ArchNet: Data Hiding Model in Distributed Machine Learning System

Authors: Kaiyan Chang, Wei Jiang, Jinyu Zhan, Zicheng Gong, Weijia Pan
Published: 2020-04-23 | Updated: 2020-05-31

Source: https://arxiv.org/abs/2004.10968

PDF: https://arxiv.org/pdf/2004.10968

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機械学習 暗号化プロトコル 画像処理

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Abstract

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 are 97.26 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.