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

Machine learning (ML) refers to algorithms (often models) that are learned directly from data, germane to past experience. As algorithms have constantly been evolving with the exponential increase of computing power and vastly generated data, privacy of algorithms as well as of data becomes extremel...

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Bibliographic Details
Main Authors:	Baris Balaban, Seyma Selcan Magara, Caglar Yilgor, Altug Yucekul, Ibrahim Obeid, Javier Pizones, Frank Kleinstueck, Francisco Javier Sanchez Perez-Grueso, Ferran Pellise, Ahmet Alanay, Erkay Savas, Cetin Bagci, Osman Ugur Sezerman
Format:	Article
Language:	English
Published:	IEEE 2025-01-01
Series:	IEEE Access
Subjects:	Homomorphic Encryption Privacy-Preserving Machine Learning XGBoost
Online Access:	https://ieeexplore.ieee.org/document/10878994/
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Privacy-Preserving Machine Learning (PPML) Inference for Clinically Actionable Models

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