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

Balaban, Baris; Magara, Seyma Selcan; Yilgor, Caglar; Yucekul, Altug; Obeid, Ibrahim; Pizones, Javier; Kleinstueck, Frank; Perez\\-Grueso, Francisco Javier Sanchez; Pellise, Ferran; Alanay, Ahmet; Savas, Erkay; Bagci, Cetin; Sezerman, Osman Ugur; European Spine Study Group, European Spine Study

doi:10.1109/ACCESS.2025.3540261

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

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Privacy-Preserving Machine Learning (PPML) Inference for Clinically Actionable Models.pdf (2.54 MB)

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2025

Authors

Perez\\-Grueso, Francisco Javier Sanchez

Pellise, Ferran

Alanay, Ahmet

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10.1109/ACCESS.2025.3540261

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WOS:001438240800035

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IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

DOI

10.1109/ACCESS.2025.3540261

Keywords

Data models , Computational modeling , Machine learning , Machine learning algorithms , Cryptography , Accuracy , Analytical models , Inference algorithms , Homomorphic encryption , Data privacy , Homomorphic Encryption , Privacy\\-Preserving Machine Learning , XGBoost

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https://openaccess.acibadem.edu.tr/handle/11443/5320

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

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