Winning the NIST Contest: A scalable and general approach to differentially private synthetic data

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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 those marginals with a noise addition mechanism, and (3) generate synthetic data that preserves the measured marginals well. Central to this approach is Private-PGM, a post-processing method that is used to estimate a high-dimensional data distribution from noisy measurements of its marginals. We present two mechanisms, NIST-MST and MST, that are instances of this general approach. NIST-MST was the winning mechanism in the 2018 NIST differential privacy synthetic data competition, and MST is a new mechanism that can work in more general settings, while still performing comparably to NIST-MST. We believe our general approach should be of broad interest, and can be adopted in future mechanisms for synthetic data generation.

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McKenna, Ryan, Gerome Miklau, and Daniel Sheldon. 2021. “Winning the NIST Contest: A Scalable and General Approach to Differentially Private Synthetic Data”. Journal of Privacy and Confidentiality 11 (3).
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