5 citations · 9 across the 12 of their papers we have counts for
4 papers · 1 filter
To Shuffle or not to Shuffle: Auditing DP-SGD with Shuffling
Meenatchi Sundaram Muthu Selva Annamalai, Borja Balle, Jamie Hayes +1
The Differentially Private Stochastic Gradient Descent (DP-SGD) algorithm supports the training of machine learning (ML) models with formal Differential Privacy (DP) guarantees. Tr…
The Elusive Pursuit of Reproducing PATE-GAN: Benchmarking, Auditing, Debugging
Georgi Ganev, Meenatchi Sundaram Muthu Selva Annamalai, Emiliano De Cristofaro
Synthetic data created by differentially private (DP) generative models is increasingly used in real-world settings. In this context, PATE-GAN has emerged as one of the most popula…
"What do you want from theory alone?" Experimenting with Tight Auditing of Differentially Private Synthetic Data Generation
Meenatchi Sundaram Muthu Selva Annamalai, Georgi Ganev, Emiliano De Cristofaro
Differentially private synthetic data generation (DP-SDG) algorithms are used to release datasets that are structurally and statistically similar to sensitive data while providing…
Nearly Tight Black-Box Auditing of Differentially Private Machine Learning
Meenatchi Sundaram Muthu Selva Annamalai, Emiliano De Cristofaro
This paper presents an auditing procedure for the Differentially Private Stochastic Gradient Descent (DP-SGD) algorithm in the black-box threat model that is substantially tighter…