# Interpretable and Differentially Private Predictions

## TL;DR

This paper introduces a method for creating interpretable machine learning models that provide private explanations, balancing accuracy, interpretability, and privacy, especially for complex models on big data.

## Contribution

The authors propose a family of simple, locally linear models that approximate complex models and offer differentially private explanations, addressing the privacy-interpretability trade-off.

## Key findings

- Effective on image benchmark datasets
- Provides differentially private explanations
- Maintains high classification accuracy

## Abstract

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.