Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
8-2013
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) 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 the solution.
Discipline
Artificial Intelligence and Robotics
Research Areas
Data Science and Engineering
Publication
Proceedings of the 23rd international joint conference on Artificial Intelligence, Beijing, China, 2013 August 3-9
First Page
41
Last Page
47
ISBN
9781577356332
Publisher
ACM
City or Country
Beijing China
Citation
BLOCKI, Jeremiah; CHRISTIN, Nicolas; DATTA, Anupam; PROCACCIA, Ariel D.; and SINHA, Arunesh.
Audit games. (2013). Proceedings of the 23rd international joint conference on Artificial Intelligence, Beijing, China, 2013 August 3-9. 41-47.
Available at: https://ink.library.smu.edu.sg/sis_research/4491
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.