Privacy-Preserving Machine Learning for Mental Health Risk Prediction

Machine learning can support earlier identification of mental-health risk from electronic health records, speech, social-media text, wearable sensors, and other digital traces, but centralized data collection creates substantial privacy and governance concerns. This paper presents a literature-grounded analysis of privacy-preserving machine learning (PPML) for anxiety and depression risk prediction, focusing on federated learning, differential privacy, secure aggregation, homomorphic encryption, and trusted execution environments. The study compares the privacy guarantees, utility implications, computational requirements, and deployment constraints of these approaches and examines recent evidence from mental-health applications. Particular attention is given to federated depression detection from multilingual social-media text, privacy-preserving speech-based depression diagnosis, and federated classification of major depressive disorder from EEG. The analysis shows that federated learning can reduce the need to centralize raw sensitive data, but it does not eliminate privacy risks: model updates may still leak information and can be exposed to poisoning or inference attacks. Differential privacy provides formal protection but introduces a measurable privacy–utility trade-off. The paper proposes a layered deployment framework combining local training, secure aggregation, differential privacy, robustness checks, and human-centred governance. The study concludes that privacy protection should be evaluated jointly with predictive performance, calibration, fairness, robustness, communication cost, and clinical utility rather than treated as an independent feature.