Skip to main content

Research Repository

Advanced Search

Evaluation of Privacy-Preserving Support Vector Machine (SVM) Learning Using Homomorphic Encryption

Buchanan, William J.; Ali, Hisham

Authors



Abstract

The requirement for privacy-aware machine learning increases as we continue to use PII (personally identifiable information) within machine training. To overcome the existing privacy issues, we can apply fully homomorphic encryption (FHE) to encrypt data before they are fed into a machine learning model. This involves generating a homomorphic encryption key pair, where the public key encrypts the input data and the private key decrypts the output. However, there is often a performance hit when we use homomorphic encryption, so this paper evaluates the performance overhead of using an SVM (support vector machine) machine learning technique with the OpenFHE homomorphic encryption library. This uses Python and the scikit-learn library to create an SVM model, which can then be used with homomorphically encrypted data inputs and then produce a homomorphically encrypted result. The experiments include a range of variables, such as multiplication depth, scale size, first modulus size, security level, batch size, and ring dimension, along with two different SVM models, SVM-poly and SVM-linear. Overall, the results show that the two main parameters that affect performance are ring dimension and modulus size, and SVM-poly and SVM-linear show similar performance levels.

Citation

Buchanan, W. J., & Ali, H. (2025). Evaluation of Privacy-Preserving Support Vector Machine (SVM) Learning Using Homomorphic Encryption. Cryptography, 9(2), Article 33. https://doi.org/10.3390/cryptography9020033

Journal Article Type Article
Acceptance Date May 21, 2025
Online Publication Date May 26, 2025
Publication Date 2025
Deposit Date May 26, 2025
Publicly Available Date May 27, 2025
Journal Cryptography
Print ISSN 2410-387X
Electronic ISSN 2410-387X
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 9
Issue 2
Article Number 33
DOI https://doi.org/10.3390/cryptography9020033
Keywords homomorphic encryption; support vector machine; privacy-preserving
Public URL http://researchrepository.napier.ac.uk/Output/4518900
Publisher URL https://www.mdpi.com/2410-387X/9/2/33
This output contributes to the following UN Sustainable Development Goals:

SDG 9 - Industry, Innovation and Infrastructure

Build resilient infrastructure, promote inclusive and sustainable industrialisation and foster innovation

Files








You might also like



Downloadable Citations