"Individualized PATE: Differentially Private Machine Learning with ..."

Franziska Boenisch et al. (2023)

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DOI: 10.56553/POPETS-2023-0010

access: open

type: Journal Article

metadata version: 2023-02-06

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Franziska Boenisch, Christopher Mühl, Roy Rinberg, Jannis Ihrig, Adam Dziedzic:
Individualized PATE: Differentially Private Machine Learning with Individual Privacy Guarantees. Proc. Priv. Enhancing Technol. 2023(1): 158-176 (2023)

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