Individualized PATE: Differentially Private Machine Learning with Individual Privacy Guarantees

Franziska Boenisch, Christopher Mühl, Roy Rinberg +2

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