Hyunghoon Cho, PhD
he/him/his
Cards
About
Research
Publications
2024
Privacy of single-cell gene expression data
Cho H. Privacy of single-cell gene expression data. Patterns 2024, 5: 101096. DOI: 10.1016/j.patter.2024.101096.Peer-Reviewed Original ResearchPrivacy-Enhancing Technologies in Biomedical Data Science
Cho H, Froelicher D, Dokmai N, Nandi A, Sadhuka S, Hong M, Berger B. Privacy-Enhancing Technologies in Biomedical Data Science. Annual Review Of Biomedical Data Science 2024, 7: 317-343. PMID: 39178425, PMCID: PMC11346580, DOI: 10.1146/annurev-biodatasci-120423-120107.Peer-Reviewed Original ResearchConceptsPrivacy-enhancing technologiesAdoption of privacy-enhancing technologiesBiomedical data scienceData scienceAnalyze sensitive dataBiomedical data repositoriesPrivacy protectionSensitive dataPrivacy concernsData silosProtect privacyHuman subject dataBiomedical domainData repositoriesPrivacySubjective dataConventional frameworkSecure discovery of genetic relatives across large-scale and distributed genomic datasets
Hong M, Froelicher D, Magner R, Popic V, Berger B, Cho H. Secure discovery of genetic relatives across large-scale and distributed genomic datasets. Genome Research 2024, 34: gr.279057.124. PMID: 39111815, PMCID: PMC11529841, DOI: 10.1101/gr.279057.124.Peer-Reviewed Original ResearchMultiparty homomorphic encryptionIdentity-by-descentEffective hash functionsGenomic datasetsHomomorphic encryptionHash functionPrivate dataFederated algorithmBucketing strategyData holdersData silosDegree of relatednessRelation detectionGenetic relationEfficient algorithmMultiple entitiesRelatedness coefficientsPairs of individualsGenomic studiesDatasetIdentification of relationsRuntimeGenetic sequencesAccurate detectionAlgorithmSecure Discovery of Genetic Relatives Across Large-Scale and Distributed Genomic Datasets
Hong M, Froelicher D, Magner R, Popic V, Berger B, Cho H. Secure Discovery of Genetic Relatives Across Large-Scale and Distributed Genomic Datasets. Lecture Notes In Computer Science 2024, 14758: 308-313. PMID: 39027313, PMCID: PMC11257153, DOI: 10.1007/978-1-0716-3989-4_19.Peer-Reviewed Original ResearchIdentity-by-descentMultiparty homomorphic encryptionGenomic datasetsPairwise sequence comparisonsPrivacy-preserving solutionsDegree of relatednessEffective hash functionsGenetic relationPairs of individualsRelatedness coefficientsSequence comparisonCryptographic techniquesHomomorphic encryptionPrivacy guaranteesHash functionPrivate dataFederated algorithmPrivacy concernsGenetic sequencesData silosRelation detectionEfficient algorithmMultiple entitiesBurden of operatorsPrivacy
2023
Reconstruction of private genomes through reference-based genotype imputation
Mosca M, Cho H. Reconstruction of private genomes through reference-based genotype imputation. Genome Biology 2023, 24: 271. PMID: 38053191, PMCID: PMC10698978, DOI: 10.1186/s13059-023-03105-6.Peer-Reviewed Original ResearchAssessing transcriptomic reidentification risks using discriminative sequence models
Sadhuka S, Fridman D, Berger B, Cho H. Assessing transcriptomic reidentification risks using discriminative sequence models. Genome Research 2023, 33: 1101-1112. PMID: 37541758, PMCID: PMC10538488, DOI: 10.1101/gr.277699.123.Peer-Reviewed Original ResearchConceptsExpression quantitative trait lociGene expression dataExpression dataQuantitative trait lociOmics data setsGene expression profilesTrait lociGenomic regionsGenetic variationGene expressionExpression profilesMolecular insightsLinkage disequilibriumFunctional impactGenotypesTranscriptomicsLociSame individualDisequilibriumSequenceExpressionPrevious studiesFull extentData setssfkit: a web-based toolkit for secure and federated genomic analysis.
