Biblio

In recent years, artificial intelligence technology based on image data has been widely used in various industries. Rational analysis and mining of image data can not only promote the development of the technology field but also become a new engine to drive economic development. However, the privacy leakage problem has become more and more serious. To solve the privacy leakage problem of image data, this paper proposes the RDP-WGAN privacy protection framework, which deploys the Rényi differential privacy (RDP) protection techniques in the training process of generative adversarial networks to obtain a generative model with differential privacy. This generative model is used to generate an unlimited number of synthetic datasets to complete various data analysis tasks instead of sensitive datasets. Experimental results demonstrate that the RDP-WGAN privacy protection framework provides privacy protection for sensitive image datasets while ensuring the usefulness of the synthetic datasets.