Secure Key Generation and Management Using Generative Adversarial Networks

Abstract

The generation and control of cryptographic keys are the most important things when it comes to the security and integrity of encrypted data. The traditional key generation methods including pseudorandom number generation often fail to generate truly random values and become predictable as well. This chapter explores the potential of generative adversarial networks (GANs) for secure key generation and management. GANs, a very powerful deep learning architecture, can produce the statistical properties of true randomness utilizing adversarial training, thus generating key sequences that are random and unpredictable. The chapter proceeds with the essence of cryptographic key generation, key management lifecycle, GAN architectures, training strategies, and evaluation techniques for key randomness and security. Furthermore, it explores how this approach is applied for key distribution, synchronization, revocation, and updates, and these issues are analyzed, with a special focus on scalability and performance.