2024
Secure discovery of genetic relatives across large-scale and distributed genomic datasets
Hong M, Froelicher D, Magner R, Popic V, Berger B, Cho H. Secure discovery of genetic relatives across large-scale and distributed genomic datasets. Genome Research 2024, 34: gr.279057.124. PMID: 39111815, PMCID: PMC11529841, DOI: 10.1101/gr.279057.124.Peer-Reviewed Original ResearchMultiparty homomorphic encryptionIdentity-by-descentEffective hash functionsGenomic datasetsHomomorphic encryptionHash functionPrivate dataFederated algorithmBucketing strategyData holdersData silosDegree of relatednessRelation detectionGenetic relationEfficient algorithmMultiple entitiesRelatedness coefficientsPairs of individualsGenomic studiesDatasetIdentification of relationsRuntimeGenetic sequencesAccurate detectionAlgorithmSecure Discovery of Genetic Relatives Across Large-Scale and Distributed Genomic Datasets
Hong M, Froelicher D, Magner R, Popic V, Berger B, Cho H. Secure Discovery of Genetic Relatives Across Large-Scale and Distributed Genomic Datasets. Lecture Notes In Computer Science 2024, 14758: 308-313. PMID: 39027313, PMCID: PMC11257153, DOI: 10.1007/978-1-0716-3989-4_19.Peer-Reviewed Original ResearchIdentity-by-descentMultiparty homomorphic encryptionGenomic datasetsPairwise sequence comparisonsPrivacy-preserving solutionsDegree of relatednessEffective hash functionsGenetic relationPairs of individualsRelatedness coefficientsSequence comparisonCryptographic techniquesHomomorphic encryptionPrivacy guaranteesHash functionPrivate dataFederated algorithmPrivacy concernsGenetic sequencesData silosRelation detectionEfficient algorithmMultiple entitiesBurden of operatorsPrivacy
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