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arXiv · 2506.15349

Enhancing One-run Privacy Auditing with Quantile Regression-Based Membership Inference

Terrance Liu·Matteo Boglioni·Yiwei Fu·Shengyuan Hu·Pratiksha Thaker·Zhiwei Steven Wu
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Abstract

Differential privacy (DP) auditing aims to provide empirical lower bounds on the privacy guarantees of DP mechanisms like DP-SGD. While some existing techniques require many training runs that are prohibitively costly, recent work introduces one-run auditing approaches that effectively audit DP-SGD in white-box settings while still being computationally efficient. However, in the more practical black-box setting where gradients cannot be manipulated during training and only the last model iterate is observed, prior work shows that there is still a large gap between the empirical lower bounds and theoretical upper bounds. Consequently, in this work, we study how incorporating approaches for stronger membership inference attacks (MIA) can improve one-run auditing in the black-box setting. Evaluating on image classification models trained on CIFAR-10 with DP-SGD, we demonstrate that our proposed approach, which utilizes quantile regression for MIA, achieves tighter bounds while crucially maintaining the computational efficiency of one-run methods.

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Terrance Liu, Matteo Boglioni, Yiwei Fu, Shengyuan Hu, Pratiksha Thaker, Zhiwei Steven Wu. 2025-06-18. Enhancing One-run Privacy Auditing with Quantile Regression-Based Membership Inference. https://arxiv.org/abs/2506.15349

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