16 papers
Gaussian DP for Reporting Differential Privacy Guarantees in Machine Learning
Juan Felipe Gomez, Bogdan Kulynych, Georgios Kaissis +4
Current practices for reporting differential privacy (DP) guarantees for machine learning (ML) algorithms such as DP-SGD provide an incomplete and potentially misleading picture. F…
Mitigating Disparate Impact of Differentially Private Learning through Bounded Adaptive Clipping
Linzh Zhao, Aki Rehn, Mikko A. Heikkilä +2
Differential privacy (DP) has become an essential framework for privacy-preserving machine learning. Existing DP learning methods, however, often have disparate impacts on model pr…
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…
Noise-Aware Differentially Private Variational Inference
Talal Alrawajfeh, Joonas Jälkö, Antti Honkela
Differential privacy (DP) provides robust privacy guarantees for statistical inference, but this can lead to unreliable results and biases in downstream applications. While several…
On Reliability of Efficient 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: Explicit Memory-Efficient 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…