Comparison of Machine Learning Algorithms for Homomorphic Encryption

Abstract

The proposed paper is study of comparison of various machine learning algorithms based on computational complexities both in time and space complexity. The objective of this paper is to find widely used algorithm for   Homomorphic Encryption in real time. Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without being explicitly programmed.  Homomorphic encryption is the conversion of data into cipher text that can be analysed and worked with as if it were still in its original form. Homomorphic encryption is a paradigm that allows running arbitrary operations on encrypted data. This paper is a literature review on the symbiotic relationship between machine learning and encryption. It enables us to run any sophisticated machine learning algorithm without access to the underlying raw data. Some of application of Homomorphic Encryption for Machine Learning   in Medicine and Bioinformatics, financial service sector.