Interpretable and Differentially Private Predictions

Authors

  • Frederik Harder University of Tuebingen
  • Matthias Bauer Max Planck Institute for Intelligent Systems
  • Mijung Park University of Tuebingen

DOI:

https://doi.org/10.1609/aaai.v34i04.5827

Abstract

Interpretable predictions, which clarify why a machine learning model makes 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 with 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.

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Published

2020-04-03

How to Cite

Harder, F., Bauer, M., & Park, M. (2020). Interpretable and Differentially Private Predictions. Proceedings of the AAAI Conference on Artificial Intelligence, 34(04), 4083-4090. https://doi.org/10.1609/aaai.v34i04.5827

Issue

Section

AAAI Technical Track: Machine Learning