A Data-Interventional Framework for Auditing Privacy and Fairness in Generative Medical Imaging

Diffusion-based synthetic data generation offers a promising route for sharing medical imaging data without releasing sensitive patient records. However, generative models face a fundamental tension between privacy and fairness: they may memorize rare training samples, leading to privacy risks, or f