Introduces natural identifiers (NIDs) from common training data to support post-hoc differential privacy auditing and dataset inference for LLMs without retraining or private held-out sets.
Enhancing one-run privacy auditing with quantile regression-based membership inference.arXiv preprint arXiv:2506.15349,
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
In white-box DP-SGD, canary-aligned signals form a sequence of random variables whose normalized sum is asymptotically Gaussian, enabling a new one-run auditing framework with tighter privacy lower bounds.
citing papers explorer
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Natural Identifiers for Privacy and Data Audits in Large Language Models
Introduces natural identifiers (NIDs) from common training data to support post-hoc differential privacy auditing and dataset inference for LLMs without retraining or private held-out sets.
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Let's Ask Gauss: Improved One-Run Privacy Auditing
In white-box DP-SGD, canary-aligned signals form a sequence of random variables whose normalized sum is asymptotically Gaussian, enabling a new one-run auditing framework with tighter privacy lower bounds.