Privacy-Preserving AI Techniques for Secure Data Sharing in Healthcare

Rajashekhar Reddy Kethireddy

Authors

Rajashekhar Reddy Kethireddy Department Of Software Engineering, IBM, USA Author

Keywords:

Privacy-Preserving AI, Secure Data Sharing, Healthcare, Federated Learning, Differential Privacy

Abstract

While AI integration into healthcare has revolutionized diagnostics, treatments, and operational efficiency, it has also heightened the need for secure sharing of sensitive health data. This paper presents novel AI-driven techniques that ensure robust privacy preservation and secure data exchange among healthcare stakeholders. We propose a framework that integrates federated learning with differential privacy and homomorphic encryption, enabling collaborative model training without exposing raw patient data. Additionally, we introduce a dynamic anonymization protocol that adjusts privacy parameters based on data sensitivity and usage context. Our experimentation on healthcare datasets demonstrates superior performance in maintaining privacy and data integrity compared to state-of-the-art techniques, while complying with HIPAA and GDPR standards. Furthermore, we explore the scalability and adaptability of these techniques in real-world settings. This research contributes to the development of trustworthy AI systems that safeguard data privacy and enhance patient care and medical research.

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