More Web Proxy on the site http://driver.im/

research-article

Secure and Lightweight Blockchain-based Truthful Data Trading for Real-Time Vehicular Crowdsensing

Authors:

Naixue XiongAuthors Info & Claims

ACM Transactions on Embedded Computing Systems, Volume 23, Issue 1

Article No.: 7, Pages 1 - 31

https://doi.org/10.1145/3582008

Published: 10 January 2024 Publication History

Abstract

As the number of smart cars grows rapidly, vehicular crowdsensing (VCS) is gradually becoming popular. In a VCS infrastructure, sensing devices and computing units hold on smart cars as well as cloud servers form an IoT-edge-cloud continuum to perform real-time sensing tasks. In order to encourage the smart cars to participate in the real-time VCS process, blockchain technology can be combined with VCS to provide an automated incentive for VCS data trading without relying on trusted third parties. However, directly using blockchain to enforce the VCS data trading process incurs expensive service fees and participants still can conduct various misbehavior. In this article, we propose a secure blockchain-based data trading system for VCS named BTT system to address the above issues. In particular, we first integrate the blockchain-based data trading process with a lightweight privacy-preserving truth discovery algorithm to ensure the accuracy of sensing data while preserving data privacy. We then propose a gas-aware optimization mechanism to minimize the gas consumption of the data trading process. Finally, we carefully design a distributed judgment mechanism to regulate all participants to behave correctly in the data trading process. To demonstrate the practicability of our design, we implement a prototype of the BTT system deployed on an Ethereum test network and conduct extensive simulations.

References

[1]

2022. ETH GAS STATION. Retrieved July 5, 2021 from https://ethgasstation.info/.

[2]

2022. The Ethereum Project. Retrieved July 5, 2021 from https://ethereum.org/en/.

[3]

2022. General Data Protection Regulation (GDPR) — Official Legal Text: 2020. Retrieved July 5, 2021 from https://gdpr-info.eu/.

[4]

2022. Remix IDE. Retrieved July 13, 2022 from https://remix.ethereum.org/.

[5]

Waleed Alasmary, Hamed Sadeghi, and Shahrokh Valaee. 2013. Crowdsensing in vehicular sensor networks with limited channel capacity. In 2013 IEEE International Conference on Communications (ICC’13). IEEE, 1833–1838.

[6]

Baoyi An, Mingjun Xiao, An Liu, Guoju Gao, and Hui Zhao. 2019. Truthful crowdsensed data trading based on reverse auction and blockchain. In International Conference on Database Systems for Advanced Applications. Springer, 292–309.

Digital Library

[7]

Baoyi An, Mingjun Xiao, An Liu, Guoju Gao, and Hui Zhao. 2019. Truthful crowdsensed data trading based on reverse auction and blockchain. In Proceedings of the 24th International Conference on Database Systems for Advanced Applications, Part I (DASFAA’19), Lecture Notes in Computer Science, Vol. 11446, Guoliang Li, Jun Yang, João Gama, Juggapong Natwichai, and Yongxin Tong (Eds.). Springer, 292–309. DOI:

Digital Library

[8]

Baoyi An, Mingjun Xiao, An Liu, Yun Xu, Xiangliang Zhang, and Qing Li. 2021. Secure crowdsensed data trading based on blockchain. IEEE Transactions on Mobile Computing (2021).

[9]

Masoud Barati and Omer Rana. 2022. Tracking GDPR compliance in cloud-based service delivery. IEEE Transactions on Services Computing 15, 3 (2022), 1498–1511. DOI:

[10]

Lorenzo Blanco, Valter Crescenzi, Paolo Merialdo, and Paolo Papotti. 2010. Probabilistic models to reconcile complex data from inaccurate data sources. In Advanced Information Systems Engineering, Barbara Pernici (Ed.). Vol. 6051, Springer Berlin Heidelberg, Berlin, Heidelberg, 83–97.

[11]

Chengjun Cai, Yifeng Zheng, and Cong Wang. 2018. Leveraging crowdsensed data streams to discover and sell knowledge: A secure and efficient realization. In Proceedings of the 38th IEEE International Conference on Distributed Computing Systems (ICDCS’18). IEEE Computer Society, 589–599. DOI:

[12]

Chenyun Dai, Dan Lin, Elisa Bertino, and Murat Kantarcioglu. 2008. An approach to evaluate data trustworthiness based on data provenance. In Workshop on Secure Data Management. Springer, 82–98.

