Research on Homomorphic Encryption Methods for Financial Data Based on Parameter Optimization and Hybrid Architecture

Authors

  • Jingming Hu

DOI:

https://doi.org/10.61173/07r22n10

Keywords:

homomorphic encryption, parameter optimization, hybrid architecture, financial data

Abstract

This study addresses the privacy protection requirements in financial data analysis by proposing a homomorphic encryption method that integrates CKKS parameter optimization with a hybrid FHE-PHE architecture. Through orthogonal experimental design, the optimal parameter combinations are selected, resulting in significantly improved encryption time compared to conventional encryption methods and greater stability in encryption time compared to FHE.  

References

[1] Cheon J H, et al. Homomorphic Encryption for Arithmetic of Approximate Numbers. ASIACRYPT 2017

[2] Gentry C. Fully Homomorphic Encryption Using Ideal Lattices. STOC 2009

[3] China Communications Standards Association. Privacy Computing Technical Specifications. 2023

[4] Microsoft SEAL Documentation. 2023

[5] Ant Technology Research Institute. Practical Performance Optimization of Homomorphic AES. 2023

[6] Tsinghua University Privacy Computing Laboratory. Adaptation Guidelines for Homomorphic Encryption in Financial Data Scenarios. 2024

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Published

2025-10-23

Issue

Vol. 3 No. 5 (2025): Volume 3, Issue 5

Section

Articles

License

Copyright (c) 2025 by the authors.

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.