Mendelsohn S, Froelicher D, Loginov D, Bernick D, Berger B, Cho H. sfkit: a web-based toolkit for secure and federated genomic analysis. Nucleic Acids Research 2023, 51: w535-w541. PMID: 37246709, PMCID: PMC10320181, DOI: 10.1093/nar/gkad464.Peer-Reviewed Original ResearchConceptsCommand line interfaceGroup of collaboratorsCryptographic techniquesPrivacy concernsCollaborative workflowsUse casesWeb-based toolkitWeb serverComputational environmentCollaborative toolsMultiple partiesEssential taskDatasetServerPrivacyGenomic data collectionPrincipal component analysisToolkitData collectionWorkflowToolTaskComponent analysisRecent workComplexityScalable and Privacy-Preserving Federated Principal Component Analysis
Froelicher D, Cho H, Edupalli M, Sousa J, Bossuat J, Pyrgelis A, Troncoso-Pastoriza J, Berger B, Hubaux J. Scalable and Privacy-Preserving Federated Principal Component Analysis. 2016 IEEE Symposium On Security And Privacy (SP) 2023, 00: 1908-1925. PMID: 38665901, PMCID: PMC11044025, DOI: 10.1109/sp46215.2023.10179350.Peer-Reviewed Original ResearchHomomorphic encryptionData providersMultiparty homomorphic encryptionPrivacy-preserving alternativeMultiple data providersSecure multiparty computationPassive adversary modelData science domainCleartext dataData confidentialityPrivate dataMultiparty computationSecure systemsInteractive protocolDataset dimensionsEssential algorithmsCentralized solutionData distributionScience domainLocal analysis resultsDimensionality reductionIntermediate resultsEncryptionPrincipal component analysisOriginal dataSequre: a high-performance framework for secure multiparty computation enables biomedical data sharing
Smajlović H, Shajii A, Berger B, Cho H, Numanagić I. Sequre: a high-performance framework for secure multiparty computation enables biomedical data sharing. Genome Biology 2023, 24: 5. PMID: 36631897, PMCID: PMC9832703, DOI: 10.1186/s13059-022-02841-5.Peer-Reviewed Original ResearchConceptsSecure multiparty computationHigh-performance frameworkMultiparty computationMPC applicationsSensitive biomedical dataRapid application developmentPython programming languageCompile-time optimizationBiomedical data sharingCryptographic toolsApplication developmentInvolved entitiesProgramming languageBioinformatics tasksData sharingBiomedical dataPrivate informationComputationFrameworkUsabilitySharingApplicationsSyntaxPerformanceTask
2022
k-SALSA: k-Anonymous Synthetic Averaging of Retinal Images via Local Style Alignment
Jeon M, Park H, Kim H, Morley M, Cho H. k-SALSA: k-Anonymous Synthetic Averaging of Retinal Images via Local Style Alignment. Lecture Notes In Computer Science 2022, 13681: 661-678. PMID: 37525827, PMCID: PMC10388376, DOI: 10.1007/978-3-031-19803-8_39.Peer-Reviewed Original ResearchStyle alignmentMembership inference attacksRetinal imagesGenerative adversarial networkPotential of machineRetinal image analysisRetinal fundus imagesK-anonymityInference attacksPrivacy notionPrivate datasetAdversarial networkData sharingBenchmark datasetsTraining dataClassification performanceModern machineArt techniquesSource imagesImage fidelityFundus imagesPrior workVisual patternsImage analysisImages
Teaching & Mentoring
Mentoring
Lucy Zheng
CBB PhD student2024 - PresentAnupama Nandi
Postdoc2023 - PresentNatnatee Dokmai
Postdoc2023 - Present