  Cyberattacks on both databases and critical infrastructure have threatened
public and private sectors. Ubiquitous tracking and wearable computing have
infringed upon privacy. Advocates and engineers have recently proposed using
defensive deception as a means to leverage the information asymmetry typically
enjoyed by attackers as a tool for defenders. The term deception, however, has
been employed broadly and with a variety of meanings. In this paper, we survey
24 articles from 2008-2018 that use game theory to model defensive deception
for cybersecurity and privacy. Then we propose a taxonomy that defines six
types of deception: perturbation, moving target defense, obfuscation, mixing,
honey-x, and attacker engagement. These types are delineated by their
information structures, agents, actions, and duration: precisely concepts
captured by game theory. Our aims are to rigorously define types of defensive
deception, to capture a snapshot of the state of the literature, to provide a
menu of models which can be used for applied research, and to identify
promising areas for future work. Our taxonomy provides a systematic foundation
for understanding different types of defensive deception commonly encountered
in cybersecurity and privacy.
