@inproceedings{
wu2023membership,
title={Membership Inference Attack on Diffusion Models via Quantile Regression},
author={Steven Wu and Shuai Tang and Sergul Aydore and Michael Kearns and Aaron Roth},
booktitle={NeurIPS 2023 Workshop on Regulatable ML},
year={2023},
url={https://openreview.net/forum?id=8WH2t9F0Ip}
}
Recently, diffusion models have demonstrated great potential for image synthesis due to their ability to generate high-quality synthetic data. However, when applied to sensitive data, privacy concerns have been raised about these models. In this paper, we evaluate the privacy risks of diffusion models through a \emph{membership inference (MI) attack}, which aims to identify whether a target example is in the training set when given the trained diffusion model. Our proposed MI attack learns a single quantile regression model that predicts (a quantile of) the distribution of reconstruction loss for each example. This enables us to identify a unique threshold on the reconstruction loss tailored to each example when determining their membership status. We show that our attack outperforms the prior state-of-the-art MI attack and avoids their high computational cost from training multiple shadow models. Consequently, our work enriches the set of practical tools for auditing the privacy risks of large-scale generative models.
Membership Inference Attack on Diffusion Models via Quantile Regression
Steven Wu Shuai Tang Sergul Aydore Michael Kearns Aaron Roth