Title: Big data security using homomorphic encryption: application in finance

Authors: Tingxin Jiang

Addresses: Beijing Incopat Technology Co., Ltd., Beijing, 100085, China

Abstract: Techniques that protect privacy make it possible to utilise private information without compromising the confidentiality of the information. The use of homomorphic encryption algorithms offers unique ways that make it possible to do computations on encrypted data while still preserving the secrecy of the information that is being protected. The use of homomorphic encryption methods is also discussed in relation to a security framework for big data analysis that is designed to protect individuals' privacy. After that, we will proceed to provide a comparison of the properties that have been discovered in relation to the common homomorphic encryption tools that are now accessible. Analysis is performed on the outcomes of the installation of a variety of different homomorphic encryption toolkits, and a comparison is made between the various performances of each of these kits. The proposed model has an accuracy rate of about 93.75%.

Keywords: big data; encryption algorithms; homomorphic encryption; privacy preserving; machine learning.

DOI: 10.1504/IJESDF.2025.149339

International Journal of Electronic Security and Digital Forensics, 2025 Vol.17 No.6, pp.762 - 775

Received: 11 Jan 2024
Accepted: 04 Mar 2024
Published online: 27 Oct 2025 *

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