  In this paper, we propose an extraction method of HOG
(histograms-of-oriented-gradients) features from encryption-then-compression
(EtC) images for privacy-preserving machine learning, where EtC images are
images encrypted by a block-based encryption method proposed for EtC systems
with JPEG compression, and HOG is a feature descriptor used in computer vision
for the purpose of object detection and image classification. Recently, cloud
computing and machine learning have been spreading in many fields. However, the
cloud computing has serious privacy issues for end users, due to unreliability
of providers and some accidents. Accordingly, we propose a novel block-based
extraction method of HOG features, and the proposed method enables us to carry
out any machine learning algorithms without any influence, under some
conditions. In an experiment, the proposed method is applied to a face image
recognition problem under the use of two kinds of classifiers: linear support
vector machine (SVM), gaussian SVM, to demonstrate the effectiveness.
