Privacy-Preserving AI Framework Using Differential Privacy for Healthcare Data Analytics

Abstract

The increasing use of artificial intelligence in healthcare has raised significant concerns regarding patient data privacy and security. This paper presents a Privacy-Preserving AI Framework using Differential Privacy for Healthcare Data Analytics to ensure secure utilization of sensitive medical data. The proposed system integrates differential privacy mechanisms with machine learning models to prevent the leakage of individual patient information while maintaining analytical utility. Noise injection techniques are applied during model training to ensure that sensitive data cannot be reverse-engineered. The framework supports various healthcare applications, including disease prediction, patient risk assessment, and clinical decision support. Additionally, the system is designed to operate efficiently in distributed environments, enabling secure data sharing across multiple institutions. Deep learning models are adapted to incorporate privacy constraints without significantly degrading performance. Experimental results demonstrate that the proposed approach achieves a balance between data privacy and predictive accuracy. Integration with prior research in healthcare analytics further enhances system robustness and scalability. The study highlights the importance of privacy-preserving techniques in enabling secure, trustworthy, and compliant AI-driven healthcare systems.