2023
Scalable and Privacy-Preserving Federated Principal Component Analysis
Froelicher D, Cho H, Edupalli M, Sousa J, Bossuat J, Pyrgelis A, Troncoso-Pastoriza J, Berger B, Hubaux J. Scalable and Privacy-Preserving Federated Principal Component Analysis. 2016 IEEE Symposium On Security And Privacy (SP) 2023, 00: 1908-1925. PMID: 38665901, PMCID: PMC11044025, DOI: 10.1109/sp46215.2023.10179350.Peer-Reviewed Original ResearchHomomorphic encryptionData providersMultiparty homomorphic encryptionPrivacy-preserving alternativeMultiple data providersSecure multiparty computationPassive adversary modelData science domainCleartext dataData confidentialityPrivate dataMultiparty computationSecure systemsInteractive protocolDataset dimensionsEssential algorithmsCentralized solutionData distributionScience domainLocal analysis resultsDimensionality reductionIntermediate resultsEncryptionPrincipal component analysisOriginal data
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