Audit Games with Multiple Defender Resources
Jeremiah Blocki, Nicolas Christin, Anupam Datta, Ariel Procaccia and, Arunesh Sinha

TL;DR
This paper extends audit game models to include multiple audit resources with subset restrictions, providing an efficient approximation algorithm and demonstrating practical computational improvements.
Contribution
It introduces a generalized audit game model with multiple resources and develops an FPTAS using a novel optimization transformation.
Findings
The FPTAS efficiently computes approximate solutions for complex audit games.
The optimization transformation significantly accelerates solution computation.
Experimental results confirm the method's practical effectiveness.
Abstract
Modern organizations (e.g., hospitals, social networks, government agencies) rely heavily on audit to detect and punish insiders who inappropriately access and disclose confidential information. Recent work on audit games models the strategic interaction between an auditor with a single audit resource and auditees as a Stackelberg game, augmenting associated well-studied security games with a configurable punishment parameter. We significantly generalize this audit game model to account for multiple audit resources where each resource is restricted to audit a subset of all potential violations, thus enabling application to practical auditing scenarios. We provide an FPTAS that computes an approximately optimal solution to the resulting non-convex optimization problem. The main technical novelty is in the design and correctness proof of an optimization transformation that enables the…
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