Encryption and Privacy Preserving Strategies in Deep Learning- Review

Abstract

Deep neural networks are powerful tools in artificial intelligence, proven effective in diverse applications ranging from image and text processing to encryption. Advances in deep learning technologies have enabled the use of these models in data security. Deep network encryption aims to enhance information security by leveraging the superior pattern-analysis and feature-extraction capabilities of these models, resulting in encryption that surpasses traditional methods. Deep networks benefit from their ability to learn massive amounts of data, enabling them to develop accurate and efficient encryption models. This approach opens new horizons in information security, and encryption methods can provide a high level of protection against escalating cyber threats. Encryption using this approach represents a promising field that combines security with modern technology. Despite the challenges associated with implementing this technology, such as the need for large amounts of data to train the models and ensure their efficiency, its potential benefits make it a promising area for research and development. The ability to provide innovative encryption solutions that contribute to protecting privacy and data confidentiality makes this topic vital and worthy of further study. In this context, examining deep learning-based encryption techniques is a crucial step toward enhancing data security in an increasingly complex world. A deep understanding of how to effectively leverage these techniques will pave the way for developing new solutions that contribute to strengthening cybersecurity and achieving advanced data protection in various sectors. In this paper, we review privacy preserving techniques using deep learning, drawing on research papers published in scientific journals and conference proceedings. We focus on emerging techniques and deep learning models that have proven effective in the field of privacy security. We evaluate these models using precise cryptographic metrics such as entropy, NPCR, and others.