[13]

Xin Luna Dong, Laure Berti-Equille, and Divesh Srivastava. 2009. Integrating conflicting data: The role of source dependence. Proceedings of the VLDB Endowment 2, 1 (2009), 550–561.

Digital Library

[14]

Xin Luna Dong and Divesh Srivastava. 2013. Big data integration. In Proceedings of the 2013 IEEE 29th International Conference on Data Engineering (ICDE’13). IEEE, 1245–1248.

Digital Library

[15]

Huayi Duan, Yifeng Zheng, Yuefeng Du, Anxin Zhou, Cong Wang, and Man Ho Au. 2019. Aggregating crowd wisdom via blockchain: A private, correct, and robust realization. In Proceedings of the 2019 IEEE International Conference on Pervasive Computing and Communications (PerCom’19). IEEE, 1–10.

[16]

Alban Galland, Serge Abiteboul, Amélie Marian, and Pierre Senellart. 2010. Corroborating information from disagreeing views. In Proceedings of the 3rd ACM International Conference on Web Search and Data Mining. 131–140.

Digital Library

[17]

Guoju Gao, Mingjun Xiao, Jie Wu, Liusheng Huang, and Chang Hu. 2018. Truthful incentive mechanism for nondeterministic crowdsensing with vehicles. IEEE Transactions on Mobile Computing 17, 12 (2018), 2982–2997. DOI:

Digital Library

[18]

Jingsheng Gao, Shaojing Fu, Yuchuan Luo, and Tao Xie. 2020. Location privacy-preserving truth discovery in mobile crowd sensing. In Proceedings of the 29th International Conference on Computer Communications and Networks (ICCCN’20). IEEE, 1–9. DOI:

[19]

Yaodong Huang, Yiming Zeng, Fan Ye, and Yuanyuan Yang. 2020. Fair and protected profit sharing for data trading in pervasive edge computing environments. In Proceedings of the 39th IEEE Conference on Computer Communications (INFOCOM’20). IEEE, 1718–1727. DOI:

Digital Library

[20]

Gjergji Kasneci, Jurgen Van Gael, David Stern, and Thore Graepel. 2011. Cobayes: Bayesian knowledge corroboration with assessors of unknown areas of expertise. In Proceedings of the 4th ACM International Conference on Web Search and Data Mining. 465–474.

Digital Library

[21]

Qi Li, Yaliang Li, Jing Gao, Lu Su, Bo Zhao, Murat Demirbas, Wei Fan, and Jiawei Han. 2014. A confidence-aware approach for truth discovery on long-tail data. Proceedings of the VLDB Endowment 8, 4 (2014), 425–436.

Digital Library

[22]

Qi Li, Yaliang Li, Jing Gao, Bo Zhao, Wei Fan, and Jiawei Han. 2014. Resolving conflicts in heterogeneous data by truth discovery and source reliability estimation. In Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data. 1187–1198.

Digital Library

[23]

Xian Li, Xin Luna Dong, Kenneth Lyons, Weiyi Meng, and Divesh Srivastava. 2015. Truth finding on the deep web: Is the problem solved? arXiv:1503.00303. https://arxiv.org/abs/1503.00303

[24]

Danwei Liang, Jian An, Jindong Cheng, He Yang, and Ruowei Gui. 2018. The quality control in crowdsensing based on twice consensuses of blockchain. In Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers (UbiComp/ISWC’18) Adjunct. ACM, 630–635. DOI:

Digital Library

[25]

Shanshan Lyu, Wentao Ouyang, Yongqing Wang, Huawei Shen, and Xueqi Cheng. 2021. Truth discovery by claim and source embedding. IEEE Transactions on Knowledge and Data Engineering 33, 3 (2021), 1264–1275. DOI:

[26]

Deepak Maram, Harjasleen Malvai, Fan Zhang, Nerla Jean-Louis, Alexander Frolov, Tyler Kell, Tyrone Lobban, Christine Moy, Ari Juels, and Andrew Miller. 2021. Candid: Can-do decentralized identity with legacy compatibility, sybil-resistance, and accountability. In Proceedings of the 2021 IEEE Symposium on Security and Privacy (SP’21). IEEE, 1348–1366.

