Distributed asynchronous search with private constraints (extended abstract)
AGENTS '00 Proceedings of the fourth international conference on Autonomous agents
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
An asynchronous complete method for distributed constraint optimization
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
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
Solving Distributed Constraint Optimization Problems Using Cooperative Mediation
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
Distributed Constraint Satisfaction and Optimization with Privacy Enforcement
IAT '04 Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology
Towards adjustable autonomy for the real world
Journal of Artificial Intelligence Research
Autonomous Agents and Multi-Agent Systems
Experimental analysis of privacy loss in DCOP algorithms
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Privatizing constraint optimization
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Analysis of privacy loss in distributed constraint optimization
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Local search for distributed asymmetric optimization
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Solving distributed CSPs using dynamic, partial centralization without explicit constraint passing
PRIMA'10 Proceedings of the 13th international conference on Principles and Practice of Multi-Agent Systems
Asymmetric distributed constraint optimization problems
Journal of Artificial Intelligence Research
Improving the privacy of the asynchronous partial overlay protocol
Multiagent and Grid Systems - Principles and Practice of Multi-Agent Systems
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For agents deployed in real-world settings, such as businesses, universities and research laboratories, it is critical that agents protect their individual users' privacy when interacting with others entities. Indeed, privacy is recognized as a key motivating factor in design of several multiagent algorithms, such as distributed constraint optimization (DCOP) algorithms. Unfortunately, rigorous and general quantitative metrics for analysis and comparison of such multiagent algorithms with respect to privacy loss are lacking. This paper takes a key step towards developing a general quantitative model from which one can analyze and generate metrics of privacy loss by introducing the VPS (Valuations of Possible States) framework. VPS is shown to capture various existing measures of privacy created for specific domains of distributed constraint satisfactions problems (DCSPs). The utility of VPS is further illustrated via analysis of DCOP algorithms, when such algorithms are used by personal assistant agents to schedule meetings among users. In addition, VPS allows us to quantitatively evaluate the properties of several privacy metrics generated through qualitative notions. We obtain the unexpected result that decentralization does not automatically guarantee superior protection of privacy.