  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
unauthorized use and leak of data, and privacy compromise. We focus on
templates protected by using a random unitary transformation, and consider some
properties of the protected templates for secure SVM computing, where templates
mean features extracted from data. The proposed scheme enables us not only to
protect templates, but also to have the same performance as that of unprotected
templates under some useful kernel functions. Moreover, it can be directly
carried out by using well-known SVM algorithms, without preparing any
algorithms specialized for secure SVM computing. In the experiments, the
proposed scheme is applied to a face-based authentication algorithm with SVM
classifiers to confirm the effectiveness.
