Privacy preserving auctions and mechanism design
Proceedings of the 1st ACM conference on Electronic commerce
The Distributed Constraint Satisfaction Problem: Formalization and Algorithms
IEEE Transactions on Knowledge and Data Engineering
Developing an Automated Distributed Meeting Scheduler
IEEE Expert: Intelligent Systems and Their Applications
Electric Elves: Applying Agent Technology to Support Human Organizations
Proceedings of the Thirteenth Conference on Innovative Applications of Artificial Intelligence Conference
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
Agent-Based Approach to Dynamic Meeting Scheduling Problems
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
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
Bumping strategies for the multiagent agreement problem
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
The value of privacy: optimal strategies for privacy minded agents
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Security in multiagent systems by policy randomization
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Towards adjustable autonomy for the real world
Journal of Artificial Intelligence Research
Experimental analysis of privacy loss in DCOP algorithms
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
SSDPOP: improving the privacy of DCOP with secret sharing
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Distributed Private Constraint Optimization
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
An overview of privacy improvements to k-optimal DCOP algorithms
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Analysis of privacy loss in distributed constraint optimization
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
PC-DPOP: a new partial centralization algorithm for distributed optimization
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Quantifying privacy in multiagent planning
Multiagent and Grid Systems - Planning in multiagent systems
Eliciting User Preferences in Multi-Agent Meeting Scheduling Problem
International Journal of Intelligent Information Technologies
Improving Asynchronous Partial Overlay
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
Protecting privacy through distributed computation in multi-agent decision making
Journal of Artificial Intelligence Research
Hi-index | 0.00 |
It is critical that agents deployed in real-world settings, such as businesses, offices, universities and research laboratories, protect their individual users' privacy when interacting with other entities. Indeed, privacy is recognized as a key motivating factor in the design of several multiagent algorithms, such as in distributed constraint reasoning (including both algorithms for distributed constraint optimization (DCOP) and distributed constraint satisfaction (DisCSPs)), and researchers have begun to propose metrics for analysis of privacy loss in such multiagent algorithms. Unfortunately, a general quantitative framework to compare these existing metrics for privacy loss or to identify dimensions along which to construct new metrics is currently lacking. This paper presents three key contributions to address this shortcoming. First, the paper presents VPS (Valuations of Possible States), a general quantitative framework to express, analyze and compare existing metrics of privacy loss. Based on a state-space model, VPS is shown to capture various existing measures of privacy created for specific domains of DisCSPs. The utility of VPS is further illustrated through analysis of privacy loss in DCOP algorithms, when such algorithms are used by personal assistant agents to schedule meetings among users. In addition, VPS helps identify dimensions along which to classify and construct new privacy metrics and it also supports their quantitative comparison. Second, the article presents key inference rules that may be used in analysis of privacy loss in DCOP algorithms under different assumptions. Third, detailed experiments based on the VPS-driven analysis lead to the following key results: (i) decentralization by itself does not provide superior protection of privacy in DisCSP/DCOP algorithms when compared with centralization; instead, privacy protection also requires the presence of uncertainty about agents' knowledge of the constraint graph. (ii) one needs to carefully examine the metrics chosen to measure privacy loss; the qualitative properties of privacy loss and hence the conclusions that can be drawn about an algorithm can vary widely based on the metric chosen. This paper should thus serve as a call to arms for further privacy research, particularly within the DisCSP/DCOP arena.