# Accurate, Explainable, and Private Models: Providing Recourse While Minimizing Training Data Leakage

arXiv: https://arxiv.org/abs/2308.04341 · HTML: https://mlsanity.com/paper/2308.04341/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 16 references that are in the map.
- First posted on arXiv 8 Aug 2023.

## Builds on (most cited first, 8 of 8)

- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR](https://mlsanity.com/paper/1711.00399/) (2017)
- [Membership Inference Attacks From First Principles](https://mlsanity.com/paper/2112.03570/) (2021)
- [Model Reconstruction from Model Explanations](https://mlsanity.com/paper/1807.05185/) (2018)
- [Diffprivlib: The IBM Differential Privacy Library](https://mlsanity.com/paper/1907.02444/) (2019)
- [Model extraction from counterfactual explanations](https://mlsanity.com/paper/2009.01884/) (2020)
- [On the Privacy Risks of Algorithmic Recourse](https://mlsanity.com/paper/2211.05427/) (2022)
- [Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting](https://mlsanity.com/paper/1709.01604/) (2017)

## Built on by (most cited first, 5 of 5)

- [A Survey of Privacy-Preserving Model Explanations: Privacy Risks, Attacks, and Countermeasures](https://mlsanity.com/paper/2404.00673/) (2024)
- [Knowledge Distillation-Based Model Extraction Attack using GAN-based Private Counterfactual Explanations](https://mlsanity.com/paper/2404.03348/) (2024)
- [Explaining the Model, Protecting Your Data: Revealing and Mitigating the Data Privacy Risks of Post-Hoc Model Explanations via Membership Inference](https://mlsanity.com/paper/2407.17663/) (2024)
- [Privacy-Preserving Explainable AIoT Application via SHAP Entropy Regularization](https://mlsanity.com/paper/2511.09775/) (2025)
- [Quantifying the Privacy of Counterfactuals by Leveraging Membership Inference Attacks Against Synthetic Data](https://mlsanity.com/paper/2606.06334/) (2026)

## Abstract (arXiv)

Machine learning models are increasingly utilized across impactful domains to predict individual outcomes. As such, many models provide algorithmic recourse to individuals who receive negative outcomes. However, recourse can be leveraged by adversaries to disclose private information. This work presents the first attempt at mitigating such attacks. We present two novel methods to generate differentially private recourse: Differentially Private Model (DPM) and Laplace Recourse (LR). Using logistic regression classifiers and real world and synthetic datasets, we find that DPM and LR perform well in reducing what an adversary can infer, especially at low FPR. When training dataset size is large enough, we find particular success in preventing privacy leakage while maintaining model and recourse accuracy with our novel LR method.