A Survey of Blockchain-Based Schemes for Data Sharing and Exchange
Abstract
Data immutability, transparency and decentralization of blockchain make it widely used in various fields, such as Internet of things, finance, energy and healthcare. With the advent of the Big Data era, various companies and organizations urgently need data from other parties for data analysis and mining to provide better services. Therefore, data sharing and data exchange have become an enormous industry. Traditional centralized data platforms face many problems, such as privacy leakage, high transaction costs and lack of interoperability. Introducing blockchain into this field can address these problems, while providing decentralized data storage and exchange, access control, identity authentication and copyright protection. Although many impressive blockchain-based schemes for data sharing or data exchange scenarios have been presented in recent years, there is still a lack of review and summary of work in this area. In this paper, we conduct a detailed survey of blockchain-based data sharing and data exchange platforms, discussing the latest technical architectures and research results in this field. In particular, we first survey the current blockchain-based data sharing solutions and provide a detailed analysis of system architecture, access control, interoperability, and security. We then review blockchain-based data exchange systems and data marketplaces, discussing trading process, monetization, copyright protection and other related topics.
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Federated learning enhances system robustness by ensuring efficient data utilization even in environments with limited network connectivity.
“In terms of resilience, FL enhances system robustness through various means.For instance, it ensures efficient data utilization even in environments with limited network connectivity [60].”
From Federated learning meets blockchain in decentralized data-sharing: Healthcare use case (2024) · cites this paper as [60]
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Bootstrapping a Blockchain Based Ecosystem for Big Data Exchange
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Flagged first, then by citations
Cited by
Sorted by citations
Federated learning meets blockchain in decentralized data-sharing: Healthcare use case
Alsamhi, S. H., Myrzashova, R., Hawbani, A., Kumar, S., Srivastava, S., Zhao, L., Wei, X., Guizan, M., & Curry, E. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3367249 · Cited by 131 · see citing works
- Authors
- Song, R., Xiao, B., Song, Y., Guo, S., & Yang, Y.
- Affiliations
- Hong Kong Polytechnic University (HK)
- Southeast University (CN)
- Chongqing University (CN)
- Stony Brook University (US)
- Venue
- IEEE Transactions on Big Data
- Published
- 2023-12
- Volume
- 9
- Issue
- 6
- Pages
- 1477-1495
- ISSN
- 2332-7790 (ISSN-L)
- 2372-2096
- OpenAlex
- W4383503718
- Cited by
- 59
- References
- 124
- Citations by year
- 2026: 13 · 2025: 20 · 2024: 26
- FWCI
- 13.85
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Big Data (publishedVersion)
- Funders
- NSFC/RGC Joint Research Scheme (N_PolyU529/22)
- HK RGC GRF PolyU (15209822)
- Topics
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology in Education and Learning
- Physical Sciences › Computer Science › Information Systems › Big Data and Digital Economy
- Keywords
- blockchain, data sharing, data exchange, data markets, access control, interoperability, data security, copyright protection, decentralized storage, identity verification, data privacy, monetization, system architecture
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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