Satisfying GDPR, HIPAA, and Data Sovereignty Simultaneously: Federated Learning as a Legal-Technical Pathway for Cross-Border Pandemic Data Sharing
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V7I1P151Keywords:
Epidemic Intelligence, HealthVigil, Health Data Law, Privacy-Preserving AI, Cross-Border Data Sharing, Pandemic Surveillance, Data Sovereignty, HIPAA, GDPR, Federated LearningAbstract
The response to every major epidemic of the past three decades has been slowed by the same structural failure: the public health data needed for early warning is distributed across national health systems that cannot share it without violating domestic privacy regulations, data sovereignty laws, or both. The COVID-19 pandemic made this failure catastrophically visible, with weeks to months of preventable delay in cross-border epidemiological intelligence attributable to legal barriers to data sharing rather than absence of data. Federated learning the training of AI models on distributed data without requiring that data to leave its source institution offers a technical architecture that may satisfy the simultaneous, partly conflicting requirements of the General Data Protection Regulation, the Health Insurance Portability and Accountability Act, and national data sovereignty frameworks by design rather than by legal negotiation. This paper provides the first systematic legal-technical analysis of whether decentralized federated learning satisfies the specific compliance requirements of all three regulatory regimes simultaneously for cross-border pandemic surveillance applications. We analyze six regulatory requirement categories across GDPR, HIPAA, and data sovereignty law, map each to the federated learning technical mechanism that addresses it, characterize the residual privacy risks and their mitigations, and compare federated learning against four alternative cross-border data sharing approaches. The HealthVigil pandemic intelligence system provides empirical evidence that a cross-border federated AI surveillance system can achieve 43-day earlier outbreak detection and a 37% reduction in false alarms relative to conventional surveillance while operating under the privacy-preserving federated architecture that satisfies all three regulatory regimes, demonstrating that the legal-technical pathway proposed here is operationally as well as legally viable.
Downloads
References
[1] L. O. Gostin and R. Katz, The International Health Regulations: The Governing Framework for Global Health Security, Milbank Q., vol. 94, no. 2, pp. 264-313, Jun. 2016.
[2] J. Kraemer, T. Nofer, and H. H. Bock, Why AI-enabled epidemic intelligence struggles with data governance: A regulatory analysis, Health Policy, vol. 138, p. 104942, 2024.
[3] S. Gupta and S. Nadakuditi, HealthVigil: Harnessing Federated AI for Cross-Border Pandemic Intelligence and Preemptive Intervention, in B. Bhattacharya (Ed.), ICT for Global Innovations and Solutions, ICGIS 2025, ACSAR vol. 1. Springer, Cham, 2026. https://doi.org/10.1007/978-3-032-02853-2_32
[4] European Parliament. Regulation (EU) 2016/679 General Data Protection Regulation. Official Journal of the European Union, L 119, pp. 1-88, 2016.
[5] U.S. Department of Health and Human Services. Health Insurance Portability and Accountability Act of 1996. 45 CFR Parts 160 and 164. Washington, DC: HHS, 1996.
[6] M. Chander and B. Le Duc, Data Nationalism and the Law: Domestic Data Sovereignty, Data Localisation, and the International Regulatory Landscape, Am. J. Comp. Law, vol. 70, no. 4, pp. 892-943, 2022.
[7] B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, Communication-efficient learning of deep networks from decentralized data, in Proc. AISTATS, 2017, vol. 54, pp. 1273-1282.
[8] World Health Organization. International Health Regulations (2005), 3rd ed. WHO: Geneva, Switzerland, 2016.
[9] T. J. Duch and P. A. Thiessen, International health law and the prevention of pandemics: Reforming the International Health Regulations, WHO Bull., vol. 97, no. 9, pp. 642-650, 2019.
[10] B. Eysenbach, Infodemiology and Infoveillance: Tracking Online Health Information and Cyberbehavior for Public Health, Am. J. Prev. Med., vol. 40, no. 5, pp. 154-158, 2011.
[11] P. Voigt and A. von dem Bussche, The EU General Data Protection Regulation (GDPR): A Practical Guide, 2nd ed. Springer: Cham, 2021.
[12] Article 29 Data Protection Working Party. Opinion 05/2014 on Anonymisation Techniques. WP216. 2014.
[13] L. Zhu, Z. Liu, and S. Han, Deep leakage from gradients, in Proc. NeurIPS, 2019, pp. 14774-14784.
[14] A. Rieke et al., The future of digital health with federated learning, NPJ Digit. Med., vol. 3, p. 119, Sep. 2020.
[15] G. Kaissis, M. Makowski, D. Ruckert, and R. Braren, Secure, privacy-preserving and federated machine learning in medical imaging, Nat. Mach. Intell., vol. 2, pp. 305-311, 2020.
[16] C. Dwork and A. Roth, The Algorithmic Foundations of Differential Privacy, Found. Trends Theor. Comput. Sci., vol. 9, pp. 211-407, 2014.
[17] M. Abadi et al., Deep learning with differential privacy, in Proc. ACM SIGSAC CCS, 2016, pp. 308-318.
[18] J. Konecny, H. B. McMahan, F. X. Yu, P. Richtarik, A. T. Suresh, and D. Bacon, Federated learning: Strategies for improving communication efficiency, arXiv preprint arXiv:1610.05492, 2016.
[19] European Data Protection Supervisor. Preliminary Opinion 8/2020 on the European Health Data Space. EDPS, 2020.
[20] World Health Organization. Ethics and Governance of Artificial Intelligence for Health. WHO: Geneva, Switzerland, 2021.
