Articles Open Access
Privacy-Aware Healthcare Data Sharing using Blockchain and Federated Learning Integration Frameworks
R V College of Engineering, Bengaluru, India
Abstract
The increasing digitization of healthcare systems has led to unprecedented volumes of sensitive patient data being generated across hospitals, clinics, wearable devices, and telemedicine platforms. While this data holds significant potential for improving predictive analytics, personalized treatment, and population health management, it also raises critical concerns regarding privacy, security, and regulatory compliance. Traditional centralized data-sharing models expose healthcare systems to risks such as data breaches, unauthorized access, and single points of failure
This study explores a privacy-aware healthcare data sharing framework that integrates blockchain technology and federated learning to enable secure, decentralized, and privacy-preserving data collaboration. Blockchain provides an immutable, transparent, and auditable ledger for managing access control, consent management, and data provenance. Federated learning enables machine learning models to be trained across distributed healthcare datasets without transferring raw patient data, thereby preserving privacy while maintaining analytical utility
The proposed integration framework combines the strengths of both technologies to address key challenges in healthcare data interoperability, trust management, and regulatory compliance (e.g., HIPAA and GDPR). The study evaluates architectural models, security mechanisms, and communication protocols required for scalable deployment. The findings suggest that blockchain-enabled federated learning can significantly enhance data security, improve trust among stakeholders, and enable collaborative AI-driven healthcare innovation without compromising patient privacy
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Publication Information
| Journal | International Research Journal of Innovative Engineering |
|---|---|
| Volume | 6 |
| Number | 5 |
| Pages | 11047-11053 |
| Published | 2022-09-10 |
| Issue | Vol. 6 No. 5 (2022): International Research Journal of Innovative Engineering (IRJIE) |
References
1. Benet, J. (2014). IPFS - content addressed, versioned, P2P file system. arXiv preprint arXiv:1407.3561.
2. Bera, B., Das, A. K., & Dutta, R. (2020). Blockchain-enabled secure healthcare system with federated learning. IEEE Access, 8, 140457–140473.
3. Kuo, T. T., Kim, H. E., & Ohno-Machado, L. (2017). Blockchain distributed ledger technologies for biomedical and health care applications. Journal of the American Medical Informatics Association, 24(6), 1211–1220.
4. Li, W., Andre, J., & Gupta, B. (2020). Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine, 37(3), 50–60.
5. McMahan, H. B., Moore, E., Ramage, D., & Hampson, S. (2017). Communication-efficient learning of deep networks from decentralized data. Proceedings of AISTATS.
6. Mamoshina, P., Ojomoko, L., Yanovich, Y., Ostrovski, A., Botezatu, A., & Aliper, A. (2018). Converging blockchain and AI for healthcare. Nature Communications, 9, 514.
7. Nguyen, D. C., Ding, M., Pathirana, P. N., & Seneviratne, A. (2020). Blockchain and AI-based solutions for healthcare data sharing. IEEE Access, 8, 133357–133378.
8. Rieke, N., Hancox, J., Li, W., Milletarì, F., Roth, H. R., Albarqouni, S., … Cardoso, M. J. (2020). The future of digital health with federated learning. NPJ Digital Medicine, 3, 119.
9. Sharma, P. K., Moon, S. Y., & Park, J. H. (2019). Block-CHAIN: Blockchain-based secure storage and access control scheme for electronic health records. IEEE Access, 7, 136920–136937.
10. Zhang, P., Schmidt, D. C., White, J., & Lenz, G. (2018). Blockchain technology use cases in healthcare. Advances in Computers, 111, 1–41.
11. Zhou, J., & Chen, F. (2021). Privacy-preserving federated learning for healthcare applications. IEEE Transactions on Big Data, 7(4), 620–631.
12. Mathew, A., & Nair, A., & Decouth, J. R. (2019). Audience Behaviour Mining Using Data Analysis. International Journal of Data Structures, 5(2), 1-7.
13. Sudarsan, V., & Sugumar, R. (2018). Building a Distributed K-Means Model using Simple K-Means of Weka.
14. Murugeshwari, B., Jayakumar, C., & Sarukesi, K. (2012). Secure Multi Party Computation Technique for Classification Rule Sharing. International Journal of Computer Applications, 55(7).
