  While data sharing is crucial for knowledge development, privacy concerns and
strict regulation (e.g., European General Data Protection Regulation (GDPR))
unfortunately limit its full effectiveness. Synthetic tabular data emerges as
an alternative to enable data sharing while fulfilling regulatory and privacy
constraints. The state-of-the-art tabular data synthesizers draw methodologies
from generative Adversarial Networks (GAN) and address two main data types in
the industry, i.e., continuous and categorical. In this paper, we develop
CTAB-GAN, a novel conditional table GAN architecture that can effectively model
diverse data types, including a mix of continuous and categorical variables.
Moreover, we address data imbalance and long-tail issues, i.e., certain
variables have drastic frequency differences across large values. To achieve
those aims, we first introduce the information loss and classification loss to
the conditional GAN. Secondly, we design a novel conditional vector, which
efficiently encodes the mixed data type and skewed distribution of data
variable. We extensively evaluate CTAB-GAN with the state of the art GANs that
generate synthetic tables, in terms of data similarity and analysis utility.
The results on five datasets show that the synthetic data of CTAB-GAN
remarkably resembles the real data for all three types of variables and results
into higher accuracy for five machine learning algorithms, by up to 17%.
