Wavelet-Domain Privacy SGD (WDP-SGD): FrequencySelective Privacy-Preserving Medical AI.

Protecting sensitive medical data during training is critical because transformer gradients can leak patient-specific information. We introduce a privacy-preserving clinical AI framework that integrates three complementary elements: (i) Bayesian synthetic data generation to produce epidemiologically realistic yet non-identifiable electronic health records, (ii) Wavelet-Domain Privacy Stochastic Gradient Descent(WDP-SGD) to apply frequency-selective noise to gradient updates of BERT-based classifiers, and (iii) multi-modal privacy auditing to empirically monitor potential information leakage. Unlike conventional differential privacy, which injects uniform noise, WDP-SGD perturbs high-frequency gradient components that disproportionately encode patient-specific information while preserving low-frequency components containing generalisable medical knowledge. Applied to a large synthetic medical text corpus covering multiple conditions, our approach consistently delivers stronger privacy protection and improved model performance relative to standard DP-SGD while maintaining convergence behaviour close to a non-private baseline. Privacy attack simulations, including membership inference, attribute inference and gradient reconstruction, further demonstrate enhanced resilience to adversarial attempts to extract sensitive information. These results indicate that wavelet-based differential privacy offers a practical pathway to privacy-conscious clinical language models, achieving a more favourable balance between privacy and utility than existing uniform-noise methods.