15. Mathew, A. R. (2019). Cyber-infrastructure connections and smart gird security. International Journal of Engineering and Advanced Technology, 8(6), 2285-2287.
16. Sugumar, R., & Murugeshwari, B. (2016). An Efficient MChord based Authentication for Vehicular Ad-Hoc Networks.
17. Sudhan, S. K. H. H., & Kumar, S. S. (2016). Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection. Indian Journal of Science and Technology, 9, 44.
18. Sruthi, R. S., Ananya, S., & Murugeshwari, B. (2010). Web Based Virtual Control System Laboratory and On-Line Temperature Control of Electrophoresis Equipment using LabVIEW. International Journal of Computer Applications, 975, 8887.
19. Mathew, A. (2019). Cybersecurity infrastructure and security automation. Adv Comput: Int J (ACIJ), 10(6).
20. Sugumar, R. (2018). Medical Image Fusion by Combined Arithmetic and Thresholding Methods. EDITORS OF SPECIAL ISSUE JOURNAL, 17.
21. Balasubramanian, V., & Rajendran, S. (2019). Rough set theory-based feature selection and FGA-NN classifier for medical data classification. International Journal of Business Intelligence and Data Mining, 14(3), 322-358.
22. Brijet, Z., Kumar, B. S., & Bharathi, N. (2017). Vehicle anti-theft system using fingerprint recognition technique. Open Academic Journal of Advanced Science and Technology, 1(1), 36-41.
23. Mathew, A. R. Malware Analysis of API Calls using FPGA Hardware Level Security.
24. Sugumar, R. (2014). A technique to stock market prediction using fuzzy clustering and artificial neural networks.
25. Mathew, A., & Mai, C. (2018, May). Study of Various Data Recovery and Data Back Up Techniques in Cloud Computing & Their Comparison. In 2018 3rd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT) (pp. 2021-2024). IEEE.
26. Murugeshwari, B., Sudharson, K., Panimalar, S. P., Shanmugapriya, M., & Abinaya, M. (2020). SAFE–Secure Authentication in Federated Environment using CEG Key code.
27. Z. Brijet, N. Bharathi, G. Shanmugapriya. (2015). Design of Model Predictive Controller for Fluid Catalytic Cracking Unit. Fluid-IJCTA, 8(5), 1917-1926.
28. Singh, T. J., & Sugumar, R. (2012, December). An extensibility of THEMIS billing system for the cloud computing environment. In 2012 Fourth International Conference on Advanced Computing (ICoAC) (pp. 1-6). IEEE.
29. Mathew, A. (2020). Threat intelligence and internet of medical things (IoMT). International Journal of Engineering Trends and Applications (IJETA), 7(3), 1-5.
30. Amutha, M., & Sugumar, R. (2015). A survey on dynamic data replication system in cloud computing. International Journal of Innovative Research in Science, Engineering and Technology, 4(4), 1454-1467.
31. Sudarsan, V., & Sugumar, R. (2019). Building a distributed K‐Means model for Weka using remote method invocation (RMI) feature of Java. Concurrency and Computation: Practice and Experience, 31(14), e5313.
32. Jayaraman, S., Rajendran, S., & P, S. P. (2019). Fuzzy c-means clustering and elliptic curve cryptography using privacy preserving in cloud. International Journal of Business Intelligence and Data Mining, 15(3), 273-287.
33. Brijet, Z., & Bharathi, N. (2021). Design of type-2 fuzzy logic controller for fluid catalytic cracking unit. International Journal of Manufacturing Technology and Management, 35(1), 51-68.
34. Sudhan, S. K. H. H., & Kumar, S. S. (2015). An innovative proposal for secure cloud authentication using encrypted biometric authentication scheme. Indian Journal of Science and Technology, 8(35), 1-5.
