35 citations · 79 across the 8 of their papers we have counts for
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cs.CR2021★ 2 cited
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…
cs.CR2020
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…