  Despite the remarkable success of Generative Adversarial Networks (GANs) on
text, images, and videos, generating high-quality tabular data is still under
development owing to some unique challenges such as capturing dependencies in
imbalanced data, optimizing the quality of synthetic patient data while
preserving privacy. In this paper, we propose DP-CGANS, a differentially
private conditional GAN framework consisting of data transformation, sampling,
conditioning, and networks training to generate realistic and
privacy-preserving tabular data. DP-CGANS distinguishes categorical and
continuous variables and transforms them to latent space separately. Then, we
structure a conditional vector as an additional input to not only presents the
minority class in the imbalanced data, but also capture the dependency between
variables. We inject statistical noise to the gradients in the networking
training process of DP-CGANS to provide a differential privacy guarantee. We
extensively evaluate our model with state-of-the-art generative models on three
public datasets and two real-world personal health datasets in terms of
statistical similarity, machine learning performance, and privacy measurement.
We demonstrate that our model outperforms other comparable models, especially
in capturing dependency between variables. Finally, we present the balance
between data utility and privacy in synthetic data generation considering the
different data structure and characteristics of real-world datasets such as
imbalance variables, abnormal distributions, and sparsity of data.
