Privacy-Preserving Support Vector Machine Computing Using Random Unitary Transformation
This addresses privacy issues for users of cloud-based image processing systems, but it is incremental as it builds on existing SVM methods with a specific protection technique.
The paper tackles privacy concerns in cloud-based SVM computing by proposing a scheme that uses random unitary transformation to protect image visual information while maintaining classification performance with standard kernel functions, achieving the same accuracy as unprotected images in experiments.
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.