# Model Explanations with Differential Privacy

arXiv: https://arxiv.org/abs/2006.09129 · HTML: https://mlsanity.com/paper/2006.09129/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2020.

## Built on by (most cited first, 23 of 23)

- [The privacy issue of counterfactual explanations: explanation linkage attacks](https://mlsanity.com/paper/2210.12051/) (2022)
- [Inferring Sensitive Attributes from Model Explanations](https://mlsanity.com/paper/2208.09967/) (2022)
- [Differentially Private Counterfactuals via Functional Mechanism](https://mlsanity.com/paper/2208.02878/) (2022)
- [Privacy Meets Explainability: A Comprehensive Impact Benchmark](https://mlsanity.com/paper/2211.04110/) (2022)
- [When Differential Privacy Meets Interpretability: A Case Study](https://mlsanity.com/paper/2106.13203/) (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)
- [Trustworthy AI Must Account for Interactions](https://mlsanity.com/paper/2504.07170/) (2025)
- [SoK: Taming the Triangle -- On the Interplays between Fairness, Interpretability and Privacy in Machine Learning](https://mlsanity.com/paper/2312.16191/) (2023)
- [Regulating eXplainable Artificial Intelligence (XAI) May Harm Consumers](https://mlsanity.com/paper/2209.03499/) (2022)
- [Privacy Implications of Explainable AI in Data-Driven Systems](https://mlsanity.com/paper/2406.15789/) (2024)
- [Beware of "Explanations" of AI](https://mlsanity.com/paper/2504.06791/) (2025)
- [Achieving Transparency Report Privacy in Linear Time](https://mlsanity.com/paper/2104.00137/) (2021)
- [Combining Machine Learning Defenses without Conflicts](https://mlsanity.com/paper/2411.09776/) (2024)
- [Knowledge Distillation-Based Model Extraction Attack using GAN-based Private Counterfactual Explanations](https://mlsanity.com/paper/2404.03348/) (2024)
- [Contrastive Explainable Clustering with Differential Privacy](https://mlsanity.com/paper/2406.04610/) (2024)
- [Reconciling Privacy and Explainability in High-Stakes: A Systematic Inquiry](https://mlsanity.com/paper/2412.20798/) (2024)
- [Onboard Optimization and Learning: A Survey](https://mlsanity.com/paper/2505.08793/) (2025)
- [Towards integration of Privacy Enhancing Technologies in Explainable Artificial Intelligence](https://mlsanity.com/paper/2507.04528/) (2025)
- [DeepLeak: Privacy Enhancing Hardening of Model Explanations Against Membership Leakage](https://mlsanity.com/paper/2601.03429/) (2026)
- [Tensions Between the Proxies of Human Values in AI](https://mlsanity.com/paper/2212.07508/) (2022)
- [SoK: Privacy Attacks on Machine Learning via Explainable AI](https://mlsanity.com/paper/2609.10627/) (2026)

## Abstract (arXiv)

Black-box machine learning models are used in critical decision-making domains, giving rise to several calls for more algorithmic transparency. The drawback is that model explanations can leak information about the training data and the explanation data used to generate them, thus undermining data privacy. To address this issue, we propose differentially private algorithms to construct feature-based model explanations. We design an adaptive differentially private gradient descent algorithm, that finds the minimal privacy budget required to produce accurate explanations. It reduces the overall privacy loss on explanation data, by adaptively reusing past differentially private explanations. It also amplifies the privacy guarantees with respect to the training data. We evaluate the implications of differentially private models and our privacy mechanisms on the quality of model explanations.