A Secure Softwarized Blockchain-based Federated Health Alliance for Next Generation IoT Networks
Abstract
Medical health care centres are seen as a potential paradigm for dealing with large amounts of data utilizing artificial intelligence (AI). The fast growth in the massive volume of data created by connected devices in the health paradigm brings up new opportunities for data sharing to improve the quality of service for developing applications. Traditional AI approaches frequently need centralized data gathering and model training within a single company, which is a significant flaw owing to the lack of privacy and security of raw data transfer through mobile networks. The leakage of sensitive data can result in substantial consequences for the providers, in addition to financial loss. For that, we first build a blockchain-enabled safe data sharing architecture for distributed multiple parties in this paper. Then, by adding suggested privacy-preserved federated learning, we turn the data-sharing problem into a machine-learning challenge. Recommendations are based on the user’s previous search values, and data privacy is protected by providing the data model rather than the raw figures. Finally, we include federated learning into the permissioned blockchain consensus process, allowing the consensus computing effort to be used for federated training. According to numerical findings generated from data sets, the suggested data sharing method delivers good accuracy, high efficiency, and increased security.
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A blockchain-enabled collaborative federated learning architecture using differential privacy for safeguarding user data and blockchain for secure sharing of data provides security to processes requiring data sharing, per theoretical analysis.
“A blockchain-enabled collaborative FL architecture is proposed that uses DP for safeguarding user data and blockchain for secure sharing of data [30]. The theoretical analysis showed that the system provides security to the processes that require sharing of data.”
From A Review of Privacy Enhancement Methods for Federated Learning in Healthcare Systems (2023) · cites this paper as [30]
DP poses all relevant characteristics to mitigate all risks associated with information leakage during model parameter exchange.
“DP also poses all relevant characteristics to mitigate all risks associated with information leakage during model parameter exchange [26,30,32].”
From A Review of Privacy Enhancement Methods for Federated Learning in Healthcare Systems (2023) · cites this paper as [30]
Cites
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Realizing the Heterogeneity: A Self-Organized Federated Learning Framework for IoT
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Optimized Energy Efficient Secure Routing Protocol for Wireless Body Area Network
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QoS-aware secure transaction framework for internet of things using blockchain mechanism
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Self-citationDROPS: Dynamic Radio Protocol Selection for Energy-Constrained Wearable IoT Healthcare
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Myrzashova, R., Alsamhi, S. H., Shvetsov, A. V., Hawbani, A., & Wei, X. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3263598 · Cited by 178 · see citing works
A Review of Privacy Enhancement Methods for Federated Learning in Healthcare Systems
Gu, X., Sabrina, F., Fan, Z., & Sohail, S. (2023) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph20156539 · Cited by 139 · see citing works
- Authors
- Das, P., Singh, M., & Roy, D. G.
- Affiliations
- Indian Institute of Science Bangalore (IN)
- Venue
- 2021 IEEE Globecom Workshops (GC Wkshps)
- Publisher
- IEEE
- Published
- 2021-12
- Pages
- 1-6
- OpenAlex
- W4206924239
- Cited by
- 19
- References
- 11
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 6 · 2023: 5 · 2022: 2
- FWCI
- 2.70
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Globecom Workshops (GC Wkshps) (publishedVersion)
- 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 › Information Systems › Internet of Things and AI
- Keywords
- blockchain, federated learning, privacy-preserving data sharing, health data security, data privacy, health data sharing, softwarized networks
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