Anti-AsynDGAN: Black-box Membership Inference Attacks Against Medical Distributed Generation Models

Recently, Distributed Asynchronized Discriminator GAN (AsynDGAN), one of the representatives of distributed generative adversarial networks, has received much attention in the medical field. It provides a collaborative model training approach to protect raw medical data from leaving the local medical entity. However, does raw data not leave the local client to ensure absolute data security? And potential privacy risks associated with distributed generative adversarial networks have not been systematically analyzed. In this paper, we design and implement two targeted membership inference attacks against AsynDGAN, i.e., global and local black-box attacks, by investigating the possible vulnerabilities during the training and deployment of AsynDGAN, respectively. Experimental results on three widely-used and multi-modality medical datasets show that AsynDGAN leads to serious leakage of sensitive information and does not effectively protect private-sensitive medical data.