Audit Games with Multiple Defender Resources
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 construction of this FPTAS. In addition, we experimentally demonstrate that this transformation significantly speeds up computation of solutions for a class of audit games and security games.
Cite
Text
Blocki et al. "Audit Games with Multiple Defender Resources." AAAI Conference on Artificial Intelligence, 2015. doi:10.1609/AAAI.V29I1.9317Markdown
[Blocki et al. "Audit Games with Multiple Defender Resources." AAAI Conference on Artificial Intelligence, 2015.](https://mlanthology.org/aaai/2015/blocki2015aaai-audit/) doi:10.1609/AAAI.V29I1.9317BibTeX
@inproceedings{blocki2015aaai-audit,
title = {{Audit Games with Multiple Defender Resources}},
author = {Blocki, Jeremiah and Christin, Nicolas and Datta, Anupam and Procaccia, Ariel D. and Sinha, Arunesh},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2015},
pages = {791-797},
doi = {10.1609/AAAI.V29I1.9317},
url = {https://mlanthology.org/aaai/2015/blocki2015aaai-audit/}
}