  We consider a game-theoretic setting to model the interplay between attacker
and defender in the context of information flow, and to reason about their
optimal strategies. In contrast with standard game theory, in our games the
utility of a mixed strategy is a convex function of the distribution on the
defender's pure actions, rather than the expected value of their utilities.
Nevertheless, the important properties of game theory, notably the existence of
a Nash equilibrium, still hold for our (zero-sum) leakage games, and we provide
algorithms to compute the corresponding optimal strategies. As typical in
(simultaneous) game theory, the optimal strategy is usually mixed, i.e.,
probabilistic, for both the attacker and the defender. From the point of view
of information flow, this was to be expected in the case of the defender, since
it is well known that randomization at the level of the system design may help
to reduce information leaks. Regarding the attacker, however, this seems the
first work (w.r.t. the literature in information flow) proving formally that in
certain cases the optimal attack strategy is necessarily probabilistic.
