# Interpretable and Differentially Private Predictions

arXiv: https://arxiv.org/abs/1906.02004 · HTML: https://mlsanity.com/paper/1906.02004/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2019.

## Built on by (most cited first, 28 of 28)

- [Towards Personalized Federated Learning](https://mlsanity.com/paper/2103.00710/) (2021)
- [From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI](https://mlsanity.com/paper/2201.08164/) (2022)
- [Perturbation-Restrained Sequential Model Editing](https://mlsanity.com/paper/2405.16821/) (2024)
- [Inferring Sensitive Attributes from Model Explanations](https://mlsanity.com/paper/2208.09967/) (2022)
- [Recent Advances of Differential Privacy in Centralized Deep Learning: A Systematic Survey](https://mlsanity.com/paper/2309.16398/) (2023)
- [A Survey on Explainable Artificial Intelligence for Cybersecurity](https://mlsanity.com/paper/2303.12942/) (2023)
- [Attack-agnostic Adversarial Detection on Medical Data Using Explainable Machine Learning](https://mlsanity.com/paper/2105.01959/) (2021)
- [SoK: Modeling Explainability in Security Analytics for Interpretability, Trustworthiness, and Usability](https://mlsanity.com/paper/2210.17376/) (2022)
- [Balancing Privacy Protection and Interpretability in Federated Learning](https://mlsanity.com/paper/2302.08044/) (2023)
- [SoK: Unintended Interactions among Machine Learning Defenses and Risks](https://mlsanity.com/paper/2312.04542/) (2023)
- [A Survey of Privacy-Preserving Model Explanations: Privacy Risks, Attacks, and Countermeasures](https://mlsanity.com/paper/2404.00673/) (2024)
- [Privacy-Preserving Algorithmic Recourse](https://mlsanity.com/paper/2311.14137/) (2023)
- [Mathematical Foundation of Interpretable Equivariant Surrogate Models](https://mlsanity.com/paper/2503.01942/) (2025)
- [A Differentially Private Framework for Deep Learning with Convexified Loss Functions](https://mlsanity.com/paper/2204.01049/) (2022)
- [SoK: Taming the Triangle -- On the Interplays between Fairness, Interpretability and Privacy in Machine Learning](https://mlsanity.com/paper/2312.16191/) (2023)
- [The Need for Interpretable Features: Motivation and Taxonomy](https://mlsanity.com/paper/2202.11748/) (2022)
- [Private Boosted Decision Trees via Smooth Re-Weighting](https://mlsanity.com/paper/2201.12648/) (2022)
- [Spending Privacy Budget Fairly and Wisely](https://mlsanity.com/paper/2204.12903/) (2022)
- [MLHOps: Machine Learning for Healthcare Operations](https://mlsanity.com/paper/2305.02474/) (2023)
- [Differential Privacy for Anomaly Detection: Analyzing the Trade-off Between Privacy and Explainability](https://mlsanity.com/paper/2404.06144/) (2024)
- [GLEAMS: Bridging the Gap Between Local and Global Explanations](https://mlsanity.com/paper/2408.05060/) (2024)
- [Privacy Risks and Preservation Methods in Explainable Artificial Intelligence: A Scoping Review](https://mlsanity.com/paper/2505.02828/) (2025)
- [Reconciling Privacy and Explainability in High-Stakes: A Systematic Inquiry](https://mlsanity.com/paper/2412.20798/) (2024)
- [Towards integration of Privacy Enhancing Technologies in Explainable Artificial Intelligence](https://mlsanity.com/paper/2507.04528/) (2025)
- [Towards the Formalization of a Trustworthy AI for Mining Interpretable Models explOiting Sophisticated Algorithms](https://mlsanity.com/paper/2510.20621/) (2025)
- [Privacy-Preserving Explainable AIoT Application via SHAP Entropy Regularization](https://mlsanity.com/paper/2511.09775/) (2025)
- [Tensions Between the Proxies of Human Values in AI](https://mlsanity.com/paper/2212.07508/) (2022)
- [Where Privacy Belongs: Placement Diagnosis and Certified Selection for Private Counterfactual Explanations on Graphs](https://mlsanity.com/paper/2609.37667/) (2026)

## Abstract (arXiv)

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.