Summary: Adversarial Leakage in Games
While the maximin strategy has become the standard, and most agreed-upon solution for
decision-making in adversarial settings, as discussed in game theory, computer science and other
disciplines, its power arises from the use of mixed strategies, a.k.a. probabilistic algorithms.
Nevertheless, in adversarial settings we face the risk of information leakage about the actual
strategy instantiation. Hence, real robust algorithms should take information leakage into account.
To address this fundamental issue, we introduce the study of adversarial leakage in games. We
consider two models of leakage. In both of them the adversary is able to learn the value of b binary
predicates about the strategy instantiation. In one of the models these predicates are selected after
the decision-maker announces its probabilistic algorithm and in the other one they are decided in
advance. We give tight results about the effects of adversarial leakage in general zero-sum games
with binary payoffs as a function of the level of leakage captured by b in both models. We also
compare the power of adversarial leakage in the two models and the robustness of the original
maximin strategies of games to adversarial leakage. Finally, we study the computation of optimal
strategies for adversarial leakage models. Together, our study introduces a new framework for