CuTS: Customizable Tabular Synthetic Data Generation

arXiv:2307.03577v3 Announce Type: replace
Abstract: Privacy, data quality, and data sharing concerns pose a key limitation for tabular data applications. While generating synthetic data resembling the original distribution addresses some of these issues, most applications would benefit from additional customization on the generated data. However, existing synthetic data approaches are limited to particular constraints, e.g., differential privacy (DP) or fairness. In this work, we introduce CuTS, the first customizable synthetic tabular data generation framework. Customization in CuTS is achieved via …

abstract applications arxiv benefit concerns constraints cs.db cs.lg cs.pl customization data data applications data quality data sharing distribution generated key privacy quality synthetic synthetic data tabular tabular data type