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

arXiv · AI, language, vision and robotics · article · Sep 22, 2026 · UTC

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 fail to reproduce underrepresented features, resulting in unfair synthetic distributions. While prior work has largely focused on either memorization or fairness in isolation, their interaction remains insufficiently understood. In this work, we introduce a data-interventional framework to systematic

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