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
Privacy Policy and Technology in Biomedical Data Science
Arellano A, Dai W, Wang S, Jiang X, Ohno-Machado L. Privacy Policy and Technology in Biomedical Data Science. Annual Review Of Biomedical Data Science 2018, 1: 115-129. PMID: 31058261, PMCID: PMC6497413, DOI: 10.1146/annurev-biodatasci-080917-013416.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsReal-world health-care applicationInstitutional review boardBiomedical data scienceClinical data sharingHealth care applicationsCommon rulesData anonymizationDeidentification methodsUnstructured dataEncryption methodSensitive informationPrivacy policiesData deidentificationSecurity rulesEthics topicsResearch ethicsData scienceData sharingData governancePatient privacyConsent practicesHuman subject dataHIPAA privacyPublic trustTerms of technology
2016
Secure 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
2014
Choosing 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
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