Assessing privacy and quality of synthetic health data
Andrew Yale, Saloni Dash, Ritik Dutta, Isabelle Guyon, Adrien Pavão, Kristin P. Bennett · 2019
This paper builds on the results of the ESANN 2019 conference paper "Privacy Preserving Synthetic Health Data" [16], which develops metrics for assessing privacy and utility of synthetic data and models. The metrics laid out in the initial paper show that utility can still be achieved in synthetic data while maintaining both privacy of the model and the data being generated. Specifically, we focused on the success of the Wasserstein GAN method, renamed HealthGAN, in comparison to other data generating methods.