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Title: Winning the NIST Contest: A Scalable and General Approach to Differentially Private Synthetic Data

Citation Type: Miscellaneous

Publication Year: 2021

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Abstract: 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 [42], 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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Authors: McKenna, Ryan; Miklau, Gerome; Sheldon, Daniel

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Data Collections: IPUMS USA

Topics: Methodology and Data Collection

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