35. Mathew, A. R. Airport Cyber Security and Cyber Resilience Controls. arXiv 2019. arXiv preprint arXiv:1908.09894.
36. Sulthana, A. R., & Murugeswari, B. (2011, March). ARIPSO: Association rule interactive postmining using schemas and ontologies. In 2011 International Conference on Emerging Trends in Electrical and Computer Technology (pp. 941-946). IEEE.
37. Chiranjeevi, K. G., Latha, R., & Kumar, S. S. (2016). Enlarge Storing Concept in an Efficient Handoff Allocation during Travel by Time Based Algorithm. Indian Journal of Science and Technology, 9, 40.
38. Priya, P. S., & Sugumar, R. (2014). Multi Keyword Searching Techniques over Encrypted Cloud Data. In IJSR.
39. Suresh, T., Brijet, Z., & Sheeba, T. B. (2021). CMVHHO-DKMLC: A Chaotic Multi Verse Harris Hawks optimization (CMV-HHO) algorithm based deep kernel optimized machine learning classifier for medical diagnosis. Biomedical Signal Processing and Control, 70, 103034
2. Bera, B., Das, A. K., & Dutta, R. (2020). Blockchain-enabled secure healthcare system with federated learning. IEEE Access, 8, 140457–140473.
3. Kuo, T. T., Kim, H. E., & Ohno-Machado, L. (2017). Blockchain distributed ledger technologies for biomedical and health care applications. Journal of the American Medical Informatics Association, 24(6), 1211–1220.
4. Li, W., Andre, J., & Gupta, B. (2020). Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine, 37(3), 50–60.
5. McMahan, H. B., Moore, E., Ramage, D., & Hampson, S. (2017). Communication-efficient learning of deep networks from decentralized data. Proceedings of AISTATS.
6. Mamoshina, P., Ojomoko, L., Yanovich, Y., Ostrovski, A., Botezatu, A., & Aliper, A. (2018). Converging blockchain and AI for healthcare. Nature Communications, 9, 514.
7. Nguyen, D. C., Ding, M., Pathirana, P. N., & Seneviratne, A. (2020). Blockchain and AI-based solutions for healthcare data sharing. IEEE Access, 8, 133357–133378.
8. Rieke, N., Hancox, J., Li, W., Milletarì, F., Roth, H. R., Albarqouni, S., … Cardoso, M. J. (2020). The future of digital health with federated learning. NPJ Digital Medicine, 3, 119.
9. Sharma, P. K., Moon, S. Y., & Park, J. H. (2019). Block-CHAIN: Blockchain-based secure storage and access control scheme for electronic health records. IEEE Access, 7, 136920–136937.
10. Zhang, P., Schmidt, D. C., White, J., & Lenz, G. (2018). Blockchain technology use cases in healthcare. Advances in Computers, 111, 1–41.
11. Zhou, J., & Chen, F. (2021). Privacy-preserving federated learning for healthcare applications. IEEE Transactions on Big Data, 7(4), 620–631.
12. Mathew, A., & Nair, A., & Decouth, J. R. (2019). Audience Behaviour Mining Using Data Analysis. International Journal of Data Structures, 5(2), 1-7.
13. Sudarsan, V., & Sugumar, R. (2018). Building a Distributed K-Means Model using Simple K-Means of Weka.
14. Murugeshwari, B., Jayakumar, C., & Sarukesi, K. (2012). Secure Multi Party Computation Technique for Classification Rule Sharing. International Journal of Computer Applications, 55(7).
15. Mathew, A. R. (2019). Cyber-infrastructure connections and smart gird security. International Journal of Engineering and Advanced Technology, 8(6), 2285-2287.
16. Sugumar, R., & Murugeshwari, B. (2016). An Efficient MChord based Authentication for Vehicular Ad-Hoc Networks.
17. Sudhan, S. K. H. H., & Kumar, S. S. (2016). Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection. Indian Journal of Science and Technology, 9, 44.
18. Sruthi, R. S., Ananya, S., & Murugeshwari, B. (2010). Web Based Virtual Control System Laboratory and On-Line Temperature Control of Electrophoresis Equipment using LabVIEW. International Journal of Computer Applications, 975, 8887.
