An overview of privacy improvements to k-optimal DCOP algorithms

Rachel Greenstadt

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

    Abstract

    For agents to be trusted with sensitive data, they must have mechanisms to protect their users' privacy. This paper explores the privacy properties of k-optimal algorithms: Those algorithms that pro-duce locally optimal solutions that cannot be improved by changing the assignments of k or fewer agents. While these algorithms are subject to large amounts of privacy loss, they can be modified to reduce this privacy loss by an order of magnitude. The greatest improvements are achieved by replacing the centralized local search with a distributed algorithm, such as DPOP.

    Original languageEnglish (US)
    Title of host publication8th International Joint Conference on Autonomous Agents and Multiagent Systems 2009, AAMAS 2009
    PublisherInternational Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
    Pages1282-1283
    Number of pages2
    ISBN (Print)9781615673346
    StatePublished - 2009
    Event8th International Joint Conference on Autonomous Agents and Multiagent Systems 2009, AAMAS 2009 - Budapest, Hungary
    Duration: May 10 2009May 15 2009

    Publication series

    NameProceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
    Volume2
    ISSN (Print)1548-8403
    ISSN (Electronic)1558-2914

    Conference

    Conference8th International Joint Conference on Autonomous Agents and Multiagent Systems 2009, AAMAS 2009
    CountryHungary
    CityBudapest
    Period5/10/095/15/09

    Keywords

    • Distributed constraint optimization
    • Security and privacy

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Software
    • Control and Systems Engineering

    Fingerprint Dive into the research topics of 'An overview of privacy improvements to k-optimal DCOP algorithms'. Together they form a unique fingerprint.

    Cite this