Zero-Run auditing supplies valid lower bounds on differential privacy parameters from fixed member and non-member datasets by modeling and correcting distribution-shift confounding via causal-inference techniques.
Tight auditing of differentially private machine learning
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
A hypothesis-testing framework with class-restricted Donsker-Varadhan estimators provides optimal non-asymptotic confidence intervals and minimax lower bounds for black-box auditing of Rényi DP guarantees.
citing papers explorer
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Privacy Auditing with Zero (0) Training Run
Zero-Run auditing supplies valid lower bounds on differential privacy parameters from fixed member and non-member datasets by modeling and correcting distribution-shift confounding via causal-inference techniques.
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Optimal Guarantees for Auditing R\'enyi Differentially Private Machine Learning
A hypothesis-testing framework with class-restricted Donsker-Varadhan estimators provides optimal non-asymptotic confidence intervals and minimax lower bounds for black-box auditing of Rényi DP guarantees.