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
2020
Privacy-preserving model learning on a blockchain network-of-networks
Kuo T, Kim J, Gabriel R. Privacy-preserving model learning on a blockchain network-of-networks. Journal Of The American Medical Informatics Association 2020, 27: 343-354. PMID: 31943009, PMCID: PMC7025358, DOI: 10.1093/jamia/ocz214.Peer-Reviewed Original ResearchConceptsNetwork topologyExecution timeArt methodsPredictive correctnessPrivacy-preserving learningPrivacy-preserving methodsPrivacy-preserving modelSmall training datasetBlockchain networkBlockchain platformBlockchain technologyPrivacy concernsModel learningComplex dataLearning iterationsLearning methodsTraining datasetConsensus algorithmGeneralizable predictive modelsCorrectness resultsModel disseminationHierarchical networkSmall dataHierarchical approachRecord model
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 analysisPerformanceBioinformaticsImplementationFrameworkDataExtensionInformationCollaboration
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