2021
Truly privacy-preserving federated analytics for precision medicine with multiparty homomorphic encryption
Froelicher D, Troncoso-Pastoriza J, Raisaro J, Cuendet M, Sousa J, Cho H, Berger B, Fellay J, Hubaux J. Truly privacy-preserving federated analytics for precision medicine with multiparty homomorphic encryption. Nature Communications 2021, 12: 5910. PMID: 34635645, PMCID: PMC8505638, DOI: 10.1038/s41467-021-25972-y.Peer-Reviewed Original ResearchConceptsMultiparty homomorphic encryptionHomomorphic encryptionPrivacy-preserving analysisNecessary key stepMultiple healthcare institutionsFederated analyticsFederated settingAnalysis tasksAnalytics systemIntermediate dataEncryptionCentralized studiesPatient dataBiomedical insightsScientific collaborationAccurate resultsIndispensable complementAnalyticsHealthcare institutionsDatasetTaskSystemBiomedical researchAccessCollaboration
2018
Realizing private and practical pharmacological collaboration
Hie B, Cho H, Berger B. Realizing private and practical pharmacological collaboration. Science 2018, 362: 347-350. PMID: 30337410, PMCID: PMC6519716, DOI: 10.1126/science.aat4807.Peer-Reviewed Original ResearchConceptsArt DTI prediction methodsDrug-target interactionsDTI prediction methodsIntellectual property concernsCryptographic toolsData privacyData sharingMultiple entitiesReal datasetsOpen sharingProperty concernsPrediction methodSharingDatasetPredictive modelPrivacyProtocolConfidentialityBiomedical researchCollaborationToolDataEntities