Mani Malek Esmaeili

Antipodes of Label Differential Privacy: PATE and ALIBI

We consider the privacy-preserving machine learning (ML) setting where the trained model must satisfy differential privacy (DP) with respect to the labels of the training examples. We propose two novel approaches based on, respectively, the Laplace mechanism and the PATE framework, and demonstrate t…