84 citations · 228 across the 15 of their papers we have counts for
4 papers · 1 filter
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,…
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…
Winning the NIST Contest: A scalable and general approach to differentially private synthetic data
Ryan McKenna, Gerome Miklau, Daniel Sheldon
We propose a general approach for differentially private synthetic data generation, that consists of three steps: (1) select a collection of low-dimensional marginals, (2) measure…
Permute-and-Flip: A new mechanism for differentially private selection
Ryan McKenna, Daniel Sheldon
We consider the problem of differentially private selection. Given a finite set of candidate items and a quality score for each item, our goal is to design a differentially private…