Communications of the ACM
The vehicle routing problem
Distributed Constraint Satisfaction and Optimization with Privacy Enforcement
IAT '04 Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology
DCOPolis: a framework for simulating and deploying distributed constraint reasoning algorithms
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems: demo papers
Privacy Guarantees through Distributed Constraint Satisfaction
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
Fully homomorphic encryption using ideal lattices
Proceedings of the forty-first annual ACM symposium on Theory of computing
Privacy-Preserving Multi-agent Constraint Satisfaction
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 03
A scalable method for multiagent constraint optimization
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Distributed tree decomposition with privacy
CP'12 Proceedings of the 18th international conference on Principles and Practice of Constraint Programming
Distributed Gibbs: a memory-bounded sampling-based DCOP algorithm
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Protecting privacy through distributed computation in multi-agent decision making
Journal of Artificial Intelligence Research
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Several logistics service providers serve a certain number of customers, geographically spread over an area of operations. They would like to coordinate their operations so as to minimize overall cost. At the same time, they would like to keep information about their costs, constraints and preferences private, thus precluding conventional negotiation. We show how AI techniques, in particular Distributed Constraint Optimization (DCOP), can be integrated with cryptographic techniques to allow such coordination without revealing agents' private information. The problem of assigning customers to companies is formulated as a DCOP, for which we propose two novel, privacy-preserving algorithms. We compare their performances and privacy properties on a set of Vehicle Routing Problem benchmarks.