Differential Privacy and Federated Learning Models Ensuring HIPAA Compliant Data Sharing Across Hospital Electronic Health Record Networks

Abstract

The increasing digitalization of healthcare has amplified concerns over data privacy, interoperability, and regulatory compliance. This paper provides a comprehensive review of how differential privacy and federated learning models enable secure and HIPAA-compliant data sharing across hospital electronic health record (EHR) networks. It explores the evolution of data privacy principles under HIPAA, the structure of EHR systems, and the limitations of traditional privacy-preserving methods. Differential privacy is examined as a mathematical framework that protects patient identities while supporting research, AI-based diagnostics, and policy-driven data utilization. Similarly, federated learning is analyzed for its distributed architecture, which enables hospitals to collaboratively train machine learning models without centralizing sensitive patient data. The review highlights the synergistic potential of combining these models to address ethical, legal, and technical challenges in healthcare data management. Furthermore, it discusses emerging governance mechanisms, transparency frameworks, and innovative technologies that enhance compliance and patient trust. The paper concludes by emphasizing the need for continued collaboration between healthcare institutions, policymakers, and technologists to develop scalable, privacy-preserving infrastructures that promote secure data-driven healthcare innovation in alignment with HIPAA standards.

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