  In this paper, we propose a novel privacy-preserving machine learning scheme
with encrypted images, called EtC (Encryption-then-Compression) images. Using
machine learning algorithms in cloud environments has been spreading in many
fields. However, there are serious issues with it for end users, due to
semi-trusted cloud providers. Accordingly, we propose using EtC images, which
have been proposed for EtC systems with JPEG compression. In this paper, a
novel property of EtC images is considered under the use of z-score
normalization. It is demonstrated that the use of EtC images allows us not only
to protect visual information of images, but also to preserve both the
Euclidean distance and the inner product between vectors. In addition,
dimensionality reduction is shown to can be applied to EtC images for fast and
accurate matching. In an experiment, the proposed scheme is applied to a facial
recognition algorithm with classifiers for confirming the effectiveness of the
scheme under the use of support vector machine (SVM) with the kernel trick.
