On-Demand Anonymous Access and Roaming Authentication Protocols for 6G Satellite-Ground Integrated Networks.

Satellite-ground integrated networks (SGIN) are in line with 6th generation wireless network technology (6G) requirements. However, security and privacy issues are challenging with heterogeneous networks. Specifically, although 5G authentication and key agreement (AKA) protects terminal anonymity, privacy preserving authentication protocols are still important in satellite networks. Meanwhile, 6G will have a large number of nodes with low energy consumption. The balance between security and performance needs to be investigated. Furthermore, 6G networks will likely belong to different operators. How to optimize the repeated authentication during roaming between different networks is also a key issue. To address these challenges, on-demand anonymous access and novel roaming authentication protocols are presented in this paper. Ordinary nodes implement unlinkable authentication by adopting a bilinear pairing-based short group signature algorithm. When low-energy nodes achieve fast authentication by utilizing the proposed lightweight batch authentication protocol, which can protect malicious nodes from DoS attacks. An efficient cross-domain roaming authentication protocol, which allows terminals to quickly connect to different operator networks, is designed to reduce the authentication delay. The security of our scheme is verified through formal and informal security analysis. Finally, the performance analysis results show that our scheme is feasible.

A Method of Information Protection for Collaborative Deep Learning under GAN Model Attack.

Deep learning is widely used in the medical field owing to its high accuracy in medical image classification and biological applications. However, under collaborative deep learning, there is a serious risk of information leakage based on the deep convolutional generation against the network's privacy protection method. Moreover, the risk of such information leakage is greater in the medical field. This paper proposes a deep convolution generative adversarial networks (DCGAN) based privacy protection method to protect the information of collaborative deep learning training and enhance its stability. The proposed method adopts encrypted transmission in the process of deep network parameter transmission. By setting the buried point to detect a generative adversarial network (GAN) attack in the network and adjusting the training parameters, training based on the GAN model attack is forced to be invalid, and the information is effectively protected.