@conference {1509419,
	title = {Experimental analysis of privacy loss in DCOP algorithms (short paper)},
	booktitle = {Fifth International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS)},
	year = {2006},
	abstract = {Distributed Constraint Optimization (DCOP) is rapidly emerging
as a prominent technique for multiagent coordination. Unfortunately, rigorous quantitative evaluations of privacy loss in DCOP
algorithms have been lacking despite the fact that agent privacy is
a key motivation for applying DCOPs in many applications. Recently, Maheswaran et al. [3, 4] introduced a framework for quantitative evaluations of privacy in DCOP algorithms, showing that
early DCOP algorithms lose more privacy than purely centralized
approaches and questioning the motivation for applying DCOPs.
Do state-of-the art DCOP algorithms suffer from a similar shortcoming? This paper answers that question by investigating the most
efficient DCOP algorithms, including both DPOP and ADOPT.},
	author = {Rachel Greenstadt and Jonathan P. Pearce and Bowring, Emma and Tambe, Milind}
}
