  Integrating idle embedded devices into cloud computing is a promising
approach to support distributed machine learning. In this paper, we approach to
address the data hiding problem in such distributed machine learning systems.
For the purpose of the data encryption in the distributed machine learning
systems, we propose the Tripartite Asymmetric Encryption theorem and give
mathematical proof. Based on the theorem, we design a general image encryption
scheme ArchNet.The scheme has been implemented on MNIST, Fashion-MNIST and
Cifar-10 datasets to simulate real situation. We use different base models on
the encrypted datasets and compare the results with the RC4 algorithm and
differential privacy policy. Experiment results evaluated the efficiency of the
proposed design. Specifically, our design can improve the accuracy on MNIST up
to 97.26% compared with RC4.The accuracies on the datasets encrypted by ArchNet
are 97.26%, 84.15% and 79.80%, and they are 97.31%, 82.31% and 80.22% on the
original datasets, which shows that the encrypted accuracy of ArchNet has the
same performance as the base model. It also shows that ArchNet can be deployed
on the distributed system with embedded devices.
