IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-030-77287-1_7.html
   My bibliography  Save this book chapter

Private Outsourced Translation for Medical Data

In: Protecting Privacy through Homomorphic Encryption

Author

Listed:
  • Travis Morrison

    (Virginia Tech University, Mathematics)

  • Sarah Scheffler

    (Boston University, Department of Computer Science)

  • Bijeeta Pal

    (Cornell University, Department of Computer Science)

  • Alexander Viand

    (ETH Zurich, Department of Computer Science)

Abstract

Overcoming language barriers is a key challenge for international organizations providing medical aid in a variety of places. While bilingual doctors or human translators can achieve this effectively, many volunteer clinics are under-staffed and under-funded and must do without. Machine translations have become increasingly accurate and accessible, e.g. via Google Translate. However, uploading medical data to the cloud can violate patient privacy, while offline translation systems are often less accurate. Using homomorphic encryption, clients could submit encrypted queries to a server hosting a translation model without revealing the underlying private information. However, modern translation systems are based on Recurrent Neural Networks (RNNs) which are challenging to evaluate homomorphically due to their inherent high depth and their heavy reliance on non-linearity. We design, implement, and evaluate a proof-of-concept solution and explore a variety of solutions to these challenges.

Suggested Citation

  • Travis Morrison & Sarah Scheffler & Bijeeta Pal & Alexander Viand, 2021. "Private Outsourced Translation for Medical Data," Springer Books, in: Kristin Lauter & Wei Dai & Kim Laine (ed.), Protecting Privacy through Homomorphic Encryption, pages 107-116, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-77287-1_7
    DOI: 10.1007/978-3-030-77287-1_7
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:sprchp:978-3-030-77287-1_7. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.