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
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