A plug-n-play game theoretic framework for defending against radio window attacks

Abstract

The large scale deployment of multi-antenna wireless networks in homes and office buildings introduces new privacy concerns for people residing in these spaces. By measuring the signal strength using receivers placed outside the premises, an attacker can track the movement of people inside. One way to defend against such an attack is to have the signal strengths of the transmitters vary (sometimes reducing to zero) according to some randomized schedule. We show that the question of finding the schedule that minimizes the worst-case "privacy loss" can be formulated as a constant-sum Stackelberg game between an attacker, whose goal is to place receivers in order to learn the movement of users, and a defender who tries to prevent the attacker while maintaining the connectivity and QoS requirements of the network. We introduce a flexible framework that enables us to capture the constraints of the attacker and the defender. The framework allows us to capture features of modern wireless systems such as directional antennas and also allows us to plug in different path-loss models with minimal changes to the setup. We then formulate the problem of finding the optimal defender strategy as a linear program and show that it can be solved efficiently. We also perform numerical evaluations on how the payoffs are affected as the requirements of the defender and the resources the attacker can afford to exhaust change.