  In recent years, with the development of cloud computing platforms,
privacy-preserving methods for deep learning have become an urgent problem.
NeuraCrypt is a private random neural network for privacy-preserving that
allows data owners to encrypt the medical data before the data uploading, and
data owners can train and then test their models in a cloud server with the
encrypted data directly. However, we point out that the performance of
NeuraCrypt is heavily degraded when using color images. In this paper, we
propose a Color-NeuraCrypt to solve this problem. Experiment results show that
our proposed Color-NeuraCrypt can achieve a better classification accuracy than
the original one and other privacy-preserving methods.