[27]

Amélie Marian and Minji Wu. 2011. Corroborating information from web sources. IEEE Data Engineering Bulletin 34, 3 (2011), 11–17.

[28]

Chenglin Miao, Wenjun Jiang, Lu Su, Yaliang Li, Suxin Guo, Zhan Qin, Houping Xiao, Jing Gao, and Kui Ren. 2015. Cloud-enabled privacy-preserving truth discovery in crowd sensing systems. In Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems. 183–196.

Digital Library

[29]

Chenglin Miao, Wenjun Jiang, Lu Su, Yaliang Li, Suxin Guo, Zhan Qin, Houping Xiao, Jing Gao, and Kui Ren. 2015. Cloud-Enabled privacy-preserving truth discovery in crowd sensing systems. In Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems (SenSys’15), Junehwa Song, Tarek F. Abdelzaher, and Cecilia Mascolo (Eds.). ACM, 183–196. DOI:

Digital Library

[30]

Chenglin Miao, Wenjun Jiang, Lu Su, Yaliang Li, Suxin Guo, Zhan Qin, Houping Xiao, Jing Gao, and Kui Ren. 2019. Privacy-Preserving truth discovery in crowd sensing systems. ACM Transactions on Sensor Networks 15, 1 (2019), 9:1–9:32. DOI:

Digital Library

[31]

Satoshi Nakamoto. 2008. Bitcoin: A peer-to-peer electronic cash system. Decentralized Business Review (2008), 21260.

[32]

Lewis Nkenyereye, S. M. Riazul Islam, Muhammad Bilal, Mohammad Abdullah-Al-Wadud, Atif Alamri, and Anand Nayyar. 2021. Secure crowd-sensing protocol for fog-based vehicular cloud. Future Generation Computer Systems 120 (2021), 61–75.

[33]

Jeff Pasternack and Dan Roth. 2011. Making better informed trust decisions with generalized fact-finding. In Proceedings of the 22nd International Joint Conference on Artificial Intelligence. Citeseer.

[34]

Lingjun Pu, Xu Chen, Guoqiang Mao, Qinyi Xie, and Jingdong Xu. 2018. Chimera: An energy-efficient and deadline-aware hybrid edge computing framework for vehicular crowdsensing applications. IEEE Internet of Things Journal 6, 1 (2018), 84–99.

[35]

Guo-Jun Qi, Charu C. Aggarwal, Jiawei Han, and Thomas Huang. 2013. Mining collective intelligence in diverse groups. In Proceedings of the 22nd International Conference on World Wide Web. 1041–1052.

Digital Library

[36]

Saiyu Qi, Youshui Lu, Yuanqing Zheng, Yumo Li, and Xiaofeng Chen. 2021. Cpds: Enabling compressed and private data sharing for industrial internet of things over blockchain. IEEE Transactions on Industrial Informatics 17, 4 (2021), 2376–2387. DOI:

[37]

Saiyu Qi, Jianfeng Wang, Meixia Miao, Meng Zhang, and Xiaofeng Chen. 2021. TinyEnc: Enabling compressed and encrypted big data stores with rich query support. IEEE Transactions on Dependable and Secure Computing (2021), 1–1. DOI:

[38]

S. Qi and Y. Zheng. 2019. Crypt-DAC: Cryptographically enforced dynamic access control in the cloud. IEEE Transactions on Dependable and Secure Computing PP, 99 (2019), 1–1.

[39]

Saiyu Qi, Yuanqing Zheng, Xiaofeng Chen, and Wei Wei. 2022. Ants can carry cheese: Secure and private RFID-enabled third-party distribution. IEEE Transactions on Dependable and Secure Computing 19, 3 (2022), 1505–1517. DOI:

[40]

Marten Risius and Kai Spohrer. 2017. A blockchain research framework. Business & Information Systems Engineering 59, 6 (2017), 385–409.

[41]

Nick Szabo. 1996. Smart contracts: Building blocks for digital markets. EXTROPY: The Journal of Transhumanist Thought 18, 2 (1996).

