  Generative adversarial networks (GANs) have drawn considerable attention in
recent years for their proven capability in generating synthetic data which can
be utilised for multiple purposes. While GANs have demonstrated tremendous
successes in producing synthetic data samples that replicate the dynamics of
the original datasets, the validity of the synthetic data and the underlying
privacy concerns represent major challenges which are not sufficiently
addressed. In this work, we design a cascaded tabular GAN framework (CasTGAN)
for generating realistic tabular data with a specific focus on the validity of
the output. In this context, validity refers to the the dependency between
features that can be found in the real data, but is typically misrepresented by
traditional generative models. Our key idea entails that employing a cascaded
architecture in which a dedicated generator samples each feature, the synthetic
output becomes more representative of the real data. Our experimental results
demonstrate that our model is capable of generating synthetic tabular data that
can be used for fitting machine learning models. In addition, our model
captures well the constraints and the correlations between the features of the
real data, especially the high dimensional datasets. Furthermore, we evaluate
the risk of white-box privacy attacks on our model and subsequently show that
applying some perturbations to the auxiliary learners in CasTGAN increases the
overall robustness of our model against targeted attacks.
