Privacy-Preserving Support Vector Machine Computing Using Random Unitary Transformation

TL;DR

This paper introduces a privacy-preserving SVM scheme using random unitary transformations that protects image data in cloud computing without sacrificing classification performance, applicable with standard SVM algorithms.

Contribution

It presents a novel method for privacy-preserving SVM that maintains accuracy and can be integrated with existing SVM algorithms without modifications.

Findings

Protects visual information of images effectively.

Maintains SVM classification performance with standard kernels.

Successfully applied to face-based authentication.

Abstract

A privacy-preserving support vector machine (SVM) computing scheme is proposed in this paper. Cloud computing has been spreading in many fields. However, the cloud computing has some serious issues for end users, such as the unauthorized use of cloud services, data leaks, and privacy being compromised. Accordingly, we consider privacy-preserving SVM computing. We focus on protecting visual \red{information} of images by using a random unitary transformation. Some properties of the protected images are discussed. The proposed scheme enables us not only to protect images, but also to have the same performance as that of unprotected images even when using typical kernel functions such as the linear kernel, radial basis function(RBF) kernel and polynomial kernel. Moreover, it can be directly carried out by using well-known SVM algorithms, without preparing any algorithms specialized for secure SVM computing. In an experiment, the proposed scheme is applied to a face-based authentication algorithm with SVM classifiers to confirm the effectiveness.