Differential Privacy for Mental Health Datasets with PyTorch: From Theory to DP-SGD
Updated on December 04, 2025 15 minutes read
Frequently Asked Questions
How much mental health domain expertise do I need before using these methods?
You don’t need to be a psychiatrist, but you should understand what your labels and features mean clinically and how predictions might affect care. Working closely with clinicians or health informatics experts is essential for picking sensible outcomes and thresholds.
Can I use DP-SGD with small datasets?
Yes, but the privacy–utility trade‑off becomes sharper. With very small cohorts, the noise needed for strong DP can overwhelm the signal, so you may need simpler models, careful regularisation, and realistic expectations about model performance.
Does training with DP-SGD automatically make me GDPR or HIPAA compliant?
No. DP reduces the risk of inference from models and statistics but does not replace legal, organisational, or security obligations. It should be part of a wider compliance strategy that also covers access control, consent, data minimisation, and governance.
Should I always use a DP library like Opacus instead of writing my own?
For learning and experimentation, a scratch implementation is valuable. For anything that touches real clinical data, it is much safer to use a vetted library such as Opacus, which provides efficient per‑sample gradients and built‑in privacy accounting.