arXiv Machine Learning
Sep 23

Optimizing Canaries for Privacy Auditing with Metagradient Descent

The paper investigates black-box privacy auditing for differentially private learning algorithms, focusing on DP‑SGD. It introduces a method that optimizes the auditor’s canary set using metagradient descent, improving empirical lower bounds on privacy parameters compared to prior canary designs. The approach is shown to be DP‑SGD agnostic and efficient, with optimized canaries for small models remaining effective for larger DP‑SGD models.

By Matteo Boglioni, Terrance Liu, Andrew Ilyas, Zhiwei Steven Wu
arXiv Machine Learning
Jun 15

Let's Ask Gauss: Improved One-Run Privacy Auditing

arXiv:2606. 12733v2 Announce Type: replace Abstract: Privacy auditing provides an important safeguard by estimating the actual information leaked by a model, thus ensuring that theoretical privacy guarantees hold in practice.

By Adya Agrawal, Yu Wei, Jaspal Singh, Malik Magdon-Ismail, Vassilis Zikas
arXiv Machine Learning
Oct 2

Certification-Based Differentially Private Learning

The paper extends the abstract gradient training (AGT) framework to provide tighter differential privacy guarantees for both private prediction and private learning. It introduces Abstract Gradient Sampling (AGS) to analyze privacy in continuous, unbounded regression and offers theoretical and empirical evidence that these methods yield tighter bounds than global-sensitivity baselines, even in previously unbounded settings. The authors also demonstrate that their private learning algorithm can outperform standard private learners under comparable conditions.

By Mihnea Ghitu, Matthew Wicker
arXiv AI
Oct 21

Reliable Estimation of Differential Privacy under Selection Bias

The paper addresses a flaw in differential privacy audits where selection bias in Membership Inference Attacks leads to overestimated lower bounds on privacy leakage. By applying statistical correction methods, the authors demonstrate that uncorrected audits can violate theoretical privacy guarantees up to four times more often than claimed, while corrected audits maintain valid bounds at the nominal confidence level.

By Valentin Dorseuil (DI-ENS), Meenatchi Sundaram Muthu Selva Annamalai (UCL), Jamal Atif (CMAP), Olivier Capp\'e (DI-ENS), Nicolas Papernot (Inria)