Practical implementations of homomorphic encryption examining performance and security in real-world privacy-preserving applications

Agarwal, Ambuj Kumar; Pooja; Joshi, Mukesh; Sheoran, Snehlata; Banda, Latha; Saini, Garima

doi:10.47974/JDMSC-2546

Open Access Research Article

Practical implementations of homomorphic encryption examining performance and security in real-world privacy-preserving applications

Ambuj Kumar Agarwalambuj4u@gmail.comDepartment of Computer Science and Engineering Sharda School of Engineering and Technology Sharda UniversityGreater Noida, Uttar Pradesh, 201306, IndiaView full profile → , Poojapooja@shardauniversity.uzDepartment of Computing School of Law, Technology and Education Westminster International University in Tashkent (WIUT)Tashkent, UzbekistanView full profile → , ^*Mukesh JoshiCorresponding authormukul.san@gmail.comSchool of Computing Graphic Era Hill University Bhimtal Campus BhowaliNainital, Uttarakhand, 263156, IndiaView full profile → , Snehlata Sheoransnehsheoran312@gmail.comDepartment of Computer Science & Engineering The NorthCap UniversityGurugram, Haryana, 122017, IndiaView full profile → , Latha Bandalathabanda1104@gmail.comDepartment of Computer Science & Engineering JIMS Engineering Management TechnicalGreater Noida, Uttar Pradesh, 201308, IndiaView full profile → , Garima Sainisainidrgarima0211@gmail.comDepartment of Information Technology Institute of Innovation in Technology & ManagementJanakpuri, Delhi, 110058, IndiaView full profile →

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pp. 953–960Vol. 29Issue 2-BFebruary 2026DOI: 10.47974/JDMSC-2546 Crossmark XML

Received:: 15 Apr 2025
Published Online:: 31 Dec 2025
Article type:: Research Article
Language:: EN
Article no.:: JDMSC-2546
Pages:: 953–960

Abstract

Homomorphic Encryption (HE) makes it possible to compute on encrypted data without decrypting, thereby enabling secure data analysis. The goal of this research is to evaluate the use of HE schemes like Paillier, BFV and CKKS in sensitive real-world scenarios such as health care and financial industries. The study focuses on the performance, scalability and security implications of applying these encryption methods to encrypted machine learning models. Performance is evaluated using various criteria, including encryption time, computation overhead, model accuracy and memory utilization. A hybrid approach using two or more schemes is suggested to strike a balance between security and computational efficiency. Paillier shows a lower memory footprint, whereas CKKS delivers a stronger model accuracy for approximate calculations. The hybrid approach outperforms each of its components by offering improved processing speed and better model accuracy. Security evaluations also demonstrate that confidentiality is preserved against potential ciphertext attacks during computation. The findings in this study can be valuable for implementing HE in sensitive contexts, facilitating the development of secure data analysis techniques. These outcomes demonstrate how HE can be effectively used to ensure secure and privacy-preserving calculations in a variety of practical applications.

Keywords

Homomorphic encryption Privacy-preserving computation CKKS BFV Data security

Subject Classifications

Primary 93A30Secondary 49K15

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