[42]

Xiaoting Tang, Cong Wang, Xingliang Yuan, and Qian Wang. 2018. Non-Interactive privacy-preserving truth discovery in crowd sensing applications. In Proceedings of the 2018 IEEE Conference on Computer Communications (INFOCOM’18). IEEE, 1988–1996. DOI:

Digital Library

[43]

Yifan Tian, Jiawei Yuan, and Houbing Song. 2019. Secure and reliable decentralized truth discovery using blockchain. In Proceedings of the 7th IEEE Conference on Communications and Network Security (CNS’19). IEEE, 1–8. DOI:

[44]

V. G. Vinod Vydiswaran, ChengXiang Zhai, and Dan Roth. 2011. Content-driven trust propagation framework. In Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 974–982.

Digital Library

[45]

Wei Wei, Xunli Fan, Houbing Song, Xiumei Fan, and Jiachen Yang. 2018. Imperfect information dynamic Stackelberg game based resource allocation using hidden Markov for cloud computing. IEEE Transactions on Services Computing 11, 1 (2018), 78–89. DOI:

[46]

W. Wei, S. Liu, W. Li, and D. Du. 2018. Fractal intelligent privacy protection in online social network using attribute-based encryption schemes. IEEE Transactions on Computational Social Systems 5, 3 (2018), 736–747. DOI:

[47]

W. Wei and Y. Qi. 2011. Information potential fields navigation in wireless ad-hoc sensor networks. Sensors 11, 5 (2011), 4794–4807.

[48]

Wei Wei, Jun Shen, Akbar Telikani, Mahdi Fahmideh, and Wei Gao. 2022. Feasibility analysis of data transmission in partially damaged IoT networks of vehicles. IEEE Transactions on Intelligent Transportation Systems (2022), 1–12. DOI:

Digital Library

[49]

Wei Wei, Houbing Song, Wei Li, Peiyi Shen, and Athanasios Vasilakos. 2017. Gradient-driven parking navigation using a continuous information potential field based on wireless sensor network. Information Sciences 408 (2017), 100–114. https://www.sciencedirect.com/science/article/pii/S0020025517306928.

[50]

G. Wood. 2014. Ethereum: A secure decentralised generalised transaction ledger. http://cryptochainuni.com/wp-content/uploads/Ethereum-A-Secure-Decentralised-Generalised-Transaction-Ledger-Yellow-Paper.pdf.

[51]

Shuangke Wu, Yanjiao Chen, Qian Wang, Minghui Li, Cong Wang, and Xiangyang Luo. 2018. CReam: A smart contract enabled collusion-resistant e-auction. IEEE Transactions on Information Forensics and Security 14, 7 (2018), 1687–1701.

[52]

Guowen Xu, Hongwei Li, and Rongxing Lu. 2018. Practical and privacy-aware truth discovery in mobile crowd sensing systems. In Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security. 2312–2314.

Digital Library

[53]

Guowen Xu, Hongwei Li, and Rongxing Lu. 2018. Practical and privacy-aware truth discovery in mobile crowd sensing systems. In Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security (CCS’18), David Lie, Mohammad Mannan, Michael Backes, and XiaoFeng Wang (Eds.). ACM, 2312–2314. DOI:

Digital Library

[54]

Guowen Xu, Hongwei Li, Shengmin Xu, Hao Ren, Yinghui Zhang, Jianfei Sun, and Robert H. Deng. 2020. Catch you if you deceive me: Verifiable and privacy-aware truth discovery in crowdsensing systems. In ASIA CCS’20: The 15th ACM Asia Conference on Computer and Communications Security, Taipei, Taiwan, October 5-9, 2020, Hung-Min Sun, Shiuh-Pyng Shieh, Guofei Gu, and Giuseppe Ateniese (Eds.). ACM, 178–192. DOI:

Digital Library

[55]

H. Xu, W. Wei, Y. Qi, and S. Qi. 2022. Blockchain-based crowdsourcing makes training dataset of machine learning no longer be in short supply. Wireless Communications and Mobile Computing 2022 (2022).

