• myGriffith
    • Staff portal
    • Contact Us⌄
      • Future student enquiries 1800 677 728
      • Current student enquiries 1800 154 055
      • International enquiries +61 7 3735 6425
      • General enquiries 07 3735 7111
      • Online enquiries
      • Staff phonebook
    View Item 
    •   Home
    • Griffith Research Online
    • Conference outputs
    • View Item
    • Home
    • Griffith Research Online
    • Conference outputs
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

  • All of Griffith Research Online
    • Communities & Collections
    • Authors
    • By Issue Date
    • Titles
  • This Collection
    • Authors
    • By Issue Date
    • Titles
  • Statistics

  • Most Popular Items
  • Statistics by Country
  • Most Popular Authors
  • Support

  • Contact us
  • FAQs
  • Admin login

  • Login
  • Privacy-Preserving Gradient Descent for Distributed Genome-Wide Analysis

    Thumbnail
    View/Open
    Bai510235-Accepted.pdf (16.14Mb)
    File version
    Accepted Manuscript (AM)
    Author(s)
    Zhang, Y
    Bai, G
    Li, X
    Curtis, C
    Chen, C
    Ko, RKL
    Griffith University Author(s)
    Bai, Guangdong
    Chen, Chen
    Year published
    2021
    Metadata
    Show full item record
    Abstract
    Genome-wide analysis, which provides perceptive insights into complex diseases, plays an important role in biomedical data analytics. It usually involves large-scale human genomic data, and thus may disclose sensitive information about individuals. While existing studies have been conducted against data exfiltration by external malicious actors, this work focuses on the emerging identity tracing attack that occurs when a dishonest insider attempts to re-identify obtained DNA samples. We propose a framework named υFRAG to facilitate privacy-preserving data sharing and computation in genome-wide analysis. υFRAG mitigates privacy ...
    View more >
    Genome-wide analysis, which provides perceptive insights into complex diseases, plays an important role in biomedical data analytics. It usually involves large-scale human genomic data, and thus may disclose sensitive information about individuals. While existing studies have been conducted against data exfiltration by external malicious actors, this work focuses on the emerging identity tracing attack that occurs when a dishonest insider attempts to re-identify obtained DNA samples. We propose a framework named υFRAG to facilitate privacy-preserving data sharing and computation in genome-wide analysis. υFRAG mitigates privacy risks by using vertical fragmentations to disrupt the genetic architecture on which the adversary relies for re-identification. The fragmentation significantly reduces the overall amount of information the adversary can obtain. Notably, it introduces no sacrifice to the capability of genome-wide analysis—we prove that it preserves the correctness of gradient descent, the most popular optimization approach for training machine learning models. We also explore the efficiency performance of υFRAG through experiments on a large-scale, real-world dataset. Our experiments demonstrate that υFRAG outperforms not only secure multiparty computation (MPC) and homomorphic encryption (HE) protocols with a speedup of more than 221x for training neural networks, but also noise-based differential privacy (DP) solutions and traditional non-private algorithms in most settings.
    View less >
    Conference Title
    Lecture Notes in Computer Science
    Volume
    12973
    DOI
    https://doi.org/10.1007/978-3-030-88428-4_20
    Copyright Statement
    © Springer Nature Switzerland AG 2021. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher.The original publication is available at www.springerlink.com
    Subject
    Genomics
    Clinical sciences
    Publication URI
    http://hdl.handle.net/10072/411915
    Collection
    • Conference outputs

    Footer

    Disclaimer

    • Privacy policy
    • Copyright matters
    • CRICOS Provider - 00233E
    • TEQSA: PRV12076

    Tagline

    • Gold Coast
    • Logan
    • Brisbane - Queensland, Australia
    First Peoples of Australia
    • Aboriginal
    • Torres Strait Islander