HEalth: Privately Computing on Shared Healthcare Data

Leo de Castro (), Erin Hales () and Mimee Xu
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Leo de Castro: Massachusetts Institute of Technology, Computer Science and Artificial Intelligence Laboratory
Erin Hales: University of London, Information Security Group, Royal Holloway
Mimee Xu: New York University, Courant Institute of Mathematics

A chapter in Protecting Privacy through Homomorphic Encryption, 2021, pp 157-162 from Springer

Abstract: Abstract We give an overview of how to use threshold Fully Homomorphic Encryption (FHE) to enable data sharing in a medical context. Hospitals in the US are not currently equipped or motivated to share data privately. Threshold encryption would allow hospitals to share sensitive data securely. The combined encrypted data from all the hospitals can be used to compute statistics and even carry out machine learning at a large scale. We propose the use case of assessing ‘fairness’ in the context of hospital admissions. We analyse how fairness can be computed from the data, and describe how this could be beneficial to patients as well as regulators.

Date: 2021
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DOI: 10.1007/978-3-030-77287-1_12

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