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...
Gespeichert in:
Veröffentlicht in: | Security and communication networks 2021-11, Vol.2021, p.1-11 |
---|---|
Hauptverfasser: | , , , , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 11 |
---|---|
container_issue | |
container_start_page | 1 |
container_title | Security and communication networks |
container_volume | 2021 |
creator | Wang, Zhihong Li, Yongbiao Li, Dingcheng Li, Ming Zhang, Bincheng Huang, Shishi He, Wen |
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. |
doi_str_mv | 10.1155/2021/9678409 |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2600076781</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2600076781</sourcerecordid><originalsourceid>FETCH-LOGICAL-c403t-ac806d1f1ffe8ffda858e9d0a78968d8aa0aac1af2ebcf44138c4a2ae471c5533</originalsourceid><addsrcrecordid>eNp9kFFLwzAUhYMoOKdv_oCAj1qXtGmb-jbmpsLACepruUtuXEZNZ9Ju1F9vx8RHn-59-M458BFyydkt52k6ilnMR0WWS8GKIzLgRVJEjMfx8d_PxSk5C2HNWMZFLgakmzpYVtZ90BlY7zCEaLwDjxScpgtvt6C6aOExoN_uKVN7-tJCZZuOTrdQtdDY2lHr6DuurGor8HTi650O6EIfuKNjeo8KXeP70DdqOt5sfA1qdU5ODFQBL37vkLzNpq-Tx2j-_PA0Gc8jJVjSRKAkyzQ33BiUxmiQqcRCM8hlkUktARiA4mBiXCojBE-kEhADipyrNE2SIbk69PazXy2GplzXrXf9ZBlnjLG898V76uZAKV-H4NGUG28_wXclZ-VebrmXW_7K7fHrA76yTsPO_k__AGx8e4Q</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2600076781</pqid></control><display><type>article</type><title>Enabling Fairness-Aware and Privacy-Preserving for Quality Evaluation in Vehicular Crowdsensing: A Decentralized Approach</title><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><source>Wiley Online Library (Open Access Collection)</source><source>Alma/SFX Local Collection</source><creator>Wang, Zhihong ; Li, Yongbiao ; Li, Dingcheng ; Li, Ming ; Zhang, Bincheng ; Huang, Shishi ; He, Wen</creator><contributor>Zhang, Leo Y. ; Leo Y Zhang</contributor><creatorcontrib>Wang, Zhihong ; Li, Yongbiao ; Li, Dingcheng ; Li, Ming ; Zhang, Bincheng ; Huang, Shishi ; He, Wen ; Zhang, Leo Y. ; Leo Y Zhang</creatorcontrib><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.</description><identifier>ISSN: 1939-0114</identifier><identifier>EISSN: 1939-0122</identifier><identifier>DOI: 10.1155/2021/9678409</identifier><language>eng</language><publisher>London: Hindawi</publisher><subject>Accuracy ; Blockchain ; Cryptography ; Design ; Digital currencies ; Electronic devices ; Global positioning systems ; GPS ; Machine learning ; Mobile computing ; Privacy ; Quality assessment ; Workers</subject><ispartof>Security and communication networks, 2021-11, Vol.2021, p.1-11</ispartof><rights>Copyright © 2021 Zhihong Wang et al.</rights><rights>Copyright © 2021 Zhihong Wang et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c403t-ac806d1f1ffe8ffda858e9d0a78968d8aa0aac1af2ebcf44138c4a2ae471c5533</citedby><cites>FETCH-LOGICAL-c403t-ac806d1f1ffe8ffda858e9d0a78968d8aa0aac1af2ebcf44138c4a2ae471c5533</cites><orcidid>0000-0001-8637-3358 ; 0000-0003-0524-0604 ; 0000-0002-0874-5010 ; 0000-0002-4956-9316 ; 0000-0002-9871-556X ; 0000-0002-7569-6263 ; 0000-0003-3605-3676</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><contributor>Zhang, Leo Y.</contributor><contributor>Leo Y Zhang</contributor><creatorcontrib>Wang, Zhihong</creatorcontrib><creatorcontrib>Li, Yongbiao</creatorcontrib><creatorcontrib>Li, Dingcheng</creatorcontrib><creatorcontrib>Li, Ming</creatorcontrib><creatorcontrib>Zhang, Bincheng</creatorcontrib><creatorcontrib>Huang, Shishi</creatorcontrib><creatorcontrib>He, Wen</creatorcontrib><title>Enabling Fairness-Aware and Privacy-Preserving for Quality Evaluation in Vehicular Crowdsensing: A Decentralized Approach</title><title>Security and communication networks</title><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.</description><subject>Accuracy</subject><subject>Blockchain</subject><subject>Cryptography</subject><subject>Design</subject><subject>Digital currencies</subject><subject>Electronic devices</subject><subject>Global positioning systems</subject><subject>GPS</subject><subject>Machine learning</subject><subject>Mobile computing</subject><subject>Privacy</subject><subject>Quality assessment</subject><subject>Workers</subject><issn>1939-0114</issn><issn>1939-0122</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>RHX</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kFFLwzAUhYMoOKdv_oCAj1qXtGmb-jbmpsLACepruUtuXEZNZ9Ju1F9vx8RHn-59-M458BFyydkt52k6ilnMR0WWS8GKIzLgRVJEjMfx8d_PxSk5C2HNWMZFLgakmzpYVtZ90BlY7zCEaLwDjxScpgtvt6C6aOExoN_uKVN7-tJCZZuOTrdQtdDY2lHr6DuurGor8HTi650O6EIfuKNjeo8KXeP70DdqOt5sfA1qdU5ODFQBL37vkLzNpq-Tx2j-_PA0Gc8jJVjSRKAkyzQ33BiUxmiQqcRCM8hlkUktARiA4mBiXCojBE-kEhADipyrNE2SIbk69PazXy2GplzXrXf9ZBlnjLG898V76uZAKV-H4NGUG28_wXclZ-VebrmXW_7K7fHrA76yTsPO_k__AGx8e4Q</recordid><startdate>20211112</startdate><enddate>20211112</enddate><creator>Wang, Zhihong</creator><creator>Li, Yongbiao</creator><creator>Li, Dingcheng</creator><creator>Li, Ming</creator><creator>Zhang, Bincheng</creator><creator>Huang, Shishi</creator><creator>He, Wen</creator><general>Hindawi</general><general>Hindawi