Homomorphic encryption performance in secure data processing for machine learning

Gowda, V. Dankan; Praveena, V.; Nagabhushanam, M.; Kumar, Pullela SVVSR; Lezama, Christian Rafael Quevedo; Pathak, Disha

doi:10.47974/JDMSC-2150

Open Access Research Article

Homomorphic encryption performance in secure data processing for machine learning

^*V. Dankan GowdaCorresponding authordankan.v@bmsit.inDepartment of Electronics and Communication Engineering BMS Institute of Technology and ManagementBangalore, Karnataka, 560119, IndiaView full profile → , V. Praveenadrvpraveena@gmail.comDepartment of Computer Science and Engineering Dr NGP Institute of TechnologyCoimbatore, Tamil Nadu, IndiaView full profile → , M. Nagabhushanamnagabhushanam1971@msrit.eduDepartment of Electronics and Communication Engineering M S Ramaiah Institute of Technology MSR Nagar, MSRIT PostBengaluru, Karnataka, IndiaView full profile → , Pullela SVVSR Kumarpullelark@yahoo.comDepartment of Computer Science & Engineering Aditya University Surampalem, Andhra Pradesh, 533437, IndiaView full profile → , Christian Rafael Quevedo Lezamachristian.quevedo1@unmsm.edu.peDepartment of Electrical and Electronics Engineering Universidad Nacional Mayor de San Marcos Avenida Carlos Germán Amezaga 375 - Cercado de LimaPeruView full profile → , Disha Pathakdisha.pathak@scmshyd.siu.edu.inDepartment of Finance Symbiosis Centre for Management Studies; Symbiosis International (Deemed University)Department of Finance Symbiosis Centre for Management StudiesHyderabad, Telangana, IndiaView full profile →

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pp. 1517–1526Vol. 28Issue 5-AAugust 2025DOI: 10.47974/JDMSC-2150 Crossmark XML

Received:: 05 Nov 2024
Published Online:: 30 Aug 2025
Article type:: Research Article
Language:: EN
Article no.:: JDMSC-2150
Pages:: 1517–1526

Abstract

In terms of data privacy and security especially in the development of machine learning HE has the potential to solve it. This paper focuses on homomorphic encryption as a method for secure data processing with reference to encryption techniques that allow arithmetic operations to be performed on encrypted data for use in machine learning. When comparing different HE methods, their computational complexity, as well as the obtained precision, have been analyzed, and practical suitability of the methods in question has been discussed, that is, what actual problem-solving can be done using the methods in question. That was done in a way that does not in any way encroach into the security of the data to ensure that we do not compromise it in the proposed method used to incorporate HE with machine learning models. The provided findings of experiments show that the application of HE in experiments secured data is valid while establishing the cost between security measures and computation time. Chen’s works provide knowledge on how more efficient, personal sensitive preserving machine learning can be developed and integrated.

Keywords

Homomorphic encryption Secure data processing Machine learning Privacy-preserving Computational efficiency Encryption schemes Data confidentiality Performance evaluation

Subject Classifications

94A6020C0520C07

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