Differential Privacy Techniques in Machine Learning for Health Record Analysis

The integration of machine learning (ML) into healthcare has revolutionized the analysis of Electronic Health Records (EHRs), enabling more accurate predictions, earlier diagnoses, and personalized treatment strategies. However, the inherent sensitivity and legal protection of health records introduce significant privacy concerns when applying data-driven models to patient information. Traditional de-identification methods have proven insufficient against modern re-identification attacks, necessitating more robust privacy-preserving frameworks. This research explores the application of differential privacy (DP) techniques in machine learning for health record analysis, providing formal privacy guarantees while maintaining analytic utility. Differential privacy introduces controlled randomness into the learning process to obfuscate individual contributions, thereby preventing adversaries from inferring whether any particular patient’s data was included in the training set. This study presents a comprehensive review of DP mechanisms—including the Laplace mechanism, Gaussian mechanism, and privacy budget accounting—in the context of supervised and unsupervised learning models applied to EHRs. A detailed taxonomy of existing DP-enhanced ML frameworks is provided, followed by a critical evaluation of their performance across several public and synthetic health record datasets. Furthermore, this research investigates the trade-offs between model accuracy and privacy guarantees, analyzing how privacy budgets (ε) influence utility in disease prediction, patient stratification, and risk modeling. The paper also introduces an experimental pipeline that integrates DP into deep learning models (e.g., DP-SGD) for structured clinical data and unstructured clinical notes. Special attention is given to challenges such as gradient leakage, overfitting under noise, and handling class imbalance in sensitive datasets. Finally, the study addresses the practical implementation of differential privacy in real-world healthcare systems, including compliance with data protection regulations such as HIPAA and GDPR, the role of privacy-aware auditing, and deployment considerations in federated and cloud-based environments. The results demonstrate that with thoughtful algorithmic design and calibrated privacy parameters, differential privacy can serve as a foundational technique for enabling secure and ethical machine learning in healthcare. This work contributes toward building trustworthy, legally compliant, and privacy-respecting AI systems for health record analysis.