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
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
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
2014
“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 stepMiningSecurityEHRSystemImplementationData
2013
Detecting inappropriate access to electronic health records using collaborative filtering
Menon A, Jiang X, Kim J, Vaidya J, Ohno-Machado L. Detecting inappropriate access to electronic health records using collaborative filtering. Machine Learning 2013, 95: 87-101. PMID: 24683293, PMCID: PMC3967851, DOI: 10.1007/s10994-013-5376-1.Peer-Reviewed Original ResearchElectronic health recordsCollaborative filteringInappropriate accessHealth recordsSuspicious accessPrivacy policiesAccess patternsMachine learningManual auditingSecurity expertsLatent featuresAccess dataRecord accessHistorical dataSecurityFilteringUnrestricted accessFuture violationsAccessAudit processSVMUsersDatasetLearningAuditingIdentifying inference attacks against healthcare data repositories.
Vaidya J, Shafiq B, Jiang X, Ohno-Machado L. Identifying inference attacks against healthcare data repositories. AMIA Joint Summits On Translational Science Proceedings 2013, 2013: 262-6. PMID: 24303279, PMCID: PMC3845790.Peer-Reviewed Original Research