[56]

Kaiping Xue, Bin Zhu, Qingyou Yang, Na Gai, David Wei, and Nenghai Yu. 2020. InPPTD: A lightweight incentive-based privacy-preserving truth discovery for crowdsensing systems. IEEE Internet of Things Journal PP (Oct. 2020), 1–1. DOI:

[57]

Kaiping Xue, Bin Zhu, Qingyou Yang, Na Gai, David S. L. Wei, and Nenghai Yu. 2021. InPPTD: A lightweight incentive-based privacy-preserving truth discovery for crowdsensing systems. IEEE Internet Things Journal 8, 6 (2021), 4305–4316. DOI:

[58]

Xiaoxin Yin, Jiawei Han, and S. Yu Philip. 2008. Truth discovery with multiple conflicting information providers on the Web. IEEE Transactions on Knowledge and Data Engineering 20, 6 (2008), 796–808.

Digital Library

[59]

Tzu-Yang Yu, Xiru Zhu, and Hongji Chen. 2017. GoSense: Efficient vehicle selection for user defined vehicular crowdsensing. In Proceedings of the 2017 IEEE 85th Vehicular Technology Conference (VTC Spring). IEEE, 1–5.

[60]

Chuan Zhang, Chang Xu, Liehuang Zhu, Yanwei Li, Can Zhang, and Huishu Wu. 2020. An efficient and privacy-preserving truth discovery scheme in crowdsensing applications. Computers & Security 97 (2020), 101848. DOI:

Digital Library

[61]

Can Zhang, Liehuang Zhu, Chang Xu, and Kashif Sharif. 2020. PRVB: Achieving privacy-preserving and reliable vehicular crowdsensing via blockchain oracle. IEEE Transactions on Vehicular Technology 70, 1 (2020), 831–843.

[62]

Bo Zhao and Jiawei Han. 2012. A probabilistic model for estimating real-valued truth from conflicting sources. Proceedings of QDB 1817 (2012).

[63]

Bo Zhao, Benjamin I. P. Rubinstein, Jim Gemmell, and Jiawei Han. 2012. A Bayesian approach to discovering truth from conflicting sources for data integration. arXiv:1203.0058. https://arxiv.org/abs/1203.0058

[64]

Yifeng Zheng, Huayi Duan, and Cong Wang. 2018. Learning the truth privately and confidently: Encrypted confidence-aware truth discovery in mobile crowdsensing. IEEE Transactions on Information Forensics and Security 13, 10 (2018), 2475–2489.

[65]

Yifeng Zheng, Huayi Duan, and Cong Wang. 2018. Learning the truth privately and confidently: Encrypted confidence-aware truth discovery in mobile crowdsensing. IEEE Transactions on Information Forensics and Security 13, 10 (2018), 2475–2489. DOI:

Cited By

Chen WYang WXiao MXue LWang S(2025)LBDT: A Lightweight Blockchain-Based Data Trading Scheme in Internet of Vehicles Using Proof-of-ReputationIEEE Transactions on Mobile Computing10.1109/TMC.2024.349793424:4(2800-2816)Online publication date: Apr-2025
https://doi.org/10.1109/TMC.2024.3497934
Sun ZLiu AXiong NZhang SWang T(2024)DNF-BLPP: An Effective Deep Neuro-Fuzzy based Bilateral Location Privacy-Preserving Scheme for Service in Spatiotemporal CrowdsourcingIEEE Transactions on Services Computing10.1109/TSC.2024.3451236(1-14)Online publication date: 2024
https://doi.org/10.1109/TSC.2024.3451236
Zhang SWang RWang HDeng ZYang ZWu D(2024)Bilateral Task-Driven Privacy-Preserving Data Acquisition for Crowdsensed Data TradingIEEE Internet of Things Journal10.1109/JIOT.2023.332438411:6(9766-9780)Online publication date: 15-Mar-2024
https://doi.org/10.1109/JIOT.2023.3324384
Show More Cited By

Index Terms

Secure and Lightweight Blockchain-based Truthful Data Trading for Real-Time Vehicular Crowdsensing
1. Security and privacy
  1. Cryptography
    1. Symmetric cryptography and hash functions
      1. Hash functions and message authentication codes

