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 researchInformationDataChallenges
2021
Calibrating 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
The Data Tags Suite (DATS) model for discovering data access and use requirements
Alter G, Gonzalez-Beltran A, Ohno-Machado L, Rocca-Serra P. The Data Tags Suite (DATS) model for discovering data access and use requirements. GigaScience 2020, 9: giz165. PMID: 32031623, PMCID: PMC7006671, DOI: 10.1093/gigascience/giz165.Peer-Reviewed Original ResearchConceptsData accessData discovery toolsPrivacy of subjectsData use agreementsConfidential dataMetadata itemsData reuseMetadata schemaAutomated systemDiscovery toolTechnical systemsStandard wayUse agreementsAccessPrivacyMetadataSchemaUse requirementsReuseResearchersResearch dataSystemRequirementsInformationData
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 applicationsApplicationsSharingSecure 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
2017
A community effort to protect genomic data sharing, collaboration and outsourcing
Wang S, Jiang X, Tang H, Wang X, Bu D, Carey K, Dyke S, Fox D, Jiang C, Lauter K, Malin B, Sofia H, Telenti A, Wang L, Wang W, Ohno-Machado L. A community effort to protect genomic data sharing, collaboration and outsourcing. Npj Genomic Medicine 2017, 2: 33. PMID: 29263842, PMCID: PMC5677972, DOI: 10.1038/s41525-017-0036-1.Peer-Reviewed Original ResearchGenomic data sharingData sharingSecure computation techniquesPrivacy-preserving techniquesPublic cloud servicesMagnitude performance improvementThird Critical AssessmentData outsourcingCloud servicesPrivacy challengesData privacyHuman genomic dataSecurity researchersPrivacy risksSensitive informationComputer privacyBeacon serviceBiomedical informaticistsSecurity concernsMemory requirementsCollaborative discoveryComputation techniquesComputational runtimePrivacyTrack 2
2016
Protecting genomic data analytics in the cloud: state of the art and opportunities
Tang H, Jiang X, Wang X, Wang S, Sofia H, Fox D, Lauter K, Malin B, Telenti A, Xiong L, Ohno-Machado L. Protecting genomic data analytics in the cloud: state of the art and opportunities. BMC Medical Genomics 2016, 9: 63. PMID: 27733153, PMCID: PMC5062944, DOI: 10.1186/s12920-016-0224-3.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsHuman genomic dataSecure computation techniquesPublic cloud environmentSecure computation methodsGenomic data analyticsReal-world environmentsSecond Critical AssessmentSecure outsourcingCloud environmentCryptographic technologyPublic cloudSecure collaborationUnauthorized usersComputation tasksData privacyData analyticsBiomedical computingData scientistsComputational environmentGenomic dataWorld environmentComputation techniquesMultiple organizationsPractical algorithmPrivacyGenome privacy: challenges, technical approaches to mitigate risk, and ethical considerations in the United States
Wang S, Jiang X, Singh S, Marmor R, Bonomi L, Fox D, Dow M, Ohno‐Machado L. Genome privacy: challenges, technical approaches to mitigate risk, and ethical considerations in the United States. Annals Of The New York Academy Of Sciences 2016, 1387: 73-83. PMID: 27681358, PMCID: PMC5266631, DOI: 10.1111/nyas.13259.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsData privacySensitive individual informationComputer science communityReal-world problemsUnauthorized partiesHuman genomic dataPrivacy breachesData accessData sharingData accessibilityConfidentiality protectionGenomic dataSpectrum of techniquesIndividual informationPrivacyScience communityPhenotype informationTechnical approachPotential solutionsCurrent common practiceBiomedical researchResearch purposesConfidentialityInformationSharingSecure 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
Comparison of consumers’ views on electronic data sharing for healthcare and research
Kim K, Joseph J, Ohno-Machado L. Comparison of consumers’ views on electronic data sharing for healthcare and research. Journal Of The American Medical Informatics Association 2015, 22: 821-830. PMID: 25829461, PMCID: PMC5009901, DOI: 10.1093/jamia/ocv014.Peer-Reviewed Original ResearchConceptsElectronic data sharingData sharingHealth information exchangeData networksHealth informationTechnology infrastructureInformation exchangePrivacyHealth Insurance PortabilitySharingAccountability ActUse of dataInsurance PortabilitySecurityNetworkInformationHealthcareIndividual controlHealthcare deliveryMere relianceDepth studyPortabilityAccessInfrastructureComparison of consumersGrid 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 resultsPreserving Genome Privacy in Research Studies
