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

Mitigating the Curse of Correlation in Security Games by Entropy Maximization