Keeping patient phenotypes and genotypes private while seeking disease diagnoses

Abstract

In an age where commercial entities are allowed to collect and directly profit from large amounts of private information, an age where large data breaches of such organizations are discovered every month, science must strive to offer society viable ways to preserve privacy while benefitting from the power of data sharing. Patient phenotypes and genotypes are critical for building groups of phenotypically-similar patients, identify the gene that best explains their common phenotypes, and ultimately, diagnose a patient with a Mendelian disease. Direct computation over these quantities requires highly-sensitive patient data to be shared openly, compromising patient privacy and opening patients up for discrimination. Existing protocols focus on secure computation over genotype data and only address the final steps of the disease-diagnosis pipeline where phenotypically-similar patients have been identified. However, identifying such patients in a secure and private manner remains open. In this work, we develop secure protocols to maintain patient privacy while computing meaningful operations over both genotypic and phenotypic data for two real scenarios: COHORT DISCOVERY and GENE PRIORITIZATION. Our protocols newly enable a complete and secure end-to-end disease diagnosis pipeline that protects sensitive patient phenotypic and genotypic data.