Evaluating Privacy Leakage Risks in High Capacity Healthcare Analytics Models

Abstract

The fast development of digital healthcare systems and electronic health records has created opportunities for improved decision making based on data that was previously inaccessible. However, the sensitive nature of health information raises important questions regarding security, privacy and ethical compliance. This paper seeks to introduce an optimized machine learning model for health data analytics that is privacy-aware. The approach uses techniques such as differential privacy, federated learning and safe aggregation for collaborative health data analysis without disclosing raw patient data. The proposed model manages the privacy-utility trade-off while lowering the communication cost via gradient clipping, noise injection and effective optimization methods. An experimental evaluation on benchmark healthcare datasets shows that the model provides a high level of privacy guaranties and a high degree of prediction accuracy. This is quantified in terms of differential privacy parameters (ε, δ). The system also improves resilience against adversarial attacks and scalability, thus providing reliable results suitable for clinical decision support. Our study contributes toward the creation of a secure, effective and moral solution for the next generation of health data analytics by combining optimization algorithms with privacy-preserving safeguards.