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This paper proposes a way to measure how much a differentially private model leaks about its training data from a single training run, instead of relying on the coarser binary membership guesses used in earlier one-run audits. Its key claim is that canary signals become asymptotically Gaussian, which yields tighter empirical privacy lower bounds. Practitioners checking whether DP-SGD guarantees hold in deployment should watch whether the method's assumptions, especially the white-box setting, match their threat model.
Let's Ask Gauss: Improved One-Run Privacy Auditing
arXiv:2606.12733v3 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. We study empirical privacy auditing for differentially private (DP) machine learning, focusing on efficient one-run methods for mechanisms such as DP-SGD. Prior one-run approaches threshold training examples or "canaries" into binary membership guesses, which discards useful information. We show that, in the white-box DP-SGD setting, canary-aligned signals naturally form a sequence of random variables whose normalized sum is asymptotically Gaussian. We show that this gaussian perspective can also be naturally extended to other composition mechanisms that satisfy a classical, triangular-array, or martingale central limit theorem. Leveraging this distributional perspective, we develop a DP-auditing framework that leads to tighter privacy lower bounds from a single training run.
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. We study empirical privacy auditing for differentially private (DP) machine learning, focusing on efficient one-run methods for mechanisms such as DP-SGD. Prior one-run approaches threshold training examples or "canaries" into binary membership guesses, which discards useful information. We show that, in the white-box DP-SGD setting, canary-aligned signals naturally form a sequence of random variables whose normalized sum is asymptotically Gaussian. We show that this gaussian perspective can also be naturally extended to other composition mechanisms that satisfy a classical, triangular-array, or martingale central limit theorem. Leveraging this distributional perspective, we develop a DP-auditing framework that leads to tighter privacy lower bounds from a single training run.
Machine Learning (cs.LG)
arXiv:2606.12733 [cs.LG]
(or arXiv:2606.12733v3 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.12733 Focus to learn more arXiv-issued DOI via DataCite
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