InfoShield: Privacy-Preserving Speech Representations for Mental Health Screening via Information-Theoretic Optimization
arXiv:2606.05561v1 Announce Type: new Abstract: Speech-based mental health screening offers scalable depression detection, yet clinical deployment faces a significant barrier: users' privacy concerns about demographic information exposure. Current techniques struggle to resolve this conflict. Adversarial training often fails against unseen threats, whereas Differential Privacy tends to compromise diagnostic performance by injecting noise across all features. This paper presents InfoShield, which minimizes mutual information between speech representations and sensitive attributes while preservi
The increasing deployment of AI in sensitive applications like healthcare necessitates addressing privacy concerns to foster public trust and adoption, as current methods are proving insufficient.
This development addresses a critical barrier to scalable AI adoption in mental healthcare by enabling privacy-preserving screenings, which is vital for ethical AI deployment and public health initiatives.
The ability to minimize mutual information between speech and sensitive attributes in mental health AI without significantly compromising diagnostic performance shifts the paradigm for privacy in AI-driven healthcare tools.
- · Mental Health Tech Companies
- · Patients seeking mental health support
- · AI ethicists and privacy advocates
- · Healthcare providers
- · Developers of non-privacy preserving AI tools
- · Purely adversarial training approaches
- · Techniques reliant on uniform noise injection
More widespread and ethical adoption of AI in mental health screening.
Increased legal and regulatory focus on 'privacy-by-design' for AI systems in sensitive domains.
The development of similar information-theoretic optimization techniques for privacy protection in other sensitive AI applications beyond healthcare.
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