Enhancing Cloud Security through Efficient Polynomial Approximations for Homomorphic Evaluation of Neural Network Activation Functions

Pulido-Gaytan, Bernardo, Tchemykh, Andrei, Babenko, Mikhail, Cortés-Mendoza, Jorge M., González-Vélez, Horacio and Avetisyan, Arutyun (2024) Enhancing Cloud Security through Efficient Polynomial Approximations for Homomorphic Evaluation of Neural Network Activation Functions. In: 2024 IEEE 24th International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW). IEEE, Philadelphia, PA, USA, pp. 42-49. ISBN 979-8-3503-7751-4

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Abstract

Current security cloud practices can successfully protect stored data and data in transit, but they do not keep the same protection during data processing. The data value extraction requires decryption, creating critical exposure points. As a result, privacy-preserving techniques are emerging as a crucial consideration in cloud computing. The homomorphic processing of machine learning models in the cloud represents a central challenge. The activation function is fundamental in constructing a privacy-preserving Neural Network (NN) with Homomorphic Encryption (HE). Standard activation functions require operations not supported by HE, so it is necessary to find cryptographically compatible replacement functions to operate over encrypted data. Multiple approaches address the limitation of function compatibility with polynomial approximation. These functions should exhibit a trade-off between complexity and accuracy, limiting the efficiency of conventional approximation techniques. The current literature on polynomial approximation of NN activation functions still lacks a thorough review. In this paper, we comprehensively review the standard activation functions of modern NN models and current polynomial approximation approaches. We highlight fundamental features to consider in the activation function and the approximation technique to operate over encrypted data.

Item Type:	Book Section
Uncontrolled Keywords:	cloud security; homomorphic encryption; neural networks; polynomial approximation; privacy-preserving
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Cloud computing Q Science > QA Mathematics > Computer software > Computer Security T Technology > T Technology (General) > Information Technology > Computer software > Computer Security
Divisions:	School of Computing > Staff Research and Publications
Depositing User:	Tamara Malone
Date Deposited:	04 Nov 2024 15:52
Last Modified:	04 Nov 2024 15:52
URI:	https://norma.ncirl.ie/id/eprint/7140

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