论文标题
Archnet：分布式机器学习系统中的数据隐藏模型
ArchNet: Data Hiding Model in Distributed Machine Learning System
论文作者
论文摘要
将嵌入式设备集成到云计算中是支持分布式机器学习的一种有前途的方法。在本文中，我们解决了解决此类分布式机器学习系统中的数据隐藏问题的方法。为了在分布式机器学习系统中的数据加密目的，我们提出了三方不对称的加密定理并提供数学证明。基于定理，我们设计了一个通用图像加密方案ARPHNET。该方案已在MNIST，Fashion-Mnist和Cifar-10数据集上实现，以模拟真实情况。我们在加密数据集中使用不同的基本模型，并将结果与RC4算法和差异隐私策略进行比较。实验结果评估了拟议设计的效率。具体而言，与RC4相比，我们的设计可以提高MNIST的准确性97.26％。Archnet加密的数据集上的准确性为97.26％，84.15％和79.80％，它们的准确性为97.31％，82.31％和82.31％和80.22％的原始数据集，该数据均具有准确的效果，该数据均具有准确的效果。这也表明，可以将Archnet部署在带有嵌入式设备的分布式系统上。
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.
