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AAAI
2006

Analysis of Privacy Loss in Distributed Constraint Optimization

13 years 5 months ago
Analysis of Privacy Loss in Distributed Constraint Optimization
Distributed Constraint Optimization (DCOP) is rapidly emerging as a prominent technique for multiagent coordination. However, despite agent privacy being a key motivation for applying DCOPs in many applications, rigorous quantitative evaluations of privacy loss in DCOP algorithms have been lacking. Recently, [Maheswaran et al.2005] introduced a framework for quantitative evaluations of privacy in DCOP algorithms, showing that some DCOP algorithms lose more privacy than purely centralized approaches and questioning the motivation for applying DCOPs. This paper addresses the question of whether state-of-the art DCOP algorithms suffer from a similar shortcoming by investigating several of the most efficient DCOP algorithms, including both DPOP and ADOPT. Furthermore, while previous work investigated the impact on efficiency of distributed contraint reasoning design decisions (e.g. constraint-graph topology, asynchrony, message-contents), this paper examines the privacy aspect of such dec...
Rachel Greenstadt, Jonathan P. Pearce, Milind Tamb
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2006
Where AAAI
Authors Rachel Greenstadt, Jonathan P. Pearce, Milind Tambe
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