechnology (General)T1-995Science (General)Q1-390ENSecurity and Communication Networks, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Technology (General) T1-995 Science (General) Q1-390
spellingShingle	Technology (General) T1-995 Science (General) Q1-390 Zhihong Wang Yongbiao Li Dingcheng Li Ming Li Bincheng Zhang Shishi Huang Wen He Enabling Fairness-Aware and Privacy-Preserving for Quality Evaluation in Vehicular Crowdsensing: A Decentralized Approach
description	With the rapid development of vehicular crowdsensing, it becomes easier and more efficient for mobile devices to sense, compute, and measure various data. However, how to address the fair quality evaluation between the platform and participants while preserving the privacy of solutions is still a challenge. In the work, we present a fairness-aware and privacy-preserving scheme for worker quality evaluation by leveraging the blockchain, trusted execution environment (TEE), and machine learning technologies. Specifically, we build our framework atop the decentralized blockchain which can resist a single point of failure/compromise. The smart contracts paradigm in blockchain enforces correct and automatic program execution for task processing. In addition, machine learning and TEE are utilized to evaluate the quality of data collected by the sensors in a privacy-preserving and fair way, eliminating human subject judgement of the sensing solutions. Finally, a prototype of the proposed scheme is implemented to verify the feasibility and efficiency with a benchmark dataset.
format	article
author	Zhihong Wang Yongbiao Li Dingcheng Li Ming Li Bincheng Zhang Shishi Huang Wen He
author_facet	Zhihong Wang Yongbiao Li Dingcheng Li Ming Li Bincheng Zhang Shishi Huang Wen He
author_sort	Zhihong Wang
title	Enabling Fairness-Aware and Privacy-Preserving for Quality Evaluation in Vehicular Crowdsensing: A Decentralized Approach
title_short	Enabling Fairness-Aware and Privacy-Preserving for Quality Evaluation in Vehicular Crowdsensing: A Decentralized Approach
title_full	Enabling Fairness-Aware and Privacy-Preserving for Quality Evaluation in Vehicular Crowdsensing: A Decentralized Approach
title_fullStr	Enabling Fairness-Aware and Privacy-Preserving for Quality Evaluation in Vehicular Crowdsensing: A Decentralized Approach
title_full_unstemmed	Enabling Fairness-Aware and Privacy-Preserving for Quality Evaluation in Vehicular Crowdsensing: A Decentralized Approach
title_sort	enabling fairness-aware and privacy-preserving for quality evaluation in vehicular crowdsensing: a decentralized approach
publisher	Hindawi-Wiley
publishDate	2021
url	https://doaj.org/article/7b0b86a7d2fb4343993d434a884b9c15
work_keys_str_mv	AT zhihongwang enablingfairnessawareandprivacypreservingforqualityevaluationinvehicularcrowdsensingadecentralizedapproach AT yongbiaoli enablingfairnessawareandprivacypreservingforqualityevaluationinvehicularcrowdsensingadecentralizedapproach AT dingchengli enablingfairnessawareandprivacypreservingforqualityevaluationinvehicularcrowdsensingadecentralizedapproach AT mingli enablingfairnessawareandprivacypreservingforqualityevaluationinvehicularcrowdsensingadecentralizedapproach AT binchengzhang enablingfairnessawareandprivacypreservingforqualityevaluationinvehicularcrowdsensingadecentralizedapproach AT shishihuang enablingfairnessawareandprivacypreservingforqualityevaluationinvehicularcrowdsensingadecentralizedapproach AT wenhe enablingfairnessawareandprivacypreservingforqualityevaluationinvehicularcrowdsensingadecentralizedapproach
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Enabling Fairness-Aware and Privacy-Preserving for Quality Evaluation in Vehicular Crowdsensing: A Decentralized Approach

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