Paper 2017/857 Original: eprint.iacr.org/2017/857 ↗
Image Classification using non-linear Support Vector Machines on Encrypted Data
Abstract
In image processing, algorithms for object classification are typically based around machine learning. From the algorithm developer's perspective, these can involve a considerable amount of effort and expertise to develop, which makes them commercially valuable. On the other hand, other parties may want to make use of these algorithms to classify their images, while protecting the privacy of their data. In this paper, we show how non-linear Support Vector Machines (SVMs) can be practically used for image classification on data encrypted with a Somewhat Homomorphic Encryption (SHE) scheme. Previous work has shown how an SVM with a linear kernel can be computed on encrypted data, but this only has limited applicability. By enabling SVMs with polynomial kernels, a much larger class of applications are possible with more accuracy in classification results.
Metadata
- Available format(s)
PDF- Category
- Cryptographic protocols
- Publication info
- Preprint.
- History
- 2017-09-09: last updated
- 2017-09-09: published
- Short URL
- https://ia.cr/2017/857
- License
- https://creativecommons.org/licenses/by/4.0/
BibTeX
@misc{cryptoeprint:2017/857,
author = {Anthony Barnett and Jay Santokhi and Michael Simpson and Nigel P. Smart and Charlie Stainton-Bygrave and Srnivas Vivek and Adrian Waller},
title = {Image Classification using non-linear Support Vector Machines on Encrypted Data},
howpublished = {Cryptology {ePrint} Archive, Paper 2017/857},
year = {2017},
url = {https://eprint.iacr.org/2017/857}
}