Protecting Machine Learning Integrity in Distributed Big Data Networking

Abstract

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.