Improving Data Encryption with Generative Adversarial Networks (GANs)

Abstract

The paper addresses the importance of data protection in the digital age, where sensitive information is increasingly threatened by cyber-attacks. Encryption is one of the fundamental methods for protecting this data, but challenges related to security and performance efficiency remain. In this context, generative neural networks emerge as an innovative tool for enhancing encryption techniques.                                                                         Generative neural networks are based on the idea of two models: a generator, which seeks to generate new data, and a discriminator, which aims to distinguish between real and fake data. Through this competitive process, generative neural networks can improve the quality of the generated data and one of its most popular applications is generating encryption keys: Generative neural networks can be used to generate unique and complex encryption keys, making it more difficult to crack security systems.

Improving encryption algorithms by learning from data patterns.                                                                                                                                           Generative neural networks can improve existing algorithms, leading to more efficient encryption Generated Data Encryption: Generative neural networks can be used to securely generate new data representing sensitive information, reducing the risk of data leakage.