Security Games with Information Leakage: Modeling and Computation
This addresses a practical concern in security applications by modeling and computing strategies for defenders facing attackers with partial observation, representing an incremental advance over prior patrolling game research.
The paper tackles the problem of information leakage in Stackelberg security games, where attackers can partially observe the defender's deployed strategy, and shows that computing the defender's optimal strategy in this setting is NP-hard even for simple models. It proposes efficient algorithms for restricted cases, approximation algorithms, and heuristic sampling methods, with experiments confirming the necessity of handling leakage and the advantage of these algorithms.
Most models of Stackelberg security games assume that the attacker only knows the defender's mixed strategy, but is not able to observe (even partially) the instantiated pure strategy. Such partial observation of the deployed pure strategy -- an issue we refer to as information leakage -- is a significant concern in practical applications. While previous research on patrolling games has considered the attacker's real-time surveillance, our settings, therefore models and techniques, are fundamentally different. More specifically, after describing the information leakage model, we start with an LP formulation to compute the defender's optimal strategy in the presence of leakage. Perhaps surprisingly, we show that a key subproblem to solve this LP (more precisely, the defender oracle) is NP-hard even for the simplest of security game models. We then approach the problem from three possible directions: efficient algorithms for restricted cases, approximation algorithms, and heuristic algorithms for sampling that improves upon the status quo. Our experiments confirm the necessity of handling information leakage and the advantage of our algorithms.