Deception in Social Learning

TL;DR

This paper investigates how malicious agents can deceive social learning networks by manipulating likelihood functions, revealing conditions and strategies for successful inferential attacks in both informed and uninformed scenarios.

Contribution

It introduces a framework for understanding deception in social learning, characterizes conditions for successful attacks, and proposes optimization-based strategies for malicious agents.

Findings

Deception strategies can always be constructed when agents know the network model.

Attack strategies can succeed even without knowledge of the true hypothesis.

Simulations demonstrate the effectiveness of the proposed deception methods.

Abstract

A common assumption in the social learning literature is that agents exchange information in an unselfish manner. In this work, we consider the scenario where a subset of agents aims at deceiving the network, meaning they aim at driving the network beliefs to the wrong hypothesis. The adversaries are unaware of the true hypothesis. However, they will "blend in" by behaving similarly to the other agents and will manipulate the likelihood functions used in the belief update process to launch inferential attacks. We will characterize the conditions under which the network is misled. Then, we will explain that it is possible for such attacks to succeed by showing that strategies exist that can be adopted by the malicious agents for this purpose. We examine both situations in which the agents have access to information about the network model as well as the case in which they do not. For the first case, we show that there always exists a way to construct fake likelihood functions such that the network is deceived regardless of the true hypothesis. For the latter case, we formulate an optimization problem and investigate the performance of the derived attack strategy by establishing conditions under which the network is deceived. We illustrate the learning performance of the network in the aforementioned adversarial setting via simulations. In a nutshell, we clarify when and how a network is deceived in the context of non-Bayesian social learning.