Blockchain-Based Federated Learning in Healthcare: A Secure and Privacy-Preserving Approach for Data Sharing and Analysis

By: Himanshu Tiwari, International Center for AI and Cyber Security Research and Innovations (CCRI), Asia University, Taiwan, nomails1337@gmail.com

Abstract:

The healthcare industry is an increasing number of adopting era-driven solutions to beautify affected person care, remedy outcomes, and scientific research. One such innovative technique is federated getting to know, which allows a couple of healthcare institutions to collaboratively examine and percentage information without exposing sensitive affected person data. In this article, we discover the combination of blockchain technology with federated studying to create a secure and privateness-retaining framework for healthcare data sharing and analysis. We discuss the advantages, challenges, and future prospects of this synergy, emphasizing its potential to revolutionize healthcare research at the same time as safeguarding affected person privateness.

1. Introduction

Healthcare establishments generate widespread quantities of affected person records every day, inclusive of scientific information, diagnostic pictures, and scientific notes.[1] Analyzing this facts can result in tremendous advancements in sickness detection, remedy, and healthcare delivery. However, sharing and aggregating this information in a secure and privacy-compliant manner poses a substantial venture.

Federated getting to know addresses those challenges via allowing collaborative model education on decentralized statistics sources. However, conventional federated getting to know strategies nevertheless require accept as true with amongst taking part agencies, raising issues approximately information privateness and protection. Blockchain generation gives a promising option to this trouble, providing a obvious, tamper-evidence, and decentralized ledger for stable statistics sharing and get entry to manage.

2. Federated Learning in Healthcare

Federated learning is a device studying paradigm that permits a couple of companies to build a shared version without sharing their uncooked information. In healthcare, this method enables institutions to collaborate on medical studies with out compromising patient privacy.[2] [3] Instead of centralizing information in a single area, each organisation trains a nearby version on its records and stocks most effective model updates with the vital server.

Key advantages of federated gaining knowledge of in healthcare include:

– Privacy-upkeep: Patient records remains decentralized and by no means leaves the local institution, reducing the risk of statistics breaches or unauthorized get entry to.

– Collaboration: Multiple institutions can work collectively on large-scale studies tasks without the want for information sharing agreements.

– Data range: Federated studying lets in access to various information assets, leading to greater sturdy and generalizable fashions.

3. Blockchain Technology in Healthcare

Blockchain technology has won prominence in healthcare because of its capability to offer stable and obvious statistics control. Key traits of blockchain in healthcare consist of:

– Immutability: Once records is recorded on the blockchain, it can’t be altered, making sure the integrity of scientific records.

– Decentralization: Blockchain operates on a dispensed community, getting rid of the need for a central authority and reducing the hazard of facts manipulation.

– Smart contracts: Self-executing clever contracts can automate records sharing agreements and get right of entry to manage, lowering administrative overhead.

4. Integration of Blockchain and Federated Learning

The integration of blockchain and federated studying in healthcare gives a singular method to cope with information privacy and safety issues. This aggregate guarantees the subsequent benefits[4] [5]:

– Secure data sharing: Blockchain affords a steady and obvious ledger for tracking information get entry to and sharing, lowering the chance of information breaches.

– Data provenance: Blockchain information the starting place and change history of every data factor, enhancing information traceability and responsibility.

– Access control: Smart contracts on the blockchain can automate access manipulate rules, making sure that best legal events can take part in federated getting to know.

– Patient consent control: Blockchain can facilitate consent management via allowing patients to control get admission to to their data through immutable consent records.

5. Challenges and Future Directions

Despite the promising ability of blockchain-based federated mastering in healthcare, numerous challenges need to be addressed:

– Scalability: As healthcare statistics keeps to grow, making sure the scalability of this method is crucial.

– Interoperability: Integration with current healthcare systems and requirements can be complex.

– Regulatory compliance: Ensuring compliance with records protection policies, consisting of HIPAA inside the United States, is vital.

In the future, we anticipate improvements in blockchain generation, consisting of stepped forward scalability and interoperability, so as to make blockchain-primarily based federated getting to know an excellent greater appealing alternative for healthcare institutions. Additionally, regulatory frameworks may evolve to accommodate the specific necessities of this technique.

6. Conclusion

Blockchain-primarily based federated studying gives a ground-breaking answer for the stable and privacy-keeping sharing and analysis of healthcare facts. By combining the strengths of federated gaining knowledge of with the security and transparency of blockchain technology, healthcare establishments can collaborate on research, in the end leading to progressed patient care and medical advancements, all even as maintaining the very best requirements of information privacy and security. As this area keeps to conform, we anticipate it to play a pivotal position in the future of healthcare innovation.

References:

1. ACM Digital Library. (n.d.). ACM Digital Library. https://doi.org/10.1145/3501813
2. Xu, J., Glicksberg, B. S., Su, C., Walker, P., Bian, J., & Wang, F. (2020, November 12). Federated Learning for Healthcare Informatics – Journal of Healthcare Informatics Research. SpringerLink. https://doi.org/10.1007/s41666-020-00082-4
3. ACM Digital Library. (n.d.). ACM Digital Library. https://doi.org/10.1145/3501296
4. Lu, Y., Huang, X., Dai, Y., Maharjan, S., & Zhang, Y. (2020, June). Blockchain and Federated Learning for Privacy-Preserved Data Sharing in Industrial IoT. IEEE Transactions on Industrial Informatics, 16(6), 4177–4186. https://doi.org/10.1109/tii.2019.2942190
5. Qu, Y., Uddin, M. P., Gan, C., Xiang, Y., Gao, L., & Yearwood, J. (2022, November 21). Blockchain-enabled Federated Learning: A Survey. ACM Computing Surveys, 55(4), 1–35. https://doi.org/10.1145/3524104
6. Mishra, A., Gupta, N., & Gupta, B. B. (2021). Defense mechanisms against DDoS attack based on entropy in SDN-cloud using POX controller. Telecommunication systems, 77, 47-62.
7. Nguyen, G. N., Le Viet, N. H., Elhoseny, M., Shankar, K., Gupta, B. B., & Abd El-Latif, A. A. (2021). Secure blockchain enabled Cyber–physical systems in healthcare using deep belief network with ResNet model. Journal of parallel and distributed computing, 153, 150-160.
8. Elgendy, I. A., Zhang, W. Z., He, H., Gupta, B. B., & Abd El-Latif, A. A. (2021). Joint computation offloading and task caching for multi-user and multi-task MEC systems: reinforcement learning-based algorithms. Wireless Networks, 27(3), 2023-2038.
9. Masud, M., Gaba, G. S., Alqahtani, S., Muhammad, G., Gupta, B. B., Kumar, P., & Ghoneim, A. (2020). A lightweight and robust secure key establishment protocol for internet of medical things in COVID-19 patients care. IEEE Internet of Things Journal, 8(21), 15694-15703.
10. Kumar, N., Poonia, V., Gupta, B. B., & Goyal, M. K. (2021). A novel framework for risk assessment and resilience of critical infrastructure towards climate change. Technological Forecasting and Social Change, 165, 120532.

Cite As

Tiwari H. (2023) Blockchain-Based Federated Learning in Healthcare: A Secure and Privacy-Preserving Approach for Data Sharing and Analysis Insights2Techinfo, pp.1

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