Wang S, Jiang X, Fox D, Ohno-Machado L. Preserving Genome Privacy in Research Studies. 2015, 425-441. DOI: 10.1007/978-3-319-23633-9_16.Peer-Reviewed Original ResearchGenome privacyPrivacy researchBetter privacy protectionObfuscation of dataSecure data repositoryLoss of privacyData use agreementsPrivacy challengesPrivacy problemsPrivacy protectionAttack modelIndividual privacyData sharingMassive collectionPrivacyData repositoryTraditional clinical informationScientific discoveryGenomic dataData analysis methodsBig challengeUse agreementsBiomedical communityTechnical aspects
2014
A community assessment of privacy preserving techniques for human genomes
Jiang X, Zhao Y, Wang X, Malin B, Wang S, Ohno-Machado L, Tang H. A community assessment of privacy preserving techniques for human genomes. BMC Medical Informatics And Decision Making 2014, 14: s1. PMID: 25521230, PMCID: PMC4290799, DOI: 10.1186/1472-6947-14-s1-s1.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsBiomedical dataPrivacy preserving techniquesPrivacy protection techniquesData privacyBiomedical computingHuman genomic dataData donorsDissemination techniquesPersonal Genome ProjectRaw dataProtection techniquesRigorous protectionPrivacyGenomic dataFinal resultsComputingCommunity effortsAnalysis outcomesChallengesTechniqueDataProjectChoosing blindly but wisely: differentially private solicitation of DNA datasets for disease marker discovery
Zhao Y, Wang X, Jiang X, Ohno-Machado L, Tang H. Choosing blindly but wisely: differentially private solicitation of DNA datasets for disease marker discovery. Journal Of The American Medical Informatics Association 2014, 22: 100-108. PMID: 25352565, PMCID: PMC4433380, DOI: 10.1136/amiajnl-2014-003043.Peer-Reviewed Original ResearchConceptsData ownersData usersHuman genomic datasetsHuman genomic dataPatient privacyPrivacyGeneration approachUsersData selectionReal dataDatasetGenomic datasetsPrivate solicitationDNA datasetsScientific discoveryNew approachGenomic dataHigh confidencePilot versionEvaluation methodRight choiceOwnersAlgorithmNew techniqueDisease marker discovery“Big Data” and the Electronic Health Record
Ross M, Wei W, Ohno-Machado L. “Big Data” and the Electronic Health Record. Yearbook Of Medical Informatics 2014, 23: 97-104. PMID: 25123728, PMCID: PMC4287068, DOI: 10.15265/iy-2014-0003.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsBig dataEHR systemsElectronic health record systemsHealth record systemsData miningElectronic health recordsData applicationsActionable knowledgeMassive numberAdditional keywordsNew keywordsSecondary useInformatics professionalsHealth recordsRecord systemKeywordsLarge amountPrivacyNext stepMiningSecurityEHRSystemImplementationDataPrivacy 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
Data governance requirements for distributed clinical research networks: triangulating perspectives of diverse stakeholders
Kim K, Browe D, Logan H, Holm R, Hack L, Ohno-Machado L. Data governance requirements for distributed clinical research networks: triangulating perspectives of diverse stakeholders. Journal Of The American Medical Informatics Association 2013, 21: 714-719. PMID: 24302285, PMCID: PMC4078279, DOI: 10.1136/amiajnl-2013-002308.Peer-Reviewed Original ResearchConceptsFair Information Practice PrinciplesTechnical infrastructureClinical data reuseGovernance requirementsTrustworthy platformData reuseHealth Insurance PortabilityHIPAA regulationsAccountability ActNetworkInsurance PortabilityHealth informationRequirementsInformationResearch NetworkPrivacyPortabilityBest practicesInfrastructurePlatformReuseDiverse stakeholdersTimelinessDevelopment of a Privacy and Security Policy Framework for a Multistate Comparative Effectiveness Research Network
Kim K, McGraw D, Mamo L, Ohno-Machado L. Development of a Privacy and Security Policy Framework for a Multistate Comparative Effectiveness Research Network. Medical Care 2013, 51: s66-s72. PMID: 23774516, DOI: 10.1097/mlr.0b013e31829b1d9f.Peer-Reviewed Original Research