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Achieving anonymity and correcting bias with synthetic data through generative AI

  • Creator
  • #134148

    Collaborative meeting notes:

    Brief introduction to the HDIG
    Presentations on synthetic data critical issues and perspectives
    Q&A and discussion on topics presented
    Next steps

    Additional links to informative material
    • Group page:
    • Case statement:  
    • HDIG Sessions Presentations @RDA Plenaries (HDIG File Repository):

    Are you willing to hold your session at multiple times to accommodate various time zones?

    Avoid conflict with the following group (1)
    Blockchain Applications in Health WG

    Avoid conflict with the following group (3)
    Life Science Data Infrastructures IG

    Contact for group (email)

    Group chair serving as contact person
    Ludovica Durst

    Meeting objectives
    Former HDIG discussions have highlighted the hurdles, such as silo fragmentation and privacy concerns, which are usually encountered when striving to access significant amounts of health data, and how this comparative shortage of Big Data in health is hindering artificial intelligence and knowledge discovery in medicine.
    The forthcoming HDIG session in Edinburgh aims at addressing the issue of effective anonymisation through synthetic data generation in combination with advanced privacy-enhancing technologies. Federated approaches based on Secure Multiparty Computation and Differential Privacy can enable the creation of artificial data through Generative Adversarial Networks and allow scaling up experimentation with non-re-identifiable health data and effective training of clinical decision-support tools. At the same time, large-scale generation and use of synthetic data sets is raising renewed interest in their legal and ethical applications, as well as in their validation.
    This session proposes to highlight several critical issues related to synthetic data: 1) their still insufficiently acknowledged legal status as anonymous data; 2) the deep learning methodologies currently used to generate synthetic data and synthetic imaging; 3) their capacity to correct biased databases; 4) their potential for augmenting specific patient cohorts and for creating virtual cohorts; 5) the trust synthetic data inspire and their validation tests; 6) their combined usage with other privacy enhancing technologies.

    Please indicate the breakout slot (s) that would suit your meeting
    Breakout 2, Breakout 4, Breakout 5, Breakout 8

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