  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.
