  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 declarative statistical and logical expressions,
supporting a wide range of requirements (e.g., DP or fairness, among others).
To ensure high synthetic data quality in the presence of custom specifications,
CuTS is pre-trained on the original dataset and fine-tuned on a differentiable
loss automatically derived from the provided specifications using novel
relaxations. We evaluate CuTS over four datasets and on numerous custom
specifications, outperforming state-of-the-art specialized approaches on
several tasks while being more general. In particular, at the same fairness
level, we achieve 2.3% higher downstream accuracy than the state-of-the-art in
fair synthetic data generation on the Adult dataset.
