Hyunghoon (Hoon) Cho, PhD
he/him/his
Cards
About
Research
Publications
2026
Privacy-enhancing sequential learning under heterogeneous selection bias in multi-site electronic health records data
Kundu R, Salvatore M, Patel K, Ohno-Machado L, Cho H, Shi X, Mukherjee B. Privacy-enhancing sequential learning under heterogeneous selection bias in multi-site electronic health records data. Journal Of The American Medical Informatics Association 2026, ocag083. PMID: 42298300, DOI: 10.1093/jamia/ocag083.Peer-Reviewed Original ResearchMichigan Genomics InitiativeElectronic health recordsElectronic health record dataHealth record dataSelection biasAnalysis of smokingInverse probability weightingHealth recordsHealth NetworkAugmented inverse probability weightingHealth platformPatient privacy protectionRecord dataRare outcomesMeta-learning methodsPatient privacyMeta-analysisDiverse biobankLarynx cancerProbability weightingReal-world dataSmokingMeta-learningCentralized algorithmPrivacy protection
2025
Secure phasing of private genomes in a trusted execution environment with TX-Phase
Dokmai N, Zhu K, Sahinalp S, Cho H. Secure phasing of private genomes in a trusted execution environment with TX-Phase. Genome Research 2025, 35: 2626-2636. PMID: 41224532, PMCID: PMC12667372, DOI: 10.1101/gr.280558.125.Peer-Reviewed Original ResearchConceptsSecurity phasePractical runtimeSide-channel leakageState-of-the-artData privacy concernsPrivate genomic dataFixed-point arithmeticData confidentialityExecution environmentPrivacy constraintsPrivacy concernsSide-channelAlgorithmic techniquesServerGenomic dataPractical performanceData setsImputation workflowAnalysis toolsPhase algorithmRobust protectionHaplotype Reference ConsortiumAccuracyPrivacyUsersShechi: A Secure Distributed Computation Compiler Based on Multiparty Homomorphic Encryption.
Smajlović H, Froelicher D, Shajii A, Berger B, Cho H, Numanagić I. Shechi: A Secure Distributed Computation Compiler Based on Multiparty Homomorphic Encryption. 2025, 2025: 7703-7722. PMID: 41685295, PMCID: PMC12892296.Peer-Reviewed Original ResearchMultiparty homomorphic encryptionSecure multiparty computationHomomorphic encryptionState-of-the-art solutionsState-of-the-artHigh-performance computingMultiparty computationAnalysis tasksPython syntaxCompiler optimizationsExpert developersEnd usersRuntime improvementEncryptionData typesEfficient handlingComputational frameworkComputer compilerInput dataComputerCode expressionsDatasetMultipartyCompilationPrincipal component analysisGenerating synthetic electronic health record data: a methodological scoping review with benchmarking on phenotype data and open-source software
Chen X, Wu Z, Shi X, Cho H, Mukherjee B. Generating synthetic electronic health record data: a methodological scoping review with benchmarking on phenotype data and open-source software. Journal Of The American Medical Informatics Association 2025, 32: 1227-1240. PMID: 40460023, PMCID: PMC12203555, DOI: 10.1093/jamia/ocaf082.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsGAN-based methodselectronic health recordsopen-source softwarebaseline methodsMIMIC-IIIgenerative adversarial network (GAN)-based methodsadversarial network (GAN)-based methodssynthetic electronic health recordsdownstream use casesrule-based methodelectronic health record datasetprivacy exposureprivacy protectionevaluation metricsuse casescompetitive performancecondition generation methodsynthetic datadecision treebenchmark methodselectronic health record datadata generationgeneration methodcomprehensive benchmarkMIMIC-IVTX-Phase: Secure Phasing of Private Genomes in a Trusted Execution Environment
Dokmai N, Zhu K, Sahinalp S, Cho H. TX-Phase: Secure Phasing of Private Genomes in a Trusted Execution Environment. Lecture Notes In Computer Science 2025, 15647: 325-329. DOI: 10.1007/978-3-031-90252-9_32.Peer-Reviewed Original ResearchTrusted Execution EnvironmentExecution environmentData privacy concernsSide-channel leakageState-of-the-artExtract valuable insightsOpen-source softwarePrivate genomic dataFixed-point arithmeticData confidentialityPrivacy constraintsPrivacy concernsSecurity phaseAlgorithmic techniquesServerGenomic dataEnhanced accuracyPractical performanceImputation workflowAnalysis toolsDatasetPhase algorithmAccuracyPrivacyHaplotype phasingSecure and federated genome-wide association studies for biobank-scale datasets
Cho H, Froelicher D, Chen J, Edupalli M, Pyrgelis A, Troncoso-Pastoriza J, Hubaux J, Berger B. Secure and federated genome-wide association studies for biobank-scale datasets. Nature Genetics 2025, 57: 809-814. PMID: 39994472, PMCID: PMC11985345, DOI: 10.1038/s41588-025-02109-1.Commentaries, Editorials and Lettersgenome-wide association studiesassociation studiesgenome-wide association study pipelinediscovery of genetic variationsbiobank-scale datasetsgenomic studiesgenetic variationcryptographic toolsprivacy guaranteesdata confidentialityprivate datadistributed algorithmcomputing promisesUK Biobank cohortmultiple entitiessharing datacomputational frameworkBiobank cohortruntimecollaborative analysisdatasetprincipal-component analysislinear mixed modelsprivacydisease1,2Learning-augmented sketching offers improved performance for privacy preserving and secure GWAS
Xu J, Zhu K, Cai J, Kockan C, Dokmai N, Cho H, Woodruff D, Sahinalp S. Learning-augmented sketching offers improved performance for privacy preserving and secure GWAS. IScience 2025, 28: 112011. PMID: 40124506, PMCID: PMC11927738, DOI: 10.1016/j.isci.2025.112011.Peer-Reviewed Original ResearchTrusted Execution EnvironmentGenome-wide association studiesPublic training datasetsComputational resource constraintsOptimize memory usageIntel SGXPrivacy guaranteesPrivacy preservationExecution environmentCloud providersMemory usageMemory constraintsTraining datasetDatasetResource constraintsHigher accuracyPrivacyDedicated memoryExperimental resultsImproved performanceSignificant SNPsGWA studiesAssociation studiesGenotype dataSNPs
2024
Privacy of single-cell gene expression data
Cho H. Privacy of single-cell gene expression data. Patterns 2024, 5: 101096. PMID: 39568471, PMCID: PMC11573887, DOI: 10.1016/j.patter.2024.101096.Commentaries, Editorials and LettersPrivacy-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 Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsprivacy-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 detectionalgorithm
Teaching & Mentoring
Mentoring
Haris Smajlović, PhD
Postdoctoral Associate2025 - PresentVincent Angelo
CBB MS Student2025 - PresentDenis Loginov
Senior Software Engineer2024 - PresentLucy Zheng
CBB PhD student2024 - Present
News & Links
News
- May 20, 2026
Cho and Cheng Awarded U01 Grant to Advance Population-Scale Pangenome Research
- April 17, 2026
Two BIDS Trainees Receive 2026 NSF Graduate Research Fellowships
- March 04, 2026
AI in Medicine: Collaborating on Challenges and Opportunities
- October 15, 2025
Program Spotlight: New Human Genome Sciences PhD Track
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