Private model utility by minimizing expected loss under noise

Training of a model is performed to minimize expected loss under noise (ELUN) while maintaining differential privacy. Noise is added to weights of a machine learning model as random samples drawn from a noise distribution, the noise being added in accordance with a privacy budget. The ELUN is minimi...

Ausführliche Beschreibung

Bibliographische Detailangaben

Hauptverfasser:	Leino, Klas, Guajardo Merchan, Jorge
Format:	Patent
Sprache:	eng
Schlagworte:	CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING ELECTRIC DIGITAL DATA PROCESSING PHYSICS
Online-Zugang:	Volltext bestellen
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!