Using generative adversarial networks (gans) to enable sharing of sensitive data

Abstract

A method generates synthetic data by a data collection system where the synthetic data meets a first threshold for accuracy and a second threshold for protecting sensitive data from recovery from the synthetic data. The method includes collecting data including sensitive data and non-sensitive data, executing a first machine learning model to generate the synthetic data from the collected data where the synthetic data meets the first threshold, executing a second machine learning model to update the synthetic data to meet the second threshold, checking whether the updated synthetic data meets the first threshold, releasing the updated synthetic data where the first threshold is met, and re-executing the first machine learning model and second machine learning model to update the synthetic data where the first threshold is not met during the checking.