Privacy-Preserving Machine Learning (PPML) Inference for Clinically Actionable Models

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dc.contributor.authorBalaban, Baris
dc.contributor.authorMagara, Seyma Selcan
dc.contributor.authorYilgor, Caglar
dc.contributor.authorYucekul, Altug
dc.contributor.authorObeid, Ibrahim
dc.contributor.authorPizones, Javier
dc.contributor.authorKleinstueck, Frank
dc.contributor.authorPerez\\-Grueso, Francisco Javier Sanchez
dc.contributor.authorPellise, Ferran
dc.contributor.authorAlanay, Ahmet
dc.contributor.authorSavas, Erkay
dc.contributor.authorBagci, Cetin
dc.contributor.authorSezerman, Osman Ugur
dc.contributor.authorEuropean Spine Study Group, European Spine Study
dc.date.accessioned2025-10-16T15:12:14Z
dc.date.issued2025
dc.identifier.doi10.1109/ACCESS.2025.3540261
dc.identifier.otherWOS:001438240800035
dc.identifier.urihttps://openaccess.acibadem.edu.tr/handle/11443/5320
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.sourceIEEE ACCESS
dc.subjectData models
dc.subjectComputational modeling
dc.subjectMachine learning
dc.subjectMachine learning algorithms
dc.subjectCryptography
dc.subjectAccuracy
dc.subjectAnalytical models
dc.subjectInference algorithms
dc.subjectHomomorphic encryption
dc.subjectData privacy
dc.subjectHomomorphic Encryption
dc.subjectPrivacy\\-Preserving Machine Learning
dc.subjectXGBoost
dc.titlePrivacy-Preserving Machine Learning (PPML) Inference for Clinically Actionable Models
dc.typeArticle

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