New framework offers optimal guarantees for auditing RDP machine learning

Researchers have developed a new auditing framework for machine learning algorithms that claim Rényi differential privacy (RDP). This framework uses the Donsker-Varadhan (DV) estimator to directly measure Rényi divergence, providing explicit confidence intervals for RDP auditing. The proposed method achieves information-theoretically optimal sample-complexity guarantees and shows empirical improvements over existing black-box methods, particularly for challenging small and moderate Rényi orders. AI

IMPACT Establishes new optimal guarantees for auditing privacy in ML models, potentially improving trust and security in deployed systems.

RANK_REASON The cluster contains an academic paper detailing a new theoretical framework and empirical validation for auditing differentially private machine learning algorithms. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• CIFAR-10
• Donsker-Varadhan estimator
• DP-SGD
• MNIST
• Rényi differential privacy
• machine learning algorithms

• paper
• safety

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