Robustness of Information Diffusion Algorithms to Locally Bounded Adversaries

TL;DR

This paper introduces the concept of r-robustness in networks, showing it enhances the ability to withstand malicious nodes during information diffusion, and provides conditions for successful consensus and broadcasting.

Contribution

It defines r-robustness as a new topological property that improves bounds on tolerating malicious behavior compared to traditional metrics.

Findings

r-robustness improves malicious node tolerance

Conditions for consensus and broadcasting success are established

Preferential-attachment networks can be r-robust

Abstract

We consider the problem of diffusing information in networks that contain malicious nodes. We assume that each normal node in the network has no knowledge of the network topology other than an upper bound on the number of malicious nodes in its neighborhood. We introduce a topological property known as r-robustness of a graph, and show that this property provides improved bounds on tolerating malicious behavior, in comparison to traditional concepts such as connectivity and minimum degree. We use this topological property to analyze the canonical problems of distributed consensus and broadcasting, and provide sufficient conditions for these operations to succeed. Finally, we provide a construction for r-robust graphs and show that the common preferential-attachment model for scale-free networks produces a robust graph.