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Protecting Machine Learning Integrity in Distributed Big Data Networking
IEEE NETWORK ( IF 9.3 ) Pub Date : 2020-07-22 , DOI: 10.1109/mnet.011.1900450
Yunkai Wei , Yijin Chen , Mingyue Xiao , Sabita Maharjan , Yan Zhang

A distributed big data network is the integration of big data and the underlying distributed network. This emerging paradigm brings the potential to divide big data processing tasks into smaller ones so that they can be intelligently processed in parallel with machine learning based on distributed network resources. Such a pattern requires strict system integrity, especially machine learning integrity against data tampering or network control by malicious nodes. In this article, we propose a secure architecture consisting of one HaSi scheme and two data tampering detection schemes for protecting the machine learning integrity in distributed big data networking. Illustrative results demonstrate the effect of our proposed schemes, and show that they can ensure the learning accuracy even when 30-40 percent of processing nodes are maliciously controlled. When the figure raises to 40-50 percent, the accuracy of our proposed schemes begins to fall visibly, but still outperforms the scenario without protection by up to 70-80 percent.

中文翻译:

在分布式大数据网络中保护机器学习完整性

分布式大数据网络是大数据与基础分布式网络的集成。这种新兴的范式带来了将大数据处理任务划分为较小任务的潜力,从而可以与基于分布式网络资源的机器学习并行地对其进行智能处理。这种模式要求严格的系统完整性,尤其是机器学习完整性,以防止数据篡改或恶意节点进行的网络控制。在本文中,我们提出了一种由一个HaSi方案和两个数据篡改检测方案组成的安全体系结构,用于保护分布式大数据网络中的机器学习完整性。说明性结果证明了我们提出的方案的效果,并表明即使恶意控制30-40%的处理节点,它们也可以确保学习准确性。当数字上升到40%到50%时,我们提出的方案的准确性开始明显下降,但仍比不经过保护的方案要高出70%至80%。
更新日期:2020-07-24
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