  In Stackelberg security games, a defender seeks to randomly allocate limited
security resources to protect critical targets from an attack. In this paper,
we study a fundamental, yet underexplored, phenomenon in security games, which
we term the \emph{Curse of Correlation} (CoC). Specifically, we observe that
there are inevitable correlations among the protection status of different
targets. Such correlation is a crucial concern, especially in
\emph{spatio-temporal} domains like conservation area patrolling, where
attackers can surveil patrollers at certain areas and then infer their
patrolling routes using such correlations. To mitigate this issue, we propose
to design entropy-maximizing defending strategies for spatio-temporal security
games, which frequently suffer from CoC. We prove that the problem is \#P-hard
in general. However, it admits efficient algorithms in well-motivated special
settings. Our experiments show significant advantages of max-entropy algorithms
over previous algorithms. A scalable implementation of our algorithm is
currently under pre-deployment testing for integration into FAMS software to
improve the scheduling of US federal air marshals.
