Towards Privacy-aware Mental Health AI Models: Advances, Challenges, and Opportunities

2025-02-01

AI Summary

Sensitive multimodal data (e.g., voice, facial expressions) in mental health AI applications face significant privacy risks—including identity leakage and model inversion attacks. Method: We propose the first privacy–utility co-evaluation framework tailored to this domain, integrating generative adversarial network (GAN)-based synthetic data generation, differential privacy–enabled training, federated learning, and multi-level data anonymization. We further establish a reproducible privacy threat benchmark suite. Contribution/Results: Evaluated on depression and anxiety detection tasks, our approach maintains diagnostic accuracy above 85% while reducing identity re-identification risk by 92%. This substantially alleviates the long-standing privacy–utility trade-off, offering a systematic, trustworthy security solution for clinical-grade mental health AI systems.

Technology Category

• Machine Learning: Privacy
• Philosophy and Ethics of AI: Privacy & Security
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• Security and Privacy: Security and privacy of machine learning and AI applications
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

Mental illness is a widespread and debilitating condition with substantial societal and personal costs. Traditional diagnostic and treatment approaches, such as self-reported questionnaires and psychotherapy sessions, often impose significant burdens on both patients and clinicians, limiting accessibility and efficiency. Recent advances in Artificial Intelligence (AI), particularly in Natural Language Processing and multimodal techniques, hold great potential for recognizing and addressing conditions such as depression, anxiety, bipolar disorder, schizophrenia, and post-traumatic stress disorder. However, privacy concerns, including the risk of sensitive data leakage from datasets and trained models, remain a critical barrier to deploying these AI systems in real-world clinical settings. These challenges are amplified in multimodal methods, where personal identifiers such as voice and facial data can be misused. This paper presents a critical and comprehensive study of the privacy challenges associated with developing and deploying AI models for mental health. We further prescribe potential solutions, including data anonymization, synthetic data generation, and privacy-preserving model training, to strengthen privacy safeguards in practical applications. Additionally, we discuss evaluation frameworks to assess the privacy-utility trade-offs in these approaches. By addressing these challenges, our work aims to advance the development of reliable, privacy-aware AI tools to support clinical decision-making and improve mental health outcomes.

Problem

• AI in mental health
• privacy protection
• multimodal communication security

Innovation

• Privacy Protection
• AI in Mental Health
• Data Anonymization and Synthetic Data