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.
Cryptanalysis via machine learning based information theoretic metrics.arXiv preprint arXiv:2501.15076, 2025
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
DNN distinguishers detect no exploitable patterns in ML-KEM, BIKE, HQC, RSA hybrids, or AES/ChaCha20/DES cascades, consistent with IND-CPA security.
citing papers explorer
-
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.
-
Evaluating PQC KEMs, Combiners, and Cascade Encryption via Adaptive IND-CPA Testing Using Deep Learning
DNN distinguishers detect no exploitable patterns in ML-KEM, BIKE, HQC, RSA hybrids, or AES/ChaCha20/DES cascades, consistent with IND-CPA security.