High-end Equipment Data Desensitization Methods based on Improved Stackelberg GAN

Abstract High-end equipment refers to a type of technical equipment with high technical content, large capital investment, and long development cycle. Therefore, industrial data has extraordinary significance and its desensitization is an urgent problem in data analysis. Traditional data desensitization principles are processing original data such as substitution and adding noise. These methods may not only damage data correlation information, but also result in data disclosure and high computing cost. Given the aforementioned reasons, the study proposes a high-end equipment data desensitization method based on improved Stackelberg Generative Adversarial Networks (GAN). When compared with the normal GAN, the structure proposed in the study includes more generators and discriminators. By inputting the original data, the trained GAN can output indistinguishable data from the original data which helps data mining and also ensures the privacy of data. We experimented on two datasets: optimal improvement was determined by Gaussian dataset experiments, i.e. Stackelberg GAN with eight discriminators. The second experiment results on real-world datasets proved that the 8-discriminator Stackelberg GAN better fits the original data and significantly aids data desensitization.