2023
A hierarchical strategy to minimize privacy risk when linking “De-identified” data in biomedical research consortia
Ohno-Machado L, Jiang X, Kuo T, Tao S, Chen L, Ram P, Zhang G, Xu H. A hierarchical strategy to minimize privacy risk when linking “De-identified” data in biomedical research consortia. Journal Of Biomedical Informatics 2023, 139: 104322. PMID: 36806328, PMCID: PMC10975485, DOI: 10.1016/j.jbi.2023.104322.Peer-Reviewed Original ResearchConceptsPrivacy of individualsAppropriate privacy protectionData-driven modelsPrivacy protectionPrivacy risksData Coordination CenterData hubData repositoryHierarchical strategyPrivacyBiomedical discoveryData setsRecord linkageData Coordinating CenterRepositoryComplex strategiesCoordination centerTechnologyTechniqueDataPartiesSetHierarchy
2022
The evolving privacy and security concerns for genomic data analysis and sharing as observed from the iDASH competition
Kuo T, Jiang X, Tang H, Wang X, Harmanci A, Kim M, Post K, Bu D, Bath T, Kim J, Liu W, Chen H, Ohno-Machado L. The evolving privacy and security concerns for genomic data analysis and sharing as observed from the iDASH competition. Journal Of The American Medical Informatics Association 2022, 29: 2182-2190. PMID: 36164820, PMCID: PMC9667175, DOI: 10.1093/jamia/ocac165.Peer-Reviewed Original ResearchConceptsSensitive personal informationGenomic data analysisPotential future research directionsPersonal informationSecurity concernsGenomics data repositoryData repositoryReport lessonsProtection techniquesFuture research directionsPrivacyResearch directionsData usePractical challengesGenomic dataData analysisAnonymizationCommunity effortsRepositorySecurityBiomedical researchInformationDataChallengesCodesigning a community-based participatory research project to assess tribal perspectives on privacy and health data sharing: A report from the Strong Heart Study
Triplett C, Fletcher B, Taitingfong R, Zhang Y, Ali T, Ohno-Machado L, Bloss C. Codesigning a community-based participatory research project to assess tribal perspectives on privacy and health data sharing: A report from the Strong Heart Study. Journal Of The American Medical Informatics Association 2022, 29: 1120-1127. PMID: 35349678, PMCID: PMC9093024, DOI: 10.1093/jamia/ocac038.Peer-Reviewed Original Research
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 imputationImputationDownloadSecurityOutsourcingComputationCodeServicesRequirementsAccuracyMethodVERTIcal Grid lOgistic regression with Confidence Intervals (VERTIGO-CI).
Kim J, Li W, Bath T, Jiang X, Ohno-Machado L. VERTIcal Grid lOgistic regression with Confidence Intervals (VERTIGO-CI). AMIA Joint Summits On Translational Science Proceedings 2021, 2021: 355-364. PMID: 34457150, PMCID: PMC8378611.Peer-Reviewed Original ResearchConceptsDual spaceCovariance matrixVariance estimationSpace modelTest statisticKernel matrixLinear modelReal dataTolerable performanceNovel extensionDual-space modelPoint estimatesEquivalent accuracyCentralized versionStatisticsRegression modelsModelMatrixExtensionDual objectivesCentralized settingFederated LearningEstimationPrivacy-preserving mannerSpaceCalibrating predictive model estimates in a distributed network of patient data
Huang Y, Jiang X, Gabriel R, Ohno-Machado L. Calibrating predictive model estimates in a distributed network of patient data. Journal Of Biomedical Informatics 2021, 117: 103758. PMID: 33811986, DOI: 10.1016/j.jbi.2021.103758.Peer-Reviewed Original ResearchConceptsData privacyRecalibration modelHigh-performance predictive modelsIntegration of dataPatient dataPredictive model estimatesDistributed networkExpected calibration errorMaximum calibration errorPrivacyClinical informaticsCalibration errorsComputational efficiencyPredictive analysisAlgorithmBuilding modelsModel buildingImportant issuePerformance measuresPredictive modelMultiple health systemsLarge numberIsotonic regressionInformaticsSystem
2020
A systematic literature review of Native American and Pacific Islanders’ perspectives on health data privacy in the United States
Taitingfong R, Bloss C, Triplett C, Cakici J, Garrison N, Cole S, Stoner J, Ohno-Machado L. A systematic literature review of Native American and Pacific Islanders’ perspectives on health data privacy in the United States. Journal Of The American Medical Informatics Association 2020, 27: 1987-1998. PMID: 33063114, PMCID: PMC7727344, DOI: 10.1093/jamia/ocaa235.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus Statements
2019
Protecting patient privacy in survival analyses
Bonomi L, Jiang X, Ohno-Machado L. Protecting patient privacy in survival analyses. Journal Of The American Medical Informatics Association 2019, 27: 366-375. PMID: 31750926, PMCID: PMC7025359, DOI: 10.1093/jamia/ocz195.Peer-Reviewed Original ResearchConceptsPrivacy protectionPrivacy risksHealthcare applicationsPatient privacyPrivacy protection methodProvable privacy protectionStrong privacy protectionPerson of interestKnowledgeable adversaryDifferential privacySynthetic datasetsFormal modelEpidemiology datasetPrivacyNonparametric survival modelFuture research directionsAdversaryResearch directionsDatasetBiomedical research applicationsFrameworkFrequent sharingResearch applicationsApplicationsSharing
2018
A Scalable Privacy-preserving Data Generation Methodology for Exploratory Analysis.
