5 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…
Optimal conversion from Rényi Differential Privacy to -Differential Privacy
Anneliese Riess, Juan Felipe Gomez, Flavio du Pin Calmon +2
We prove the conjecture stated in Appendix F.3 of \citet{zhu2022optimalaccountingdifferentialprivacy}: among all conversion rules that map a Rényi Differential Privacy (RDP) profi…
Unifying Re-Identification, Attribute Inference, and Data Reconstruction Risks in Differential Privacy
Bogdan Kulynych, Juan Felipe Gomez, Georgios Kaissis +4
Differentially private (DP) mechanisms are difficult to interpret and calibrate because existing methods for mapping standard privacy parameters to concrete privacy risks -- re-ide…
The Hitchhiker's Guide to Efficient, End-to-End, and Tight DP Auditing
Meenatchi Sundaram Muthu Selva Annamalai, Borja Balle, Jamie Hayes +2
In this paper, we systematize research on auditing Differential Privacy (DP) techniques, aiming to identify key insights and open challenges. First, we introduce a comprehensive fr…
Attack-Aware Noise Calibration for Differential Privacy
Bogdan Kulynych, Juan Felipe Gomez, Georgios Kaissis +2
Differential privacy (DP) is a widely used approach for mitigating privacy risks when training machine learning models on sensitive data. DP mechanisms add noise during training to…