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
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
EXpectation Propagation LOgistic REgRession on permissioned blockCHAIN (ExplorerChain): decentralized online healthcare/genomics predictive model learning
Kuo T, Gabriel R, Cidambi K, Ohno-Machado L. EXpectation Propagation LOgistic REgRession on permissioned blockCHAIN (ExplorerChain): decentralized online healthcare/genomics predictive model learning. Journal Of The American Medical Informatics Association 2020, 27: 747-756. PMID: 32364235, PMCID: PMC7309256, DOI: 10.1093/jamia/ocaa023.Peer-Reviewed Original ResearchConceptsBlockchain technologyCentral serverServer-based methodBenefits of blockchainData protection policiesCentralized serverArtificial intelligenceModel learningDecentralized approachSmall datasetsBlockchainServerComputation strategySingle pointGeneralizable modelCost of efficiencyGenomic datasetsDatasetDistributed modelTechnologyGenomic dataMultiple institutionsDiscrimination powerIntelligencePotential advantages/disadvantagesThe 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
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
Blockchain distributed ledger technologies for biomedical and health care applications
Kuo T, Kim H, Ohno-Machado L. Blockchain distributed ledger technologies for biomedical and health care applications. Journal Of The American Medical Informatics Association 2017, 24: 1211-1220. PMID: 29016974, PMCID: PMC6080687, DOI: 10.1093/jamia/ocx068.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsHealth care domainHealth care applicationsBlockchain technologyLedger technologyCare domainFeatures of blockchainBenefits of blockchainCare applicationsFamous BitcoinInformatics researchersBlockchainTechnologyApplicationsCovered topicsBitcoinDomainLatest applicationsPotential challengesDatabase
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 ResearchMeSH KeywordsComputer SecurityGenetic Association StudiesGenomicsHumansMucocutaneous Lymph Node SyndromePrivacyRare DiseasesSoftwareConceptsSoftware Guard ExtensionsHomomorphic encryptionDistributed ComputationCollaboration frameworkTrustworthy computationCollaboration modelSupplementary dataNetwork collaborationEncryptionExperimental resultsHealth informationAlternative solutionComputationInternational collaboration frameworkHardwareAccurate analysisPerformanceBioinformaticsImplementationFrameworkDataExtensionInformationCollaborationProtecting 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 purposesConfidentialityInformationSharing
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 consumers
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 ResearchMeSH KeywordsComparative Effectiveness ResearchComputer SecurityConfidentialityHumansMulticenter Studies as TopicPolicyResearch Design
2012
Preserving 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 ResearchMeSH KeywordsArtificial IntelligenceComputer SecurityElectronic Health RecordsHumansLogistic ModelsPrivacySensitivity and SpecificityConceptsSuspicious accessAccess recordsRule-based techniquesMachine learning methodsConstruction of classifiersAnomaly detectionInformative instancesLearning methodsSymbolic clusteringClassifier performanceSignature detectionIndependent test setInappropriate accessTest setEHRFiltering methodIntegrated filtering strategyFiltering strategyClassifierFilteringNegative rateFalse negative rateAccessDetectionClusteringUsing statistical and machine learning to help institutions detect suspicious access to electronic health records
Boxwala A, Kim J, Grillo J, Ohno-Machado L. Using statistical and machine learning to help institutions detect suspicious access to electronic health records. Journal Of The American Medical Informatics Association 2011, 18: 498-505. PMID: 21672912, PMCID: PMC3128412, DOI: 10.1136/amiajnl-2011-000217.Peer-Reviewed Original ResearchConceptsSuspicious accessMachine-learning methodsPrivacy officersMachine learning techniquesVector machine modelAccess logsElectronic health recordsBaseline methodsAccess dataCross-validation setGold standard setSVM modelWhole data setMachine modelBaseline modelOrganizational dataHealth recordsData setsSVM
1999
Using Boolean reasoning to anonymize databases
Øhrn A, Ohno-Machado L. Using Boolean reasoning to anonymize databases. Artificial Intelligence In Medicine 1999, 15: 235-254. PMID: 10206109, DOI: 10.1016/s0933-3657(98)00056-6.Peer-Reviewed Original ResearchMeSH KeywordsAdultAlgorithmsComputer SecurityConfidentialityDatabases, FactualFemaleHumansMaleMedical RecordsConceptsBoolean reasoningMedical data repositoriesMeasure of anonymitySensitive dataPrivacy issuesDatabase fieldAmount of trustConfidential informationDegree of anonymityData repositoryDeterministic inferenceIndividual objectsAnonymityParticular pieceAlgorithmElectronic medical recordsSpecific needsReasoningDatabasePossible misuseAnonymizationInformationRepositoryOutside worldIssues