  In this paper, we propose generating artificial data that retain statistical
properties of real data as the means of providing privacy with respect to the
original dataset. We use generative adversarial network to draw
privacy-preserving artificial data samples and derive an empirical method to
assess the risk of information disclosure in a differential-privacy-like way.
Our experiments show that we are able to generate artificial data of high
quality and successfully train and validate machine learning models on this
data while limiting potential privacy loss.
