Hyunghoon Cho, PhD
Cards
About
Titles
Assistant Professor of Biomedical Informatics & Data Science and of Computer Science
Biography
Hyunghoon (Hoon) Cho received his Ph.D. in Electrical Engineering and Computer Science at MIT in 2019. Previously, he received his M.S. and B.S. with Honors in Computer Science from Stanford University. His research focuses on overcoming key computational challenges in analyzing massive and distributed biomedical data, creating modern tools from applied cryptography and machine learning. He is especially interested in solving problems in the areas of biomedical data privacy, single-cell genomics, and network biology. He is a recipient of the NIH Director's Early Independence Award.
Appointments
Biomedical Informatics & Data Science
Assistant ProfessorPrimary
Other Departments & Organizations
Education & Training
- Schmidt Fellow (PI)
- Broad Institute of MIT and Harvard (2023)
- PhD
- Massachusetts Institute of Technology, Electrical Engineering and Computer Science (2019)
- MS
- Stanford University, Computer Science (2013)
- BS (Hon)
- Stanford University, Computer Science (2013)
Research
Publications
2024
Secure 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. 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 analysisImagesMechanisms for Hiding Sensitive Genotypes With Information-Theoretic Privacy
Ye F, Cho H, Rouayheb S. Mechanisms for Hiding Sensitive Genotypes With Information-Theoretic Privacy. IEEE Transactions On Information Theory 2022, 68: 4090-4105. PMID: 37283781, PMCID: PMC10243750, DOI: 10.1109/tit.2022.3156276.Peer-Reviewed Original ResearchInformation-theoretic privacyGenomic data sharingOptimal greedy algorithmCritical health-related informationEfficient algorithmic implementationPrivacy leakagePrivacy mechanismsPrivacy problemsPersonal genomics servicesData sharingPrivacyGreedy algorithmStandard modeling approachesComplexity polynomialOptimal utilityAlgorithmic implementationProcessing orderHealth-related informationStraightforward solutionMarkov modelGenomic dataNearby positionsModeling approachGenomic servicesUsers
2021
Truly privacy-preserving federated analytics for precision medicine with multiparty homomorphic encryption
Froelicher D, Troncoso-Pastoriza J, Raisaro J, Cuendet M, Sousa J, Cho H, Berger B, Fellay J, Hubaux J. Truly privacy-preserving federated analytics for precision medicine with multiparty homomorphic encryption. Nature Communications 2021, 12: 5910. PMID: 34635645, PMCID: PMC8505638, DOI: 10.1038/s41467-021-25972-y.Peer-Reviewed Original ResearchConceptsMultiparty homomorphic encryptionHomomorphic encryptionPrivacy-preserving analysisNecessary key stepMultiple healthcare institutionsFederated analyticsFederated settingAnalysis tasksAnalytics systemIntermediate dataEncryptionCentralized studiesPatient dataBiomedical insightsScientific collaborationAccurate resultsIndispensable complementAnalyticsHealthcare institutionsDatasetTaskSystemBiomedical researchAccessCollaborationPrivacy-preserving genotype imputation in a trusted execution environment
Dokmai N, Kockan C, Zhu K, Wang X, Sahinalp S, Cho H. Privacy-preserving genotype imputation in a trusted execution environment. Cell Systems 2021, 12: 983-993.e7. PMID: 34450045, PMCID: PMC8542641, DOI: 10.1016/j.cels.2021.08.001.Peer-Reviewed Original ResearchConceptsTrusted Execution EnvironmentExecution environmentHardware-based solutionsSide-channel attacksIntel SGXEnhanced securityPrivacy concernsAnalysis servicesImputation ServerServer limitData resourcesImputation algorithmSGXServerImputation softwareGenomic data resourcesImputation accuracyGenotype imputationImputation strategiesServicesDownstream analysisScalabilityImputationEssential toolSecurity
Teaching & Mentoring
Mentoring
Lucy Zheng
Graduate student2024 - PresentAnupama Nandi
Postdoc2023 - PresentNatnatee Dokmai
Postdoc2023 - Present