  We propose a privacy-preserving machine learning scheme with
encryption-then-compression (EtC) images, where EtC images are images encrypted
by using a block-based encryption method proposed for EtC systems with JPEG
compression. In this paper, a novel property of EtC images is first discussed,
although EtC ones was already shown to be compressible as a property. The novel
property allows us to directly apply EtC images to machine learning algorithms
non-specialized for computing encrypted data. In addition, the proposed scheme
is demonstrated to provide no degradation in the performance of some typical
machine learning algorithms including the support vector machine algorithm with
kernel trick and random forests under the use of z-score normalization. A
number of facial recognition experiments with are carried out to confirm the
effectiveness of the proposed scheme.

Cryptography and Security