84 citations · 228 across the 15 of their papers we have counts for
16 papers
Tight End-to-End Accounting for Differentially Private Synthetic Data
Georgi Ganev, Vadym Doroshenko, Arun Ganesh +3
Differentially private (DP) pipelines often combine heterogeneous mechanisms, yet their guarantees are best analyzed using different notions of privacy (e.g., bounded range, zCDP,…
Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Data
Miguel Fuentes, Brett Mullins, Ryan McKenna +2
Mechanisms for generating differentially private synthetic data based on marginals and graphical models have been successful in a wide range of settings. However, one limitation of…
(Amplified) Banded Matrix Factorization: A unified approach to private training
Christopher A. Choquette-Choo, Arun Ganesh, Ryan McKenna +4
Matrix factorization (MF) mechanisms for differential privacy (DP) have substantially improved the state-of-the-art in privacy-utility-computation tradeoffs for ML applications in…
Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy
Anastasia Koloskova, Ryan McKenna, Zachary Charles +2
We study gradient descent under linearly correlated noise. Our work is motivated by recent practical methods for optimization with differential privacy (DP), such as DP-FTRL, which…
AIM: An Adaptive and Iterative Mechanism for Differentially Private Synthetic Data
Ryan McKenna, Brett Mullins, Daniel Sheldon +1
We propose AIM, a new algorithm for differentially private synthetic data generation. AIM is a workload-adaptive algorithm within the paradigm of algorithms that first selects a se…
Benchmarking Differentially Private Synthetic Data Generation Algorithms
Yuchao Tao, Ryan McKenna, Michael Hay +2
This work presents a systematic benchmark of differentially private synthetic data generation algorithms that can generate tabular data. Utility of the synthetic data is evaluated…