Privacy Preserving Federated Learning for Real-Time Threat Detection in Cloud-Native Healthcare Systems Using Zero Trust Access Control
Keywords:
Federated Learning, Privacy Preservation, Zero Trust Architecture, Cloud-Native Healthcare, Threat Detection, CybersecurityAbstract
The rapid adoption of cloud-native architectures in healthcare has significantly improved scalability, interoperability, and real-time data access. However, these advantages also introduce complex security and privacy challenges, particularly due to sensitive patient data and sophisticated cyber threats. This short research paper proposes a Privacy Preserving Federated Learning (PPFL) framework integrated with Zero Trust Access Control (ZTAC) for real-time threat detection in cloud-native healthcare systems. By enabling decentralized model training without direct data sharing, federated learning ensures compliance with healthcare privacy regulations while maintaining robust threat intelligence. Zero Trust principles further strengthen access control by continuously validating identities, devices, and behavioral context. The paper reviews prior literature, presents a conceptual system architecture, and highlights performance, security, and privacy benefits. The study contributes a unified approach that balances real-time detection accuracy with stringent privacy and trust requirements in modern healthcare infrastructures.
References
McMahan, Brendan, et al. “Communication-Efficient Learning of Deep Networks from Decentralized Data.” Journal of Machine Learning Research, vol. 18, no. 1, 2017, pp. 1–25.
Kairouz, Peter, et al. “Advances and Open Problems in Federated Learning.” Foundations and Trends in Machine Learning, vol. 14, no. 1, 2021, pp. 1–210.
Gundaboina, A. (2022). Quantum Computing and Cloud Security: Future-Proofing Healthcare Data Protection. International Journal for Multidisciplinary Research, 4(4), 1–12. https://doi.org/10.36948/ijfmr.2022.v04i04.61014
Sheller, Micah J., et al. “Federated Learning in Medicine: Facilitating Multi-Institutional Collaborations without Sharing Patient Data.” Medical Image Analysis, vol. 63, no. 2, 2020, pp. 101692–101705.
Geyer, Robin C., Tassilo Klein, and Moin Nabi. “Differentially Private Federated Learning: A Client-Level Perspective.” Proceedings of Neural Information Processing Systems, vol. 31, no. 1, 2018, pp. 1–12.
Gundaboina A. DevSecOps in Healthcare: Building Secure and Compliant Patient Engagement Applications. J Artif Intell Mach Learn & Data Sci 2024 2(4), 3052-3059. DOI: doi.org/10.51219/JAIMLD/anjan-gundaboina/629
Bonawitz, Keith, et al. “Towards Federated Learning at Scale: System Design.” Proceedings of the ACM Conference on Computer and Communications Security, vol. 26, no. 4, 2019, pp. 1–15.
Kindervag, John. “Build Security into Your Network’s DNA: The Zero Trust Network Architecture.” Forrester Research Report, vol. 1, no. 1, 2010, pp. 1–15.
Rose, Scott, et al. “Zero Trust Architecture.” NIST Special Publication, vol. 800, no. 207, 2020, pp. 1–62.
Gundaboina, A. (2024). HITRUST Certification Best Practices: Streamlining Compliance for Healthcare Cloud Solutions. International Journal of Computer Science and Information Technology Research, 5(1), 76–94. https://ijcsitr.org/index.php/home/article/view/IJCSITR_2024_05_01_008
Ward, Joy, and Betsy Beyer. “BeyondCorp: A New Approach to Enterprise Security.” IEEE Security & Privacy, vol. 12, no. 4, 2014, pp. 59–67.
Zhang, Yifan, et al. “Zero Trust Architecture for Secure Healthcare Cloud Systems.” IEEE Access, vol. 10, no. 3, 2022, pp. 34567–34579.
Li, Tian, Anit Kumar Sahu, and Virginia Smith. “Federated Learning: Challenges, Methods, and Future Directions.” IEEE Transactions on Neural Networks and Learning Systems, vol. 31, no. 10, 2020, pp. 1–17.
Gundaboina, A. (2024). Automated Patch Management for Endpoints: Ensuring Compliance in Healthcare and Education Sectors. International Journal of Computer Science and Information Technology Research (IJCSITR), 5(2), 114–134. https://doi.org/10.63530/IJCSITR_2024_05_02_010
Rieke, Nicola, et al. “The Future of Digital Health with Federated Learning.” Nature Machine Intelligence, vol. 2, no. 6, 2020, pp. 1–7.
Abadi, Martin, et al. “Deep Learning with Differential Privacy.” Proceedings of the ACM Conference on Computer and Communications Security, vol. 23, no. 1, 2016, pp. 308–318.
Shokri, Reza, et al. “Membership Inference Attacks against Machine Learning Models.” IEEE Symposium on Security and Privacy, vol. 38, no. 2, 2017, pp. 3–18.
Behl, Abhishek, and Kaushik Behl. “Cybersecurity and Cyberwar: What Everyone Needs to Know.” Journal of Information Security, vol. 8, no. 3, 2017, pp. 1–12.
Fernandes, Diogo A. B., et al. “Security Issues in Cloud Environments: A Survey.” Computers & Security, vol. 87, no. 4, 2019, pp. 101587–101605.
Gundaboina, A. (2024). Application Protection Platforms (CNAPP) for Healthcare: Safeguarding Patient Data in Cloud Infrastructure. International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences, 12(5), 1–12. https://doi.org/10.37082/IJIRMPS.v12.i5.232622
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Juliette Aerts (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
