PD2S: A Privacy-Preserving Differentiated Data Sharing Scheme Based on Blockchain and Federated Learning
Abstract
With the rapid development of the Internet of Things (IoT), massive terminals and emerging applications bring a large amount of data. As an essential prerequisite for mining potential value from these data, data sharing is crucial and getting more and more attention. However, security and privacy concerns, and the lack of effective incentive mechanisms in the decentralized environment hinder the data owners from sharing their data. In this article, we propose a federated learning (FL) and blockchain-based privacy-preserving data sharing system, which solve the privacy leakage problem of FL and the scalability problem of blockchain by their technical complementarity. Considering the resource shortage and the heterogeneity of terminals, we first design a cross-layer architecture by exploiting the cloud–edge–terminal collaboration, and innovatively propose differential data sharing by dividing the providers into origin data providers and model providers. Then, we design a targeted incentive mechanism to maximize the utility of requesters and two types of data providers by formulating it into a two-stage Stackelberg game. Finally, a gradient-based algorithm is designed to get the optimal solution. Theoretical analysis and numerical simulations show that the proposed data-sharing system could eliminate privacy concerns and motivate more participants to share data. With 500 nodes, the differentiated data sharing scheme (DASC) is 1.72 s faster than the shared model scheme (SMC) and 2.59 s faster than SDC. In terms of utility of the requester, DASC is$2.0 \times {10^{3}}$higher than SDC, and 9.27 higher than SMC in average utility of providers.
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Federated learning handles device failures and data corruption robustly, continuing with minimal disruption because it relies on numerous other devices that persist in their local computations.
“Additionally, FL is designed to handle device failures and data corruption robustly.Should a device go offline or experience data corruption, the FL process continues with minimal disruption, as it relies on numerous other devices that persist in their local computations.”
From Federated learning meets blockchain in decentralized data-sharing: Healthcare use case (2024) · cites this paper as [61]
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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
- Liu, Y., Liu, P., Jing, W., & Song, H. H.
- Affiliations
- Northeast Forestry University (CN)
- University of Maryland, Baltimore County (US)
- Venue
- IEEE Internet of Things Journal
- Published
- 2023-12-15
- Volume
- 10
- Issue
- 24
- Pages
- 21489-21501
- ISSN
- 2327-4662 (ISSN-L)
- 2372-2541
- OpenAlex
- W4384284141
- Cited by
- 36
- References
- 33
- Citations by year
- 2026: 5 · 2025: 12 · 2024: 15 · 2023: 4
- FWCI
- 8.45
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Internet of Things Journal (publishedVersion)
- Funders
- Natural Science Foundation of Heilongjiang Province of China (LH2022F003)
- Topics
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Keywords
- privacy-preserving data sharing, federated learning, blockchain, Internet of Things, incentive mechanisms, Stackelberg game, cross-layer architecture, scalability, privacy leakage
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