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17 Cybaris Intell. Prop. L. Rev. 1 (2026)

handle is hein.journals/cybaris17 and id is 1 raw text is: DEMOCRATIZING HEALTH DATA: A LEGAL FRAMEWORK FOR PRIVACY-PRESERVING, GOVERNMENT-LED Al COLLABORATION Nicole Nigri* INTRODUCTION........................................................................................1 II. U.S. HEALTH DATA MONOPOLIZATION AND PRIVACY C H ALLEN G ES............................................................................... 6 III. FEDERATED LEARNING (FL): PRIVACY-PRESERVING AI INFRASTRUCTURE............................................................................10 A. How FL Works: Data Models and System Design.....11 B. Strengths and Real-World Implementations....................13 C. Challenges and Privacy Risks ............................................15 IV. GOVERNMENT-LED FL HEALTH DATA NETWORK - PROPOSAL16 V. COMPARATIVE MODELS: PRIVACY-ENHANCING APPROACHES ABROAD...........................................................................................19 A. European Union: GDPR and the European Health Data Sp ace ......................................................................................... . 20 B. United Kingdom: NHS and Trusted Research Environments ..........................................................................................................21 C. Canada: Pan-Canadian Federated Data Collaboration ..22 D. India: Federated Health Data Infrastructure....................23 VI. LEGAL AND POLICY REFORMS TO ENABLE THE PRIVACY- PRESERVING INFRASTRUCTURE ......................................................24 A. Clarifying HIPAA's Treatment of FL ................................24 B. Aligning the Common Rule with FL-Based Research ....25 C. Addressing Gaps Beyond HIPAA and Federal Research R egulation ................................................................................ . 25 D. Ensuring Trust, Oversight, and Incentives.......................26 C O N CLU SIO N ................................................................................... . 26 INTRODUCTION Advances in artificial intelligence (AI) have highlighted the immense value of large, diverse health datasets for improving 1