106 citations · 491 across the 33 of their papers we have counts for
5 papers · 1 filter
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…
Relaxed Marginal Consistency for Differentially Private Query Answering
Ryan McKenna, Siddhant Pradhan, Daniel Sheldon +1
Many differentially private algorithms for answering database queries involve a step that reconstructs a discrete data distribution from noisy measurements. This provides consisten…
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…
Differentially Private Algorithms for 2020 Census Detailed DHC Race \& Ethnicity
Sam Haney, William Sexton, Ashwin Machanavajjhala +2
This article describes a proposed differentially private (DP) algorithms that the US Census Bureau is considering to release the Detailed Demographic and Housing Characteristics (D…
HDMM: Optimizing error of high-dimensional statistical queries under differential privacy
Ryan McKenna, Gerome Miklau, Michael Hay +1
In this work we describe the High-Dimensional Matrix Mechanism (HDMM), a differentially private algorithm for answering a workload of predicate counting queries. HDMM represents qu…