Individualized PATE: Differentially Private Machine Learning with Individual Privacy Guarantees

no code implementations • 21 Feb 2022 • Franziska Boenisch, Christopher Mühl, Roy Rinberg, Jannis Ihrig, Adam Dziedzic

Applying machine learning (ML) to sensitive domains requires privacy protection of the underlying training data through formal privacy frameworks, such as differential privacy (DP).

BIG-bench Machine Learning