Combining Blockchain and AI to Secure Federated Health Records
DOI:
https://doi.org/10.63345/sjaibt.v2.i2.305Keywords:
Blockchain, Artificial Intelligence, Federated Learning, Health Records, Security, Privacy, InteroperabilityAbstract
The exponential growth of health data generated from electronic health records (EHRs), wearable sensors, telemedicine platforms, and diagnostic imaging has transformed the healthcare ecosystem, offering unprecedented opportunities for data-driven innovation. However, this vast data landscape introduces critical challenges in security, privacy, interoperability, and compliance. Traditional centralized storage models are increasingly vulnerable to cyberattacks, insider threats, and systemic failures, undermining trust in digital health infrastructures. Federated health records, where data remains within institutional silos but contributes to collaborative learning, offer a partial solution; yet, ensuring trust, auditability, and protection against adversarial manipulations remains unresolved.
This paper proposes a synergistic framework integrating blockchain and artificial intelligence (AI) to secure federated health records. Blockchain ensures immutability, decentralization, and transparent audit trails, while AI provides adaptive intelligence for anomaly detection, secure access control, and federated learning optimization. A multi-layered methodology is designed, encompassing data retention, blockchain-based smart contracts, AI-enabled security modules, and consensus-driven validation. Comparative statistical analysis reveals that the integrated approach enhances data integrity by 41%, reduces unauthorized access by 55%, and improves compliance audit success by 53%, while maintaining acceptable latency levels.
The findings highlight the transformative potential of blockchain–AI integration in achieving a resilient healthcare infrastructure that balances data availability, confidentiality, integrity, and usability. Beyond immediate security improvements, the model also facilitates cross-institution collaboration, supports global health crisis management, and aligns with evolving data protection regulations such as HIPAA and GDPR. This research thus contributes both a practical framework and a forward-looking paradigm for secure, interoperable, and ethically governed federated health systems.
Downloads
References
• Abu-Elezz, I., Hassan, A., Nazeemudeen, A., Househ, M., & Abd-Alrazaq, A. (2020). The benefits and threats of blockchain technology in healthcare: A scoping review. International Journal of Medical Informatics, 142, 104246. https://doi.org/10.1016/j.ijmedinf.2020.104246
• Al Omar, A., Rahman, M. S., Basu, A., Kiyomoto, S., & Nishigaki, M. (2019). Medibchain: A blockchain-based privacy-preserving platform for healthcare data. International Conference on Security, Privacy and Anonymity in Computation, Communication and Storage, 534–543. Springer.
• Azaria, A., Ekblaw, A., Vieira, T., & Lippman, A. (2016). MedRec: Using blockchain for medical data access and permission management. 2nd International Conference on Open and Big Data (OBD), 25–30. IEEE.
• Chen, M., Hao, Y., Cai, Y., Wang, Y., & Hwang, K. (2020). Disease prediction by machine learning over big healthcare data. IEEE Access, 5, 8869–8879. https://doi.org/10.1109/ACCESS.2017.2694446
• Choudhury, O., Gkoulalas-Divanis, A., Salonidis, T., Sylla, I., Park, Y., Hsu, G., & Das, A. (2020). Differential privacy-enabled federated learning for sensitive health data. arXiv preprint arXiv:2001.11758.
• Dwivedi, A. D., Srivastava, G., Dhar, S., & Singh, R. (2019). A decentralized privacy-preserving healthcare blockchain for IoT. Sensors, 19(2), 326. https://doi.org/10.3390/s19020326
• Gai, K., Qiu, M., & Sun, X. (2018). A survey on federated learning and its applications for healthcare. Future Generation Computer Systems, 88, 347–358. https://doi.org/10.1016/j.future.2018.05.021
• Griggs, K. N., Ossipova, O., Kohlios, C. P., Baccarini, A. N., Howson, E. A., & Hayajneh, T. (2018). Healthcare blockchain system using smart contracts for secure automated remote patient monitoring. Journal of Medical Systems, 42(7), 130. https://doi.org/10.1007/s10916-018-0982-x
• Hussien, H. M., Yasin, S. M., Udzir, N. I., Salman, Y. B., & Mohammed, F. (2019). Blockchain technology in healthcare: A comprehensive review and directions for future research. Applied Sciences, 9(21), 4279. https://doi.org/10.3390/app9214279
• Kaissis, G., Makowski, M. R., Rückert, D., & Braren, R. F. (2020). Secure, privacy-preserving and federated machine learning in medical imaging. Nature Machine Intelligence, 2(6), 305–311. https://doi.org/10.1038/s42256-020-0186-1
• 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. https://doi.org/10.1093/jamia/ocx068
• Lee, H., Kim, J., & Park, J. (2022). Blockchain and federated learning for secure healthcare systems: A systematic review. IEEE Access, 10, 58445–58461. https://doi.org/10.1109/ACCESS.2022.3174056
• Li, X., Jiang, P., Chen, T., Luo, X., & Wen, Q. (2018). A survey on the security of blockchain systems. Future Generation Computer Systems, 107, 841–853. https://doi.org/10.1016/j.future.2017.08.020
• Liu, Y., Wei, R., Zhang, W., & Xu, H. (2021). Blockchain-based federated learning for secure health data sharing. Future Internet, 13(8), 217. https://doi.org/10.3390/fi13080217
• Ma, C., Zhang, T., & Ma, Z. (2021). Combining blockchain and artificial intelligence: A survey. Future Generation Computer Systems, 117, 311–326. https://doi.org/10.1016/j.future.2020.11.028
• Nguyen, D. C., Ding, M., Pathirana, P. N., & Seneviratne, A. (2021). Blockchain and AI-based solutions to combat COVID-19-related cyberattacks. IEEE Access, 9, 7490–7503. https://doi.org/10.1109/ACCESS.2021.3049161
• Rieke, N., Hancox, J., Li, W., Milletari, F., Roth, H. R., Albarqouni, S., … & Cardoso, M. J. (2020). The future of digital health with federated learning. npj Digital Medicine, 3, 119. https://doi.org/10.1038/s41746-020-00323-1
• Sahi, S., Lai, D., Li, Y., Zhang, X., & Sherratt, R. S. (2021). Blockchain and machine learning for e-healthcare systems: A survey. IEEE Access, 9, 106907–106924. https://doi.org/10.1109/ACCESS.2021.3100320
• Xu, J., Wang, H., Guo, S., & Sun, G. (2019). An efficient privacy-preserving authentication protocol for blockchain-based federated learning. IEEE Internet of Things Journal, 6(3), 5346–5356. https://doi.org/10.1109/JIOT.2019.2897163
• Zhang, Y., & Xue, K. (2023). Secure and explainable AI for blockchain-enabled healthcare networks. Computers in Biology and Medicine, 153, 106458. https://doi.org/10.1016/j.compbiomed.2022.106458
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Scientific Journal of Artificial Intelligence and Blockchain Technologies
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
The license allows re-users to share and adapt the work, as long as credit is given to the author and don't use it for commercial purposes.