Vaidya J, Shafiq B, Asani M, Adam N, Jiang X, Ohno-Machado L. A Scalable Privacy-preserving Data Generation Methodology for Exploratory Analysis. AMIA Annual Symposium Proceedings 2018, 2017: 1695-1704. PMID: 29854240, PMCID: PMC5977652.Peer-Reviewed Original ResearchConceptsPrivacy-preserving approachData management systemBig dataBiomedical datasetsClassification taskBiomedical dataContext of regressionManagement systemSynthetic dataGeneration methodologyEssential problemResearch tasksAdditional datasetsDatasetTaskSignificant effortsDirect accessFirstorder approximationDataParticular typeAccessPrecision medicine
2017
Prerequisites for International Exchanges of Health Information for Record Research: Comparison of Australian, Austrian, Finnish, Swiss, and US Policies.
Suominen H, Müller H, Ohno-Machado L, Salanterä S, Schreier G, Hanlen L. Prerequisites for International Exchanges of Health Information for Record Research: Comparison of Australian, Austrian, Finnish, Swiss, and US Policies. 2017, 245: 1312. PMID: 29295395.Peer-Reviewed Original Research
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 analysisPerformanceBioinformaticsImplementationFrameworkDataExtensionInformationCollaboration
2014
Privacy Preserving RBF Kernel Support Vector Machine
Li H, Xiong L, Ohno-Machado L, Jiang X. Privacy Preserving RBF Kernel Support Vector Machine. BioMed Research International 2014, 2014: 827371. PMID: 25013805, PMCID: PMC4071990, DOI: 10.1155/2014/827371.Peer-Reviewed Original ResearchConceptsPrivate dataPrivacy-preserving data disseminationKernel support vector machineRBF kernel support vector machinePublic dataSupport vector machineSupport vector machine modelVector machine modelData disseminationData sharingBiomedical dataPrivacy standardsVector machineRBF kernelPerformance metricsSVMMachine modelFull usePrivacyFinal outputSeparable caseAvailable informationMachineSharingMetricsDifferentially private distributed logistic regression using private and public data
Ji Z, Jiang X, Wang S, Xiong L, Ohno-Machado L. Differentially private distributed logistic regression using private and public data. BMC Medical Genomics 2014, 7: s14. PMID: 25079786, PMCID: PMC4101668, DOI: 10.1186/1755-8794-7-s1-s14.Peer-Reviewed Original ResearchConceptsPrivate dataDifferential privacyPublic datasetsPublic dataRigorous privacy guaranteeData privacy researchPrivate data setsData mining modelsData setsProvable privacyPrivacy guaranteesMining modelPrivacy researchDifferent data setsArt frameworksMedical informaticsPrivacyAmount of noisePrivate methodsAuxiliary informationBetter utilityNew algorithmUpdate stepAvailable public dataAlgorithm
2013
EXpectation Propagation LOgistic REgRession (EXPLORER): Distributed privacy-preserving online model learning
Wang S, Jiang X, Wu Y, Cui L, Cheng S, Ohno-Machado L. EXpectation Propagation LOgistic REgRession (EXPLORER): Distributed privacy-preserving online model learning. Journal Of Biomedical Informatics 2013, 46: 480-496. PMID: 23562651, PMCID: PMC3676314, DOI: 10.1016/j.jbi.2013.03.008.Peer-Reviewed Original ResearchConceptsHigh-level guaranteesOnline model learningSensitive informationModel learningEntire dataOnline learningAbsence of participantsMore flexibilitySame performanceExperimental resultsLearningCommunicationServerInformationGuaranteesModel updatingPosterior distributionServicesClientsUpdatingFrameworkFlexibilityModelPerformance
2012
Privacy-preserving heterogeneous health data sharing
Mohammed N, Jiang X, Chen R, Fung B, Ohno-Machado L. Privacy-preserving heterogeneous health data sharing. Journal Of The American Medical Informatics Association 2012, 20: 462-469. PMID: 23242630, PMCID: PMC3628047, DOI: 10.1136/amiajnl-2012-001027.Peer-Reviewed Original ResearchConceptsSet-valued dataDifferential privacyNoise additionPrivacy-preserving mannerAdversary's background knowledgeStrong privacy guaranteesBackground knowledgeHealth data sharingPrivacy modelPrivacy guaranteesSensitive dataData sharingHealthcare dataPrivate mannerAlgorithm designPrivacyRaw dataSynthetic dataAlgorithmHealth dataProbabilistic wayDiscriminative analysisExperimental resultsUseful informationClassification analysisA collaborative framework for Distributed Privacy-Preserving Support Vector Machine learning.
