1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.LG2025
PrivacyGuard: A Modular Framework for Privacy Auditing in Machine Learning
Luca Melis, Matthew Grange, Iden Kalemaj +4
The increasing deployment of Machine Learning (ML) models in sensitive domains motivates the need for robust, practical privacy assessment tools. PrivacyGuard is a comprehensive to…
cs.CR2024★ 1 cited
Auditing Private Prediction
Karan Chadha, Matthew Jagielski, Nicolas Papernot +2
Differential privacy (DP) offers a theoretical upper bound on the potential privacy leakage of analgorithm, while empirical auditing establishes a practical lower bound. Auditing t…