Audit Games

TL;DR

This paper introduces an audit game model for resource allocation and punishment in law enforcement, providing an efficient approximation algorithm for computing near-optimal strategies in complex non-convex settings.

Contribution

It develops a generalized audit game model with a novel punishment parameter and offers an additive FPTAS for solving the challenging equilibrium computation.

Findings

Efficient approximation algorithm for complex audit game equilibria.

Generalization of security game model with punishment parameter.

Near-optimal solutions achievable with polynomial-time algorithm.

Abstract

Effective enforcement of laws and policies requires expending resources to prevent and detect offenders, as well as appropriate punishment schemes to deter violators. In particular, enforcement of privacy laws and policies in modern organizations that hold large volumes of personal information (e.g., hospitals, banks, and Web services providers) relies heavily on internal audit mechanisms. We study economic considerations in the design of these mechanisms, focusing in particular on effective resource allocation and appropriate punishment schemes. We present an audit game model that is a natural generalization of a standard security game model for resource allocation with an additional punishment parameter. Computing the Stackelberg equilibrium for this game is challenging because it involves solving an optimization problem with non-convex quadratic constraints. We present an additive FPTAS that efficiently computes a solution that is arbitrarily close to the optimal solution.