Private Outsourced Translation for Medical Data

Travis Morrison (), Sarah Scheffler (), Bijeeta Pal () and Alexander Viand ()
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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

A chapter in Protecting Privacy through Homomorphic Encryption, 2021, pp 107-116 from Springer

Abstract: 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.

Date: 2021
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DOI: 10.1007/978-3-030-77287-1_7

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