Differential Privacy Images Protection Based on Generative Adversarial Network

In recent years, as image data are widely used in data analysis tasks, the problem of privacy disclosure is becoming more and more serious. However, the privacy protection technology of image data is still immature. In this paper, we propose a privacy protection framework named dp-WGAN for image data. This framework uses differential privacy and generative adversarial network to train a generative model with privacy protection function. Using this generative model, synthetic data with similar characteristics to sensitive data can be obtained, and synthetic data is published instead sensitive data to complete all kinds of data analysis tasks. Through extensive empirical evaluation on benchmark datasets, we demonstrate that dp-WGAN can provide strong privacy protection for sensitive data and produce high-quality synthetic data.