🤖 AI Summary
Existing open-source tools lack a unified infrastructure, making it challenging to efficiently support the diverse modeling choices and privacy requirements inherent in synthetic data generation. This work proposes tidysynthesis, a modular and extensible meta-package that integrates a wide range of statistical modeling and differential privacy techniques through a unified declarative API. For the first time, it enables cross-framework algorithm composition and flexible customization of synthetic data workflows. The system substantially enhances both development efficiency and privacy guarantees, with its completeness, usability, and capacity to handle complex synthesis tasks demonstrated through empirical validation on U.S. Census survey data.
📝 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