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A Privacy-Preserving Blockchain Platform for a Data Marketplace

Published: 14 March 2023 Publication History

Abstract

Recent data leak scandals, together with the under-utilization of collected data (estimated that around 90% of data never leaves a device’s local storage), limits the applicability and potential of novel data driven applications. Thus, novel ways to treat data, in which users are guaranteed control, usability, and privacy over their generated data are needed. In this paper we propose a novel privacy-preserving blockchain framework for a data sharing marketplace. The proposed framework allows users (such as sensors and devices) that generate data to store it in external servers, while the blockchain is utilized to record buy and sell transactions between parties, as well as perform access control by generating access sequences whenever trades are performed. A novel perspective over data ownership is presented, in which whoever generates the data has completed ownership and control over it and the blockchain transactions are only utilized to guarantee temporary access to it. The proposed blockchain framework also supports different types of data and provides, via the distributed and openness of the framework, quality, timeliness and similarity control over the data stored in the marketplace. In this context, different types of applications that can benefit from this framework are presented and open problems are discussed.

References

[1]
([n. d.]). Geth Documentation. https://geth.ethereum.org/docs/.
[2]
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).
[3]
Prabal Banerjee and Sushmita Ruj. 2018. Blockchain enabled data marketplace–design and challenges. arXiv preprint arXiv:1811.11462 (2018).
[4]
Suzhi Bi, Rui Zhang, Zhi Ding, and Shuguang Cui. 2015. Wireless communications in the era of big data. IEEE Communications Magazine 53, 10 (2015), 190–199.
[5]
Bin Cao, Yixin Li, Lei Zhang, Long Zhang, Shahid Mumtaz, Zhenyu Zhou, and Mugen Peng. 2019. When Internet of Things meets blockchain: Challenges in distributed consensus. IEEE Network 33, 6 (2019), 133–139.
[6]
Qian Chen, Gautam Srivastava, Reza M. Parizi, Moayad Aloqaily, and Ismaeel Al Ridhawi. 2020. An incentive-aware blockchain-based solution for internet of fake media things. Information Processing & Management 57, 6 (2020), 102370.
[7]
Konstantinos Christidis and Michael Devetsikiotis. 2016. Blockchains and smart contracts for the Internet of Things. IEEE Access 4 (2016), 2292–2303.
[8]
Mateus S. H. Cruz, Toshiyuki Amagasa, Chiemi Watanabe, Wenjie Lu, and Hiroyuki Kitagawa. 2017. Secure similarity joins using fully homomorphic encryption. In Proceedings of the 19th International Conference on Information Integration and Web-Based Applications and Services (iiWAS’17). Association for Computing Machinery, New York, NY, USA, 224–233. DOI:
[9]
Drasco Draskovic and George Saleh. 2017. Datapace-decentralized data marketplace based on blockchain. White paper. Verfügbar unter https://www.datapace.io/datapace_whitepaper.pdf, zuletzt geprüft am 13 (2017), 2019.
[10]
IDC. 2019. The growth in connected IoT devices is expected to generate 79.4 ZB of data in 2025, according to a new IDC forecast. (2019).
[11]
Heather Johnson. 2015. Digging up dark data: What puts IBM at the forefront of insight economy. SiliconANGLE, October 30 (2015).
[12]
Noah Johnson. 2019. Building a secure data market on blockchain. USENIX Association, Burlingame, CA.
[13]
Paulo Valente Klaine, Muhammad Ali Imran, Oluwakayode Onireti, and Richard Demo Souza. 2017. A survey of machine learning techniques applied to self-organizing cellular networks. IEEE Communications Surveys & Tutorials 19, 4 (2017), 2392–2431. DOI:
[14]
Paulo Valente Klaine, Lei Zhang, Bingpeng Zhou, Yao Sun, Hao Xu, and Muhammad Imran. 2020. Privacy-preserving contact tracing and public risk assessment using blockchain for COVID-19 pandemic. IEEE Internet of Things Magazine 3, 3 (2020), 58–63.
[15]
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. 2015. Deep learning. Nature 521, 7553 (2015), 436–444.
[16]
Tian Li, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith. 2020. Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine 37, 3 (2020), 50–60.
[17]
Y. Lu, X. Huang, Y. Dai, S. Maharjan, and Y. Zhang. 2020. Blockchain and federated learning for privacy-preserved data sharing in industrial IoT. IEEE Transactions on Industrial Informatics 16, 6 (2020), 4177–4186.
[18]
