• About
    • Login
    View Item 
    •   Institutional Repository Home
    • Electronic Theses and Dissertations
    • Electronic Theses and Dissertations
    • View Item
    •   Institutional Repository Home
    • Electronic Theses and Dissertations
    • Electronic Theses and Dissertations
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of Institutional RepositoryCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsDepartmentThis CollectionBy Issue DateAuthorsTitlesSubjectsDepartment

    My Account

    LoginRegister

    Privacy-Preserving Sharing of High-Dimensional Data based on Computational Game Theory

    Wan, Zhiyu
    0000-0003-3752-5778
    : http://hdl.handle.net/1803/16396
    : 2020-11-18

    Abstract

    In the big data era, person-specific data are being collected in an unprecedented manner. Given the potential wealth of insights in personal data, many organizations aim to share data while protecting privacy by sharing de-identified data, but are concerned because various demonstrations show such data can be re-identified. A wide array of deterrents have been designed to mitigate concerns, some of which are technical (e.g., obfuscating data), while others are more social (e.g., legal contracts). However, these investigations have focused on worst-case scenarios and spurred the adoption of data sharing practices that unnecessarily impede research. A formal re-identification risk assessment is required to help data sharers make better decisions about how to share data. Game-theoretic approaches, which model rational interactions among the parties involved, can optimally balance utility and risks in data sharing scenarios. I utilize a game-theoretic lens to develop more effective, quantifiable protections for data sharing. This is a fundamentally different approach because it accounts for adversarial behavior and capabilities and tailors protections to anticipated recipients with reasonable resources. I demonstrate this approach with large-scale real-world genomic datasets and show risks can be balanced against utility more effectively than traditional approaches. Confronting high dimensionality in practical scenarios, I develop AI algorithms to accelerate the solution search. I find it is possible to achieve zero risk, in that the recipient never gains from re-identification, while sharing almost as much data as the optimal solution that allows for a small amount of risk. Recognizing that such models are dependent on a variety of parameters, I perform extensive sensitivity analyses to show that my findings are robust to their fluctuations. My dissertation focuses on answering theoretical questions about the privacy-preserving data sharing problems in multi-stage adversarial scenarios and designing practical algorithms for game-solving in high-dimensional environments. I tailor my approaches for building scalable systems demanded by modern big data applications. The game-theoretic methodology that I examine using demographic, genomic, and phenotypic data has the potential to be applied to other data types and be regarded as a general data protection methodology.
    Show full item record

    Files in this item

    Icon
    Name:
    WAN-DISSERTATION-2020.pdf
    Size:
    10.61Mb
    Format:
    PDF
    View/Open
    Name:
    WAN-DISSERTATION_FINAL.docx
    Size:
    12.36Mb
    Format:
    Microsoft Word 2007
    View/Open

    This item appears in the following collection(s):

    • Electronic Theses and Dissertations

    Connect with Vanderbilt Libraries

    Your Vanderbilt

    • Alumni
    • Current Students
    • Faculty & Staff
    • International Students
    • Media
    • Parents & Family
    • Prospective Students
    • Researchers
    • Sports Fans
    • Visitors & Neighbors

    Support the Jean and Alexander Heard Libraries

    Support the Library...Give Now

    Gifts to the Libraries support the learning and research needs of the entire Vanderbilt community. Learn more about giving to the Libraries.

    Become a Friend of the Libraries

    Quick Links

    • Hours
    • About
    • Employment
    • Staff Directory
    • Accessibility Services
    • Contact
    • Vanderbilt Home
    • Privacy Policy