Communications of the ACM
The Distributed Constraint Satisfaction Problem: Formalization and Algorithms
IEEE Transactions on Knowledge and Data Engineering
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
Distributed Constraint Satisfaction and Optimization with Privacy Enforcement
IAT '04 Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology
WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
Adopt: asynchronous distributed constraint optimization with quality guarantees
Artificial Intelligence - Special issue: Distributed constraint satisfaction
Constraint-based reasoning and privacy/efficiency tradeoffs in multi-agent problem solving
Artificial Intelligence - Special issue: Distributed constraint satisfaction
Autonomous Agents and Multi-Agent Systems
Analysis of privacy loss in distributed constraint optimization
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
A scalable method for multiagent constraint optimization
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Anytime local search for distributed constraint optimization
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3
Asynchronous forward bounding for distributed COPs
Journal of Artificial Intelligence Research
BnB-ADOPT: an asynchronous branch-and-bound DCOP algorithm
Journal of Artificial Intelligence Research
Boosting distributed constraint satisfaction
Journal of Heuristics
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multiagent systems designed to work collaboratively with groups of people typically require private information that people will entrust to them only if they have assurance that this information will be protected. Although Distributed Constraint Optimization (DCOP) has emerged as a prominent technique for multiagent coordination, existing algorithms for solving DCOP problems do not adeqately protect agents' privacy. This paper analyzes privacy protection and loss in existing DCOP algorithms. It presents a new algorithm, SSDPOP, which augments a prominent DCOP algorithm (DPOP) with secret sharing techniques. This approach significantly reduces privacy loss, while preserving the structure of the DPOP algorithm and introducing only minimal computational overhead. Results show that SSDPOP reduces privacy loss by 29--88% on average over DPOP.