Recommendations

Secure Data Sharing over Vehicular Networks Based on Multi-sharding Blockchain
Internet of Vehicles (IoV) has become an indispensable technology to bridge vehicles, persons, and infrastructures and is promising to make our cities smarter and more connected. It enables vehicles to exchange vehicular data (e.g., GPS, sensors, and ...
Blockchain Based B-Health Prototype for Secure Healthcare Transactions
Big Data Analytics in Astronomy, Science, and Engineering
Abstract
The rapid advances in crypto wallet are re-defining privacy around transactions. A Crypto wallet comprises of software containing private and public keys and uses Blockchain to send and receive currency. Since the wallet will be based on the ...
A Flexible Instant Payment System Based on Blockchain
Information Security and Privacy
Abstract
Improving the throughput of blockchain systems such as Bitcoin and Ethereum has been an important research problem. Off-chain payments are one of the most promising technologies to tackle this challenge. Once a payment channel, however, is ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Transactions on Embedded Computing Systems

ACM Transactions on Embedded Computing Systems Volume 23, Issue 1

January 2024

406 pages

EISSN:1558-3465

DOI:10.1145/3613501

Editor:
Tulika Mitra
National University of Singapore, Singapore

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Journal Family

ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 10 January 2024

Online AM: 25 January 2023

Accepted: 17 January 2023

Revised: 17 January 2023

Received: 22 September 2022

Published in TECS Volume 23, Issue 1

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

National Natural Science Foundation of China
Fundamental Research Funds for the Central Universities
Qinchuangyuan Platform “Scientists+engineers” group building project

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

6
Total Citations
View Citations
660
Total Downloads

Downloads (Last 12 months)292
Downloads (Last 6 weeks)32

Reflects downloads up to 05 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Chen WYang WXiao MXue LWang S(2025)LBDT: A Lightweight Blockchain-Based Data Trading Scheme in Internet of Vehicles Using Proof-of-ReputationIEEE Transactions on Mobile Computing10.1109/TMC.2024.349793424:4(2800-2816)Online publication date: Apr-2025
https://doi.org/10.1109/TMC.2024.3497934
Sun ZLiu AXiong NZhang SWang T(2024)DNF-BLPP: An Effective Deep Neuro-Fuzzy based Bilateral Location Privacy-Preserving Scheme for Service in Spatiotemporal CrowdsourcingIEEE Transactions on Services Computing10.1109/TSC.2024.3451236(1-14)Online publication date: 2024
https://doi.org/10.1109/TSC.2024.3451236
Zhang SWang RWang HDeng ZYang ZWu D(2024)Bilateral Task-Driven Privacy-Preserving Data Acquisition for Crowdsensed Data TradingIEEE Internet of Things Journal10.1109/JIOT.2023.332438411:6(9766-9780)Online publication date: 15-Mar-2024
https://doi.org/10.1109/JIOT.2023.3324384
Xu HHe ZLan D(2024)Revolutionizing machine learning: Blockchain-based crowdsourcing for transparent and fair labeled datasets supplyFuture Generation Computer Systems10.1016/j.future.2024.06.061161(106-118)Online publication date: Dec-2024
https://doi.org/10.1016/j.future.2024.06.061
Agrawal ABhatia ATiwari K(2024)Enhancing Mobile Crowdsensing Security: A Proof of Stake-Based Publisher Selection Algorithm to Combat Sybil Attacks in Blockchain-Assisted MCS SystemsAdvanced Information Networking and Applications10.1007/978-3-031-57916-5_16(174-186)Online publication date: 9-Apr-2024
https://doi.org/10.1007/978-3-031-57916-5_16
Wu HWang SDong GZhou MLiu YZhang L(2023)Data Sharing System Based on Blockchain to Enhance The Visible of Meta Data Information in Privacy Computing2023 IEEE International Conference on High Performance Computing & Communications, Data Science & Systems, Smart City & Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys)10.1109/HPCC-DSS-SmartCity-DependSys60770.2023.00048(296-302)Online publication date: 17-Dec-2023
https://doi.org/10.1109/HPCC-DSS-SmartCity-DependSys60770.2023.00048

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Full Text

View this article in Full Text.

Figures

Tables

Media

View full text|Download PDF

View Issue’s Table of Contents