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We consider the privacy-preserving machine learning (ML) setting where thetrained model must satisfy differential privacy (DP) with respect to the labelsof the training examples. We propose two novel approaches based on,respectively, the Laplace mechanism and the PATE framework, and demonstratetheir effectiveness on standard benchmarks.  While recent work by Ghazi et al. proposed Label DP schemes based on arandomized response mechanism, we argue that additive Laplace noise coupledwith Bayesian inference (ALIBI) is a better fit for typical ML tasks. Moreover,we show how to achieve very strong privacy levels in some regimes, with ouradaptation of the PATE framework that builds on recent advances insemi-supervised learning.  We complement theoretical analysis of our algorithms' privacy guarantees withempirical evaluation of their memorization properties. Our evaluation suggeststhat comparing different algorithms according to their provable DP guaranteescan be misleading and favor a less private algorithm with a tighter analysis.  Code for implementation of algorithms and memorization attacks is availablefrom https://github.com/facebookresearch/label_dp_antipodes.

Antipodes of Label Differential Privacy: PATE and ALIBI