The field of secure communication and information theory is rapidly evolving, with a focus on developing innovative methods to protect against eavesdropping and ensure reliable data transmission. Recent research has explored the integration of physical layer security with deception techniques, resulting in proactive countermeasures against eavesdropping. Additionally, there is a growing interest in semantic communication models, which prioritize the meaning and context of information over traditional metrics. Noteworthy papers in this area include the introduction of Bilinear Compressive Security, which addresses the shortcomings of standard compressed sensing methods, and the development of a semantic generalization of Shannon's information theory. Other notable works include the proposal of new methods for measuring conflict in random permutation sets and the design of deep feedback codes for wiretap channels. Overall, these advancements have the potential to significantly enhance the security and efficiency of communication systems.
Advances in Secure Communication and Information Theory
Sources
Category learning in deep neural networks: Information content and geometry of internal representations
IB-GAN: Disentangled Representation Learning with Information Bottleneck Generative Adversarial Networks
Information Gradient for Nonlinear Gaussian Channel with Applications to Task-Oriented Communication