2021
Ultrafast homomorphic encryption models enable secure outsourcing of genotype imputation
Kim M, Harmanci A, Bossuat J, Carpov S, Cheon J, Chillotti I, Cho W, Froelicher D, Gama N, Georgieva M, Hong S, Hubaux J, Kim D, Lauter K, Ma Y, Ohno-Machado L, Sofia H, Son Y, Song Y, Troncoso-Pastoriza J, Jiang X. Ultrafast homomorphic encryption models enable secure outsourcing of genotype imputation. Cell Systems 2021, 12: 1108-1120.e4. PMID: 34464590, PMCID: PMC9898842, DOI: 10.1016/j.cels.2021.07.010.Peer-Reviewed Original ResearchConceptsHomomorphic encryption techniqueResource-intensive computationsSecure outsourcingGenomic data analysisData securityEncryption modelEncryption techniquePrivacy concernsSource codeMemory requirementsGenetic data analysisData analysisComparable accuracyFundamental stepGenotype imputationImputationDownloadSecurityOutsourcingComputationCodeServicesRequirementsAccuracyMethod
2020
Efficient determination of equivalence for encrypted data
Doctor J, Vaidya J, Jiang X, Wang S, Schilling L, Ong T, Matheny M, Ohno-Machado L, Meeker D. Efficient determination of equivalence for encrypted data. Computers & Security 2020, 97: 101939. PMID: 33223585, PMCID: PMC7676425, DOI: 10.1016/j.cose.2020.101939.Peer-Reviewed Original Research
2019
Secure and Differentially Private Logistic Regression for Horizontally Distributed Data
Kim M, Lee J, Ohno-Machado L, Jiang X. Secure and Differentially Private Logistic Regression for Horizontally Distributed Data. IEEE Transactions On Information Forensics And Security 2019, 15: 695-710. DOI: 10.1109/tifs.2019.2925496.Peer-Reviewed Original ResearchPrivacy-preserving modelHomomorphic encryption techniqueDifferential privacy methodReal-world datasetsPrivacy methodsPrivate dataSensitive dataEncryption techniqueSecurity methodsDifferential privacyInformation leakageNaive solutionPrivacyNatural wayGood accuracyScientific collaborationData analysisEncouraging resultsMajor concernSecurityDatasetPotential leakageComputationScenariosPracticability
2016
PRINCESS: Privacy-protecting Rare disease International Network Collaboration via Encryption through Software guard extensionS
Chen F, Wang S, Jiang X, Ding S, Lu Y, Kim J, Sahinalp S, Shimizu C, Burns J, Wright V, Png E, Hibberd M, Lloyd D, Yang H, Telenti A, Bloss C, Fox D, Lauter K, Ohno-Machado L. PRINCESS: Privacy-protecting Rare disease International Network Collaboration via Encryption through Software guard extensionS. Bioinformatics 2016, 33: 871-878. PMID: 28065902, PMCID: PMC5860394, DOI: 10.1093/bioinformatics/btw758.Peer-Reviewed Original ResearchConceptsSoftware Guard ExtensionsHomomorphic encryptionDistributed ComputationCollaboration frameworkTrustworthy computationCollaboration modelSupplementary dataNetwork collaborationEncryptionExperimental resultsHealth informationAlternative solutionComputationInternational collaboration frameworkHardwareAccurate analysisPerformanceBioinformaticsImplementationFrameworkDataExtensionInformationCollaborationSecure Multi-pArty Computation Grid LOgistic REgression (SMAC-GLORE)
Shi H, Jiang C, Dai W, Jiang X, Tang Y, Ohno-Machado L, Wang S. Secure Multi-pArty Computation Grid LOgistic REgression (SMAC-GLORE). BMC Medical Informatics And Decision Making 2016, 16: 89. PMID: 27454168, PMCID: PMC4959358, DOI: 10.1186/s12911-016-0316-1.Peer-Reviewed Original ResearchConceptsData sharingPatient privacySecure multi-party computationModel learning phaseMulti-party computationBiomedical data sharingInformation leakageModel learningIntermediary informationInformation exchangeSecondary usePrivacyBig concernPractical solutionLogistic regression frameworkExperimental resultsSharingRegression frameworkFrameworkMultiple institutionsPrevious workComputationLearningBiomedical researchInformation
2015
Grid multi-category response logistic models
Wu Y, Jiang X, Wang S, Jiang W, Li P, Ohno-Machado L. Grid multi-category response logistic models. BMC Medical Informatics And Decision Making 2015, 15: 10. PMID: 25886151, PMCID: PMC4342889, DOI: 10.1186/s12911-015-0133-y.Peer-Reviewed Original ResearchConceptsGrid modelLikelihood estimation problemClassification performance evaluationReal data setsGrid computingEstimation problemTypes of modelsGrid computationGrid methodPrivacyResponse modelCentralized modelMulti-center dataSuch decompositionsFit assessmentFitting methodLinear modelPerformance evaluationModel constructionData setsModel assumptionsIndividual observationsPractical solutionComputationResultsSimulation results
2012
Grid Binary LOgistic REgression (GLORE): building shared models without sharing data
Wu Y, Jiang X, Kim J, Ohno-Machado L. Grid Binary LOgistic REgression (GLORE): building shared models without sharing data. Journal Of The American Medical Informatics Association 2012, 19: 758-764. PMID: 22511014, PMCID: PMC3422844, DOI: 10.1136/amiajnl-2012-000862.Peer-Reviewed Original ResearchConceptsIntegrity of communicationCentralized data sourcesTraditional LR modelCentral repositoryComputational costData sourcesData setsSame formatPatient dataComputationGenomic dataRare patternRelevant dataLR modelPrediction valueSetRepositoryPartial elementsFormatClassificationCommunicationModelDataPatient setPerform