Secure Distributed Constraint Satisfaction: Reaching Agreement without Revealing Private Information
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Taking DCOP to the Real World: Efficient Complete Solutions for Distributed Multi-Event Scheduling
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Adopt: asynchronous distributed constraint optimization with quality guarantees
Artificial Intelligence - Special issue: Distributed constraint satisfaction
Autonomous Agents and Multi-Agent Systems
A scalable method for multiagent constraint optimization
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
ADOPT-ing: unifying asynchronous distributed optimization with asynchronous backtracking
Autonomous Agents and Multi-Agent Systems
Privatizing constraint optimization
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Balancing local resources and global goals in multiply-constrained DCOP
Multiagent and Grid Systems
Open research questions of privacy-enhanced event scheduling
iNetSec'10 Proceedings of the 2010 IFIP WG 11.4 international conference on Open research problems in network security
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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.