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

dc.contributor.author	Magara, Seyma Selcan
dc.date.accessioned	2026-01-09T08:15:14Z
dc.date.available	2026-01-09T08:15:14Z
dc.date.issued	2025
dc.identifier.issn	2169-3536
dc.identifier.uri	http://hdl.handle.net/10900/173822
dc.language.iso	en	de_DE
dc.publisher	Piscataway : IEEE - Inst Electrical Electronics Engineers Inc	de_DE
dc.relation.uri	http://dx.doi.org/10.1109/ACCESS.2025.3540261	de_DE
dc.subject.ddc	004	de_DE
dc.subject.ddc	600	de_DE
dc.title	Privacy-Preserving Machine Learning (PPML) Inference for Clinically Actionable Models	de_DE
dc.type	Article	de_DE
utue.quellen.id	20250630000000_02251
utue.publikation.seiten	37431-37456	de_DE
utue.personen.roh	Balaban, Baris
utue.personen.roh	Magara, Seyma Selcan
utue.personen.roh	Yilgor, Caglar
utue.personen.roh	Yucekul, Altug
utue.personen.roh	Obeid, Ibrahim
utue.personen.roh	Pizones, Javier
utue.personen.roh	Kleinstueck, Frank
utue.personen.roh	Perez-Grueso, Francisco Javier Sanchez
utue.personen.roh	Pellise, Ferran
utue.personen.roh	Alanay, Ahmet
utue.personen.roh	Savas, Erkay
utue.personen.roh	Bagci, Cetin
utue.personen.roh	Sezerman, Osman Ugur
utue.personen.roh	European Spine Study Group
dcterms.isPartOf.ZSTitelID	IEEE Access	de_DE
dcterms.isPartOf.ZS-Volume	13	de_DE
utue.fakultaet	07 Mathematisch-Naturwissenschaftliche Fakultät	de_DE