  Interpretable predictions, where it is clear why a machine learning model has
made a particular decision, can compromise privacy by revealing the
characteristics of individual data points. This raises the central question
addressed in this paper: Can models be interpretable without compromising
privacy? For complex big data fit by correspondingly rich models, balancing
privacy and explainability is particularly challenging, such that this question
has remained largely unexplored. In this paper, we propose a family of simple
models in the aim of approximating complex models using several locally linear
maps per class to provide high classification accuracy, as well as
differentially private explanations on the classification. We illustrate the
usefulness of our approach on several image benchmark datasets as well as a
medical dataset.