Robert A. J. Matthews. 1993. The use of genetic algorithms in cryptanalysis. Cryptologia 17, 2 (1993), 187–201. DOI:arXiv:https://doi.org/10.1080/0161-119391867863
[19]
Satoshi Nakamoto. 2019. Bitcoin: A Peer-to-Peer Electronic Cash System. Technical Report. Manubot.
[20]
Lam Duc Nguyen, Israel Leyva-Mayorga, Amari N. Lewis, and Petar Popovski. 2021. Modeling and analysis of data trading on blockchain-based market in IoT networks. IEEE Internet of Things Journal 8, 8 (2021), 6487–6497.
[21]
Aafaf Ouaddah, Anas Abou Elkalam, and Abdellah Ait Ouahman. 2016. FairAccess: A new blockchain-based access control framework for the Internet of Things. Security and Communication Networks 9, 18 (2016), 5943–5964.
[22]
Kazim Rifat Özyilmaz, Mehmet Doğan, and Arda Yurdakul. 2018. IDMoB: IoT data marketplace on blockchain. In 2018 Crypto Valley Conference on Blockchain Technology (CVCBT’18). IEEE, 11–19.
[23]
EU Blockchain Observatory and Forum. 2020. Convergence of Blockchain AI and IoT. Technical Report. 1–26 pages. https://www.eublockchainforum.eu/sites/default/files/report_convergence_v1.0.pdf.
[24]
IBM Corporation and GTD Solution Inc. 2020. TradeLens Data Sharing Specification: Data Sharing Model. Technical Report. 1–11 pages. https://docs.tradelens.com/reference/DSS_Data_Sharing_Model_V4.0.pdf.
[25]
C. E. Shannon. 1949. Communication theory of secrecy systems. The Bell System Technical Journal 28, 4 (1949), 656–715. DOI:
[26]
Hemang Subramanian. 2017. Decentralized blockchain-based electronic marketplaces. Commun. ACM 61, 1 (2017), 78–84.
[27]
Y. Sun, L. Zhang, G. Feng, B. Yang, B. Cao, and M. A. Imran. 2019. Blockchain-enabled wireless Internet of Things: Performance analysis and optimal communication node deployment. IEEE Internet of Things Journal 6, 3 (2019), 5791–5802. DOI:
[28]
Sarah Underwood. 2016. Blockchain beyond Bitcoin. Commun. ACM 59, 11 (2016), 15–17.
[29]
Qin Wang, Rujia Li, Qi Wang, and Shiping Chen. 2021. Non-fungible token (NFT): Overview, evaluation, opportunities and challenges. arXiv preprint arXiv:2105.07447 (2021).
[30]
Hao Xu, Paulo Valente Klaine, Oluwakayode Onireti, Bin Cao, Muhammad Imran, and Lei Zhang. 2020. Blockchain-enabled resource management and sharing for 6G communications. arXiv preprint arXiv:2003.13083 (2020).
[31]
Hao Xu, Lei Zhang, Oluwakayode Onireti, Yang Fang, William J. Buchanan, and Muhammad Ali Imran. 2020. BeepTrace: Blockchain-enabled privacy-preserving contact tracing for COVID-19 pandemic and beyond. IEEE Internet of Things Journal (2020).
[32]
Hao Xu, Lei Zhang, Elaine Sun, and Chih-Lin I. 2021. BE-RAN: Blockchain-enabled open RAN with decentralized identity management and privacy-preserving communication. IEEE Journal on Selected Areas in Communications Special Issue on Private Information Retrieval, Private Coded Computing over Distributed Servers, and Privacy in Distributed Learning (Jan.2021). arxiv:2101.10856http://arxiv.org/abs/2101.10856.
[33]
Jun Zhang, Shiqing Hu, and Zoe Lin Jiang. 2020. Privacy-preserving similarity computation in cloud-based mobile social networks. IEEE Access 8 (2020), 111889–111898. DOI:
[34]
G. Zyskind, O. Nathan, and A. S. Pentland. 2015. Decentralizing privacy: Using blockchain to protect personal data. In 2015 IEEE Security and Privacy Workshops. 180–184.
[35]
Guy Zyskind, Oz Nathan, and Alex Pentland. 2015. Enigma: Decentralized computation platform with guaranteed privacy. arXiv preprint arXiv:1506.03471 (2015).

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  • (undefined)AI and Blockchain Enabled Future Wireless Networks: A Survey And OutlookDistributed Ledger Technologies: Research and Practice10.1145/3644369

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Published In

cover image Distributed Ledger Technologies: Research and Practice
Distributed Ledger Technologies: Research and Practice  Volume 2, Issue 1
March 2023
190 pages
EISSN:2769-6480
DOI:10.1145/3587886
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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 March 2023
Online AM: 06 December 2022
Accepted: 10 November 2022
Revised: 14 October 2022
Received: 07 February 2022
Published in DLT Volume 2, Issue 1

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Author Tags

  1. Blockchain
  2. big data
  3. Internet of Things
  4. marketplace

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  • (undefined)AI and Blockchain Enabled Future Wireless Networks: A Survey And OutlookDistributed Ledger Technologies: Research and Practice10.1145/3644369

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