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Homomorphic Encryption and Machine Learning in the Encrypted Domain
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Author(s): Neethu Krishna (SCMS School of Engineering and Technology, Karukutty, India), Kommisetti Murthy Raju (Shri Vishnu Engineering College For Women, India), V. Dankan Gowda (BMS Institute of Technology and Management, India), G. Arun (Erode Sengunthar Engineering College, India)and Sampathirao Suneetha (Andhra University College of Engineering, Andhra University, India)
Copyright: 2024
Pages: 18
Source title:
Innovative Machine Learning Applications for Cryptography
Source Author(s)/Editor(s): J. Anitha Ruth (SRM Institute of Science and Technology, Vadapalani, India), G.V. Mahesh Vijayalakshmi (BMS Institute of Technology and Management, India), P. Visalakshi (Department of Networking and Communications, College of Engineering and Technology, SRM Institute of Science and Technology, Katankulathur, India), R. Uma (Sri Sairam Engineering College, Chennai, India)and A. Meenakshi (SRM Institute of Science and Technology, Vadapalani, India)
DOI: 10.4018/979-8-3693-1642-9.ch010
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Abstract
In cryptography, performing computations on encrypted material without first decrypting it has long been an aspiration. This is exactly what homomorphic encryption (HE) accomplishes. By allowing computation on encrypted data, the associated privacy and security of sensitive information are beyond imagination to date. This chapter delves into the vast and intricate realm of HE, its fundamental theories, and far-reaching implications for machine learning. As a result of the sensitive nature of the data on which machine learning is based, privacy and security issues often arise. In this vein, homomorphic encryption, which allows algorithms to learn from and predict encrypted data, emerges as a possible panacea. The authors thus set out in this chapter to prepare the ground for a deeper understanding of that synergy, showing how it is there but also what lies ahead.
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