Winning the NIST Contest: A scalable and general approach to differentially private synthetic data
This provides a scalable and general solution for generating synthetic data with privacy guarantees, addressing a key challenge in data sharing and analysis.
The authors tackled the problem of generating differentially private synthetic data by proposing a general three-step approach, which won the 2018 NIST competition and achieved comparable performance in more general settings.
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.