tidysynthesis: a Meta-Package for Synthetic Data Generation

Abstract

Synthetic data generation enables data curators to more easily share datasets that limits the potential for disclosive inferences about data subjects in confidential datasets. Generating synthetic data requires navigating numerous design choices; however, most existing open source software fails to provide common software infrastructure for making such design choices efficiently. In this paper, we introduce tidysynthesis, a meta-package for synthetic data generation that enables better interoperability between existing modeling frameworks and statistical data privacy methods. tidysynthesis allows users more flexibility to specify and iterate on synthetic data algorithms by providing a common syntax to easily create and modify synthetic data generation pipelines. We demonstrate the features and extensibility of tidysynthesis, as well as provide end-to-end examples for synthetic data generation using data from the American Community Survey