An overview of privacy improvements to k-optimal DCOP algorithms

Authors:
Rachel Greenstadt
Affiliations:
Drexel University, Philadelphia, PA
Venue:
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Year:
2009

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Abstract

For agents to be trusted with sensitive data, they must have mechanisms to protect their users' privacy. This paper explores the privacy properties of k-optimal algorithms: those algorithms that produce locally optimal solutions that cannot be improved by changing the assignments of k or fewer agents. While these algorithms are subject to large amounts of privacy loss, they can be modified to reduce this privacy loss by an order of magnitude. The greatest improvements are achieved by replacing the centralized local search with a distributed algorithm, such as DPOP.