You are looking at a specific version 20171213:161002 of this paper. See the latest version.

Paper 2017/1190

PICS: Private Image Classification with SVM

Eleftheria Makri and Dragos Rotaru and Nigel P. Smart and Frederik Vercauteren

Abstract

The advent of Machine Learning as a Service (MLaaS) makes it possible to outsource a visual object recognition task to an external (\eg cloud) provider. However, outsourcing such an image classification task raises privacy concerns, both from the image provider's perspective, who wishes to keep their images confidential, and from the classification algorithm provider's perspective, who wishes to protect the intellectual property of their classifier. We propose PICS, a private image classification system, based on polynomial kernel support vector machine (SVM) learning. We selected SVM because it allows us to apply only low-degree functions for the classification on private data, which is the reason why our solution remains computationally efficient. Our solution is based on Secure Multiparty Computation (MPC), it does not leak any information about the images to be classified, nor about the classifier parameters, and it is provably secure. We demonstrate the practicality of our approach by conducting experiments on realistic datasets. We show that our approach achieves high accuracy, comparable to that achieved on non-privacy-protected data while the input-dependent phase is at least 100 times faster than the similar approach with Fully Homomorphic Encryption.

Note: minor changes to abstract and a subsection

Metadata
Available format(s)
PDF
Publication info
Preprint. MINOR revision.
Keywords
multiparty computationmachine learningsupport vector machines
Contact author(s)
dragos rotaru @ bristol ac uk
History
2018-12-02: last of 8 revisions
2017-12-12: received
See all versions
Short URL
https://ia.cr/2017/1190
License
Creative Commons Attribution
CC BY
Note: In order to protect the privacy of readers, eprint.iacr.org does not use cookies or embedded third party content.