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

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...

Full description

Saved in:
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/
Tags: Add Tag
No Tags, Be the first to tag this record!

Similar Items

Similar Items