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Creators/Authors contains: "Ayday, E"

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  1. ; ; ; ; (, Proceedings of 30th European Symposium on Research in Computer Security (ESORICS))
    Accepted Manuscript Full Text Available
  2. ; ; (, Proceedings of the American Medical Informatics Association (AMIA) Annual Summit)
    Accepted Manuscript Full Text Available
  3. ; ; ; ; ; ; (, Proceedings of AAAI Conference on Artificial Intelligence (AAAI-25))
    Accepted Manuscript Full Text Available
  4. ; ; (, 24th Privacy Enhancing Technologies Symposium)
    Accepted Manuscript Full Text Available
  5. ; (, ACM)
    Accepted Manuscript Full Text Available
  6. ; ; (, Proceedings on Privacy Enhancing Technologies (PoPETs))
    Accepted Manuscript Full Text Available
  7. ; ; ; (, Proceedings of The Annual Network & Distributed System Security Symposium)
    Accepted Manuscript Full Text Available
  8. ; ; ; ; ; (, AMIA)
    Accepted Manuscript Full Text Available
  9. ; ; ; ; ; (, AMIA Jt Summits Transl Sci Proc 2023)
    Kinship relationship estimation plays a significant role in today's genome studies. Since genetic data are mostly stored and protected in different silos, retrieving the desirable kinship relationships across federated data warehouses is a non-trivial problem. The ability to identify and connect related individuals is important for both research and clinical applications. In this work, we propose a new privacy-preserving kinship relationship estimation framework: Incremental Update Kinship Identification (INK). The proposed framework includes three key components that allow us to control the balance between privacy and accuracy (of kinship estimation): an incremental process coupled with the use of auxiliary information and informative scores. Our empirical evaluation shows that INK can achieve higher kinship identification correctness while exposing fewer genetic markers. 
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    Accepted Manuscript Full Text Available
  10. ; ; ; ; ; (, American Medical Informatics Association (AMIA) Annual Symposium)
    Accepted Manuscript Full Text Available