A Survey of Machine Learning and Cryptography Algorithms

Abstract

The intersection of machine learning and encryption has emerged as a key area in technology. A model shift in technology and data security has brought the combination of machine learning and encryption. In order to provide insight on the underlying algorithms and techniques, this survey was taken between the domains. It presents an overview of machine learning and cryptography algorithms. A wide variety of algorithms are examined in the field of machine learning. This survey also clarifies the interaction between machine learning and cryptography, demonstrating how these two fields work together to produce privacy-preserving ML, secure authentication, anomaly detection, and other benefits. A new era of data privacy and security has methods like secure multi-party computation (SMPC) and homomorphic encryption, which allow calculations on encrypted data. An updated overview of machine learning techniques used in cryptography is presented in this survey. The report offers recommendations for future study initiatives and summarizes the work.