Deception through Half-Truths

TL;DR

This paper investigates how strategic hiding or leaking of information, termed 'half-truths', can influence decision-making in dynamic systems, revealing computational hardness and efficient solutions under specific conditions.

Contribution

It models the impact of half-truths on decision-making in dynamic Bayesian networks and analyzes the computational complexity of optimal attack strategies.

Findings

Optimal attack is NP-hard to approximate in general.

Efficient approximation algorithms exist for additive dependency cases.

Linear transition networks allow polynomial-time solutions.

Abstract

Deception is a fundamental issue across a diverse array of settings, from cybersecurity, where decoys (e.g., honeypots) are an important tool, to politics that can feature politically motivated "leaks" and fake news about candidates.Typical considerations of deception view it as providing false information.However, just as important but less frequently studied is a more tacit form where information is strategically hidden or leaked.We consider the problem of how much an adversary can affect a principal's decision by "half-truths", that is, by masking or hiding bits of information, when the principal is oblivious to the presence of the adversary. The principal's problem can be modeled as one of predicting future states of variables in a dynamic Bayes network, and we show that, while theoretically the principal's decisions can be made arbitrarily bad, the optimal attack is NP-hard to approximate, even under strong assumptions favoring the attacker. However, we also describe an important special case where the dependency of future states on past states is additive, in which we can efficiently compute an approximately optimal attack. Moreover, in networks with a linear transition function we can solve the problem optimally in polynomial time.