  In this paper, we propose a novel learnable image encryption method for
privacy-preserving deep neural networks (DNNs). The proposed method is carried
out on the basis of block scrambling used in combination with data augmentation
techniques such as random cropping, horizontal flip and grid mask. The use of
block scrambling enhances robustness against various attacks, and in contrast,
the combination with data augmentation enables us to maintain a high
classification accuracy even when using encrypted images. In an image
classification experiment, the proposed method is demonstrated to be effective
in privacy-preserving DNNs.
