Poster

Many differentially private data release solutions have been proposed for different types of data with the sacrifice of inherent correlation structure. Here, we propose a unified framework of releasing differentially private data. In particular, our proposed generative adversarial network (GAN)-based framework learns the input distribution, irrespective of tabular data and graphs, and generates synthetic data in a differentially private manner. Our preliminary results show the acceptable utility of the synthetic dataset.