Que J, Jiang X, Ohno-Machado L. A collaborative framework for Distributed Privacy-Preserving Support Vector Machine learning. AMIA Annual Symposium Proceedings 2012, 2012: 1350-9. PMID: 23304414, PMCID: PMC3540462.Peer-Reviewed Original ResearchConceptsSupport vector machineVector machinePrivacy-preserving collaborative learningSensitive raw dataPrivacy-preserving mannerEfficient information exchangeDistributed PrivacyLocal repositoryPrivacy concernsCentralized repositoryCollaborative frameworkDecision supportMultiple participantsInformation exchangeRaw dataSVM modelIntermediary resultsMachineCollaborative learningPrivacyPopular toolRepositoryTraditional wayPatient dataServerPreserving Institutional Privacy in Distributed binary Logistic Regression.
Wu Y, Jiang X, Ohno-Machado L. Preserving Institutional Privacy in Distributed binary Logistic Regression. AMIA Annual Symposium Proceedings 2012, 2012: 1450-8. PMID: 23304425, PMCID: PMC3540539.Peer-Reviewed Original Research
2011
Anomaly and signature filtering improve classifier performance for detection of suspicious access to EHRs.
Kim J, Grillo J, Boxwala A, Jiang X, Mandelbaum R, Patel B, Mikels D, Vinterbo S, Ohno-Machado L. Anomaly and signature filtering improve classifier performance for detection of suspicious access to EHRs. AMIA Annual Symposium Proceedings 2011, 2011: 723-31. PMID: 22195129, PMCID: PMC3243249.Peer-Reviewed Original ResearchConceptsSuspicious accessAccess recordsRule-based techniquesMachine learning methodsConstruction of classifiersAnomaly detectionInformative instancesLearning methodsSymbolic clusteringClassifier performanceSignature detectionIndependent test setInappropriate accessTest setEHRFiltering methodIntegrated filtering strategyFiltering strategyClassifierFilteringNegative rateFalse negative rateAccessDetectionClustering
2004
Protecting patient privacy by quantifiable control of disclosures in disseminated databases
Ohno-Machado L, Silveira P, Vinterbo S. Protecting patient privacy by quantifiable control of disclosures in disseminated databases. International Journal Of Medical Informatics 2004, 73: 599-606. PMID: 15246040, DOI: 10.1016/j.ijmedinf.2004.05.002.Peer-Reviewed Original ResearchConceptsSensitive patient dataPattern recognition algorithmsLevel of confidentialitySensitive dataPrivacy protectionSensitive informationDisseminated dataRecognition algorithmDegree of anonymityPatient privacyAlgorithmPrivacyPatient dataDatabaseAnonymizationQuantifiable controlPublic health purposesConfidentialityInformationAnonymityHealth care organizationsHealth purposesCare organizationsCommon practiceAmbiguation
2001
Disambiguation data: extracting information from anonymized sources.
Dreiseitl S, Vinterbo S, Ohno-Machado L. Disambiguation data: extracting information from anonymized sources. AMIA Annual Symposium Proceedings 2001, 144-8. PMID: 11825171, PMCID: PMC2243291.Peer-Reviewed Original Research