Analysis of privacy loss in distributed constraint optimization

Rachel Greenstadt, Jonathan P. Pearce, Milind Tambe

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    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 decisions, providing an improved understanding of privacy-efficiency tradeoffs.

    Original languageEnglish (US)
    Title of host publicationProceedings of the 21st National Conference on Artificial Intelligence and the 18th Innovative Applications of Artificial Intelligence Conference, AAAI-06/IAAI-06
    Pages647-653
    Number of pages7
    StatePublished - 2006
    Event21st National Conference on Artificial Intelligence and the 18th Innovative Applications of Artificial Intelligence Conference, AAAI-06/IAAI-06 - Boston, MA, United States
    Duration: Jul 16 2006Jul 20 2006

    Publication series

    NameProceedings of the National Conference on Artificial Intelligence
    Volume1

    Conference

    Conference21st National Conference on Artificial Intelligence and the 18th Innovative Applications of Artificial Intelligence Conference, AAAI-06/IAAI-06
    CountryUnited States
    CityBoston, MA
    Period7/16/067/20/06

    ASJC Scopus subject areas

    • Software
    • Artificial Intelligence

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