Iterative Methods for Private Synthetic Data: Unifying Framework and New
Methods
Iterative Methods for Private Synthetic Data: Unifying Framework and New
Methods
We study private synthetic data generation for query release, where the goal is to construct a sanitized version of a sensitive dataset, subject to differential privacy, that approximately preserves the answers to a large collection of statistical queries. We first present an algorithmic framework that unifies a long line of …