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    Differentially private set intersection cardinalities in the multiparty setting

    Author
    Hadley, Connor
    View/Open
    179123_Hadley_rpi_0185N_11323.pdf (242.1Kb)
    Other Contributors
    Zikas, Vassilis; Magdon-Ismail, Malik; Milanova, Ana;
    Date Issued
    2018-05
    Subject
    Computer science
    Degree
    MS;
    Terms of Use
    This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.;
    Metadata
    Show full item record
    URI
    https://hdl.handle.net/20.500.13015/2239
    Abstract
    This paper examines the problem of learning information on multiple private databases in a quick and secure manner. The owners of such databases may either not desire to or are otherwise unable to leak information about their respective databases, despite the desired information possibly being entirely benign in nature. For example, to reference an earlier paper creating the DJoin system, one may wish to study the correlation between disease and travel to certain regions of the world, but not have access to the information required for this process. This project builds upon past attempts at providing a system to answer this problem, with a focus on improving the scalability as the number of datasets being examined increases.; As such, this paper introduces the Secure Multiparty Differentially Private Mechanism, or MDP, designed to improve upon past works in this field, and offer a universal solution to the problem that works regardless of the number of parties involved.;
    Description
    May 2018; School of Science
    Department
    Dept. of Computer Science;
    Publisher
    Rensselaer Polytechnic Institute, Troy, NY
    Relationships
    Rensselaer Theses and Dissertations Online Collection;
    Access
    Restricted to current Rensselaer faculty, staff and students. Access inquiries may be directed to the Rensselaer Libraries.;
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    • RPI Theses Online (Complete)

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