MAAICYDSGTMay 28, 2019

A Parameterized Perspective on Protecting Elections

arXiv:1905.11838v110 citations
Originality Synthesis-oriented
AI Analysis

This work addresses election security for computational social choice researchers, but it is incremental as it extends known complexity results to parameterized settings.

The paper tackles the problem of protecting elections from manipulation by studying the parameterized complexity of optimal defense and attack strategies in voting systems, showing that these problems are W[2]-hard for natural parameters, indicating computational intractability in many cases.

We study the parameterized complexity of the optimal defense and optimal attack problems in voting. In both the problems, the input is a set of voter groups (every voter group is a set of votes) and two integers $k_a$ and $k_d$ corresponding to respectively the number of voter groups the attacker can attack and the number of voter groups the defender can defend. A voter group gets removed from the election if it is attacked but not defended. In the optimal defense problem, we want to know if it is possible for the defender to commit to a strategy of defending at most $k_d$ voter groups such that, no matter which $k_a$ voter groups the attacker attacks, the outcome of the election does not change. In the optimal attack problem, we want to know if it is possible for the attacker to commit to a strategy of attacking $k_a$ voter groups such that, no matter which $k_d$ voter groups the defender defends, the outcome of the election is always different from the original (without any attack) one.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

Your Notes