19. Mathew, A. (2019). Cybersecurity infrastructure and security automation. Adv Comput: Int J (ACIJ), 10(6).
20. Sugumar, R. (2018). Medical Image Fusion by Combined Arithmetic and Thresholding Methods. EDITORS OF SPECIAL ISSUE JOURNAL, 17.
21. Balasubramanian, V., & Rajendran, S. (2019). Rough set theory-based feature selection and FGA-NN classifier for medical data classification. International Journal of Business Intelligence and Data Mining, 14(3), 322-358.
22. Brijet, Z., Kumar, B. S., & Bharathi, N. (2017). Vehicle anti-theft system using fingerprint recognition technique. Open Academic Journal of Advanced Science and Technology, 1(1), 36-41.
23. Mathew, A. R. Malware Analysis of API Calls using FPGA Hardware Level Security.
24. Sugumar, R. (2014). A technique to stock market prediction using fuzzy clustering and artificial neural networks.
25. Mathew, A., & Mai, C. (2018, May). Study of Various Data Recovery and Data Back Up Techniques in Cloud Computing & Their Comparison. In 2018 3rd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT) (pp. 2021-2024). IEEE.
26. Murugeshwari, B., Sudharson, K., Panimalar, S. P., Shanmugapriya, M., & Abinaya, M. (2020). SAFE–Secure Authentication in Federated Environment using CEG Key code.
27. Z. Brijet, N. Bharathi, G. Shanmugapriya. (2015). Design of Model Predictive Controller for Fluid Catalytic Cracking Unit. Fluid-IJCTA, 8(5), 1917-1926.
28. Singh, T. J., & Sugumar, R. (2012, December). An extensibility of THEMIS billing system for the cloud computing environment. In 2012 Fourth International Conference on Advanced Computing (ICoAC) (pp. 1-6). IEEE.
29. Mathew, A. (2020). Threat intelligence and internet of medical things (IoMT). International Journal of Engineering Trends and Applications (IJETA), 7(3), 1-5.
30. Amutha, M., & Sugumar, R. (2015). A survey on dynamic data replication system in cloud computing. International Journal of Innovative Research in Science, Engineering and Technology, 4(4), 1454-1467.
31. Sudarsan, V., & Sugumar, R. (2019). Building a distributed K‐Means model for Weka using remote method invocation (RMI) feature of Java. Concurrency and Computation: Practice and Experience, 31(14), e5313.
32. Jayaraman, S., Rajendran, S., & P, S. P. (2019). Fuzzy c-means clustering and elliptic curve cryptography using privacy preserving in cloud. International Journal of Business Intelligence and Data Mining, 15(3), 273-287.
33. Brijet, Z., & Bharathi, N. (2021). Design of type-2 fuzzy logic controller for fluid catalytic cracking unit. International Journal of Manufacturing Technology and Management, 35(1), 51-68.
34. Sudhan, S. K. H. H., & Kumar, S. S. (2015). An innovative proposal for secure cloud authentication using encrypted biometric authentication scheme. Indian Journal of Science and Technology, 8(35), 1-5.
35. Mathew, A. R. Airport Cyber Security and Cyber Resilience Controls. arXiv 2019. arXiv preprint arXiv:1908.09894.
36. Sulthana, A. R., & Murugeswari, B. (2011, March). ARIPSO: Association rule interactive postmining using schemas and ontologies. In 2011 International Conference on Emerging Trends in Electrical and Computer Technology (pp. 941-946). IEEE.
37. Chiranjeevi, K. G., Latha, R., & Kumar, S. S. (2016). Enlarge Storing Concept in an Efficient Handoff Allocation during Travel by Time Based Algorithm. Indian Journal of Science and Technology, 9, 40.
38. Priya, P. S., & Sugumar, R. (2014). Multi Keyword Searching Techniques over Encrypted Cloud Data. In IJSR.
39. Suresh, T., Brijet, Z., & Sheeba, T. B. (2021). CMVHHO-DKMLC: A Chaotic Multi Verse Harris Hawks optimization (CMV-HHO) algorithm based deep kernel optimized machine learning classifier for medical diagnosis. Biomedical Signal Processing and Control, 70, 103034
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