Developing High Performance Secure Multi-Party Computation Protocols in Healthcare: A Case Study of Patient Risk Stratification

Xiao Dong, David A. Randolph, Chenkai Weng, Abel N. Kho, Jennie M. Rogers, Xiao Wang

Research output: Contribution to journalArticlepeer-review

Abstract

We demonstrate that secure multi-party computation (MPC) using garbled circuits is viable technology for solving clinical use cases that require cross-institution data exchange and collaboration. We describe two MPC protocols, based on Yao's garbled circuits and tested using large and realistically synthesized datasets. Linking records using private set intersection (PSI), we compute two metrics often used in patient risk stratification: high utilizer identification (PSI-HU) and comorbidity index calculation (PSI-CI). Cuckoo hashing enables our protocols to achieve extremely fast run times, with answers to clinically meaningful questions produced in minutes instead of hours. Also, our protocols are provably secure against any computationally bounded adversary in a semi-honest setting, the de-facto mode for cross-institution data analytics. Finally, these protocols eliminate the need for an implicitly trusted third-party "honest broker" to mediate the information linkage and exchange.

Original languageEnglish (US)
Pages (from-to)200-209
Number of pages10
JournalAMIA ... Annual Symposium proceedings. AMIA Symposium
Volume2021
StatePublished - 2021

ASJC Scopus subject areas

  • Medicine(all)

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