Security of Distributed Machine Learning: A Game-Theoretic Approach to Design Secure DSVM
This addresses security risks in distributed machine learning for applications relying on ICTs, but it is incremental as it builds on existing game-theoretic and SVM methods.
The paper tackles the vulnerability of distributed support vector machines (SVMs) to data poisoning and network attacks by developing a game-theoretic framework and a distributed iterative algorithm to secure them, with numerical results showing that attack impact depends on network structure and capabilities.
Distributed machine learning algorithms play a significant role in processing massive data sets over large networks. However, the increasing reliance on machine learning on information and communication technologies (ICTs) makes it inherently vulnerable to cyber threats. This work aims to develop secure distributed algorithms to protect the learning from data poisoning and network attacks. We establish a game-theoretic framework to capture the conflicting goals of a learner who uses distributed support vector machines (SVMs) and an attacker who is capable of modifying training data and labels. We develop a fully distributed and iterative algorithm to capture real-time reactions of the learner at each node to adversarial behaviors. The numerical results show that distributed SVM is prone to fail in different types of attacks, and their impact has a strong dependence on the network structure and attack capabilities.