# Decision Tree Classification with Differential Privacy: A Survey

arXiv: https://arxiv.org/abs/1611.01919 · HTML: https://mlsanity.com/paper/1611.01919/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Nov 2016.

## Built on by (most cited first, 17 of 17)

- [Trustworthy AI: From Principles to Practices](https://mlsanity.com/paper/2110.01167/) (2021)
- [How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy](https://mlsanity.com/paper/2303.00654/) (2023)
- [On Tackling Explanation Redundancy in Decision Trees](https://mlsanity.com/paper/2205.09971/) (2022)
- [Accuracy, Interpretability, and Differential Privacy via Explainable Boosting](https://mlsanity.com/paper/2106.09680/) (2021)
- [Trained Random Forests Completely Reveal your Dataset](https://mlsanity.com/paper/2402.19232/) (2024)
- [Differentially Private Post-Processing for Fair Regression](https://mlsanity.com/paper/2405.04034/) (2024)
- [SoK: Privacy-Preserving Collaborative Tree-based Model Learning](https://mlsanity.com/paper/2103.08987/) (2021)
- [SoK: Taming the Triangle -- On the Interplays between Fairness, Interpretability and Privacy in Machine Learning](https://mlsanity.com/paper/2312.16191/) (2023)
- [Privacy Risks and Preservation Methods in Explainable Artificial Intelligence: A Scoping Review](https://mlsanity.com/paper/2505.02828/) (2025)
- [Differentially-Private Decision Trees and Provable Robustness to Data Poisoning](https://mlsanity.com/paper/2305.15394/) (2023)
- [Privacy-Preserving Federated Learning over Vertically and Horizontally Partitioned Data for Financial Anomaly Detection](https://mlsanity.com/paper/2310.19304/) (2023)
- [Privacy Constrained Fairness Estimation for Decision Trees](https://mlsanity.com/paper/2312.08413/) (2023)
- [The Conditioning Bias in Binary Decision Trees and Random Forests and Its Elimination](https://mlsanity.com/paper/2312.10708/) (2023)
- [Smooth Sensitivity for Learning Differentially-Private yet Accurate Rule Lists](https://mlsanity.com/paper/2403.13848/) (2024)
- [Training Set Reconstruction from Differentially Private Forests: How Effective is DP?](https://mlsanity.com/paper/2502.05307/) (2025)
- [Developing synthetic microdata through machine learning for firm-level business surveys](https://mlsanity.com/paper/2512.05948/) (2025)
- [SLDP: Semi-Local Differential Privacy for Density-Adaptive Analytics](https://mlsanity.com/paper/2602.18910/) (2026)

## Abstract (arXiv)

Data mining information about people is becoming increasingly important in the data-driven society of the 21st century. Unfortunately, sometimes there are real-world considerations that conflict with the goals of data mining; sometimes the privacy of the people being data mined needs to be considered. This necessitates that the output of data mining algorithms be modified to preserve privacy while simultaneously not ruining the predictive power of the outputted model. Differential privacy is a strong, enforceable definition of privacy that can be used in data mining algorithms, guaranteeing that nothing will be learned about the people in the data that could not already be discovered without their participation. In this survey, we focus on one particular data mining algorithm -- decision trees -- and how differential privacy interacts with each of the components that constitute decision tree algorithms. We analyze both greedy and random decision trees, and the conflicts that arise when trying to balance privacy requirements with the accuracy of the model.