@conference {1509424,
	title = {Analysis of Privacy Loss in Distributed Constraint Optimization },
	booktitle = {The Twenty-First National Conference on Artificial Intelligence (AAAI-06)},
	year = {2006},
	abstract = {Distributed Constraint Optimization (DCOP) is rapidly
emerging as a prominent technique for multiagent coordination. However, despite agent privacy being a key motivation
for applying DCOPs in many applications, rigorous quantitative evaluations of privacy loss in DCOP algorithms have
been lacking. Recently, [Maheswaran et al.2005] introduced
a framework for quantitative evaluations of privacy in DCOP
algorithms, showing that some DCOP algorithms lose more
privacy than purely centralized approaches and questioning
the motivation for applying DCOPs. This paper addresses
the question of whether state-of-the art DCOP algorithms suffer from a similar shortcoming by investigating several of the
most efficient DCOP algorithms, including both DPOP and
ADOPT. Furthermore, while previous work investigated the
impact on efficiency of distributed contraint reasoning design decisions (e.g. constraint-graph topology, asynchrony,
message-contents), this paper examines the privacy aspect
of such decisions, providing an improved understanding of
privacy-efficiency tradeoffs.},
	author = {Rachel Greenstadt and Jonathan P. Pearce and Tambe, Milind}
}
