1 citations · 1 across the 17 of their papers we have counts for
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Shift-aware score transport for privacy auditing without retraining
Yaohong Yang, Gauri Pradhan, Qi Chen +2
Privacy auditing empirically assesses privacy leakage by using membership inference attacks to establish lower bounds on privacy loss. We consider an observational auditing setting…
Efficient Provably Private Classification with a Tabular Foundation Model
Talal Alrawajfeh, Cristiana Diaconu, Ossi Räisä +5
Tabular data underpin prediction and decision-making in medicine, finance, government and science, but often contain sensitive individual-level information, creating a need for acc…
On Choosing the Parameter in Gaussian Differential Privacy
Bogdan Kulynych, Antti Honkela
Recent work argues for using Gaussian differential privacy (GDP) to report the privacy guarantees in privacy-preserving machine learning. We provide principled mappings from pure-D…
On Reliability of Membership Inference Vulnerability Evaluation
Joonas Jälkö, Gauri Pradhan, Ossi Räisä +1
Membership inference attacks (MIAs) are popular methods for empirically assessing the leakage of sensitive information in the training data through models or statistics learned fro…
Beyond Square Roots: A Memory-Efficient Explicit Factorization for Multi-Epoch Private Learning
Nikita P. Kalinin, Aki Rehn, Joel Daniel Andersson +2
Correlated-noise mechanisms are among the most promising approaches for improving the utility of differentially private model training, but rigorous guarantees require explicit, an…