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
2012
Privacy-preserving heterogeneous health data sharing
Mohammed N, Jiang X, Chen R, Fung B, Ohno-Machado L. Privacy-preserving heterogeneous health data sharing. Journal Of The American Medical Informatics Association 2012, 20: 462-469. PMID: 23242630, PMCID: PMC3628047, DOI: 10.1136/amiajnl-2012-001027.Peer-Reviewed Original ResearchConceptsSet-valued dataDifferential privacyNoise additionPrivacy-preserving mannerAdversary's background knowledgeStrong privacy guaranteesBackground knowledgeHealth data sharingPrivacy modelPrivacy guaranteesSensitive dataData sharingHealthcare dataPrivate mannerAlgorithm designPrivacyRaw dataSynthetic dataAlgorithmHealth dataProbabilistic wayDiscriminative analysisExperimental resultsUseful informationClassification analysis
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
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 ResearchConceptsBoolean reasoningMedical data repositoriesMeasure of anonymitySensitive dataPrivacy issuesDatabase fieldAmount of trustConfidential informationDegree of anonymityData repositoryDeterministic inferenceIndividual objectsAnonymityParticular pieceAlgorithmElectronic medical recordsSpecific needsReasoningDatabasePossible misuseAnonymizationInformationRepositoryOutside worldIssues