A Data-Interventional Framework for Auditing Privacy and Fairness in Generative Medical Imaging
Paper recorded by Signals 4 on 2026-09-22 in cs.CV. Abstract reproduced from arXiv; link to the original below.
Published 2026-09-22 on arXiv · recorded by Signals 4 on 2026-09-23
Category: cs.CV · 计算机视觉 · first seen 2026-09-23
Abstract
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
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Cite this page: A Data-Interventional Framework for Auditing Privacy and Fairness in Generative Medical Imaging: the #27 most recent of 301 cs.CV papers we have recorded (as of 2026-09-22). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/a-data-interventional-framework-for-auditing-privacy-and-fairness-in-generative-.html
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