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