Limited</general><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><orcidid>https://orcid.org/0000-0001-8637-3358</orcidid><orcidid>https://orcid.org/0000-0003-0524-0604</orcidid><orcidid>https://orcid.org/0000-0002-0874-5010</orcidid><orcidid>https://orcid.org/0000-0002-4956-9316</orcidid><orcidid>https://orcid.org/0000-0002-9871-556X</orcidid><orcidid>https://orcid.org/0000-0002-7569-6263</orcidid><orcidid>https://orcid.org/0000-0003-3605-3676</orcidid></search><sort><creationdate>20211112</creationdate><title>Enabling Fairness-Aware and Privacy-Preserving for Quality Evaluation in Vehicular Crowdsensing: A Decentralized Approach</title><author>Wang, Zhihong ; Li, Yongbiao ; Li, Dingcheng ; Li, Ming ; Zhang, Bincheng ; Huang, Shishi ; He, Wen</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c403t-ac806d1f1ffe8ffda858e9d0a78968d8aa0aac1af2ebcf44138c4a2ae471c5533</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Accuracy</topic><topic>Blockchain</topic><topic>Cryptography</topic><topic>Design</topic><topic>Digital currencies</topic><topic>Electronic devices</topic><topic>Global positioning systems</topic><topic>GPS</topic><topic>Machine learning</topic><topic>Mobile computing</topic><topic>Privacy</topic><topic>Quality assessment</topic><topic>Workers</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Zhihong</creatorcontrib><creatorcontrib>Li, Yongbiao</creatorcontrib><creatorcontrib>Li, Dingcheng</creatorcontrib><creatorcontrib>Li, Ming</creatorcontrib><creatorcontrib>Zhang, Bincheng</creatorcontrib><creatorcontrib>Huang, Shishi</creatorcontrib><creatorcontrib>He, Wen</creatorcontrib><collection>Hindawi Publishing Complete</collection><collection>Hindawi Publishing Subscription Journals</collection><collection>Hindawi Publishing Open Access Journals</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Access via ProQuest (Open Access)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><jtitle>Security and communication networks</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wang, Zhihong</au><au>Li, Yongbiao</au><au>Li, Dingcheng</au><au>Li, Ming</au><au>Zhang, Bincheng</au><au>Huang, Shishi</au><au>He, Wen</au><au>Zhang, Leo Y.</au><au>Leo Y Zhang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Enabling Fairness-Aware and Privacy-Preserving for Quality Evaluation in Vehicular Crowdsensing: A Decentralized Approach</atitle><jtitle>Security and communication networks</jtitle><date>2021-11-12</date><risdate>2021</risdate><volume>2021</volume><spage>1</spage><epage>11</epage><pages>1-11</pages><issn>1939-0114</issn><eissn>1939-0122</eissn><abstract>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.</abstract><cop>London</cop><pub>Hindawi</pub><doi>10.1155/2021/9678409</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0001-8637-3358</orcidid><orcidid>https://orcid.org/0000-0003-0524-0604</orcidid><orcidid>https://orcid.org/0000-0002-0874-5010</orcidid><orcidid>https://orcid.org/0000-0002-4956-9316</orcidid><orcidid>https://orcid.org/0000-0002-9871-556X</orcidid><orcidid>https://orcid.org/0000-0002-7569-6263</orcidid><orcidid>https://orcid.org/0000-0003-3605-3676</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 1939-0114 |
ispartof | Security and communication networks, 2021-11, Vol.2021, p.1-11 |
issn | 1939-0114 1939-0122 |
language | eng |
recordid | cdi_proquest_journals_2600076781 |
source | Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Wiley Online Library (Open Access Collection); Alma/SFX Local Collection |
subjects | Accuracy Blockchain Cryptography Design Digital currencies Electronic devices Global positioning systems GPS Machine learning Mobile computing Privacy Quality assessment Workers |
title | Enabling Fairness-Aware and Privacy-Preserving for Quality Evaluation in Vehicular Crowdsensing: A Decentralized Approach |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-23T13%3A24%3A50IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Enabling%20Fairness-Aware%20and%20Privacy-Preserving%20for%20Quality%20Evaluation%20in%20Vehicular%20Crowdsensing:%20A%20Decentralized%20Approach&rft.jtitle=Security%20and%20communication%20networks&rft.au=Wang,%20Zhihong&rft.date=2021-11-12&rft.volume=2021&rft.spage=1&rft.epage=11&rft.pages=1-11&rft.issn=1939-0114&rft.eissn=1939-0122&rft_id=info:doi/10.1155/2021/9678409&rft_dat=%3Cproquest_cross%3E2600076781%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2600076781&rft_id=info:pmid/&rfr_iscdi=true |