Paper 2017/857

Image Classification using non-linear Support Vector Machines on Encrypted Data

Anthony Barnett, Jay Santokhi, Michael Simpson, Nigel P. Smart, Charlie Stainton-Bygrave, Srnivas Vivek, and Adrian Waller


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.

Available format(s)
Cryptographic protocols
Publication info
Preprint. MINOR revision.
Contact author(s)
nigel @ cs bris ac uk
sv venkatesh @ bristol ac uk
Anthony Barnett @ uk thalesgroup com
Adrian Waller @ uk thalesgroup com
2017-09-09: received
Short URL
Creative Commons Attribution


      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},
      note = {\url{}},
      url = {}
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