# Private Boosted Decision Trees via Smooth Re-Weighting

arXiv: https://arxiv.org/abs/2201.12648 · HTML: https://mlsanity.com/paper/2201.12648/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 39 references that are in the map.
- First posted on arXiv 29 Jan 2022.

## Builds on (most cited first, 6 of 6)

- [Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds](https://mlsanity.com/paper/1605.02065/) (2016)
- [Diffprivlib: The IBM Differential Privacy Library](https://mlsanity.com/paper/1907.02444/) (2019)
- [Interpretable and Differentially Private Predictions](https://mlsanity.com/paper/1906.02004/) (2019)
- [Privacy-Preserving Gradient Boosting Decision Trees](https://mlsanity.com/paper/1911.04209/) (2019)
- [Efficient, Noise-Tolerant, and Private Learning via Boosting](https://mlsanity.com/paper/2002.01100/) (2020)
- [Scalable and Provably Accurate Algorithms for Differentially Private Distributed Decision Tree Learning](https://mlsanity.com/paper/2012.10602/) (2020)

## Built on by (most cited first, 2 of 2)

- [Federated Boosted Decision Trees with Differential Privacy](https://mlsanity.com/paper/2210.02910/) (2022)
- [Differentially-Private Decision Trees and Provable Robustness to Data Poisoning](https://mlsanity.com/paper/2305.15394/) (2023)

## Abstract (arXiv)

Protecting the privacy of people whose data is used by machine learning algorithms is important. Differential Privacy is the appropriate mathematical framework for formal guarantees of privacy, and boosted decision trees are a popular machine learning technique. So we propose and test a practical algorithm for boosting decision trees that guarantees differential privacy. Privacy is enforced because our booster never puts too much weight on any one example; this ensures that each individual's data never influences a single tree "too much." Experiments show that this boosting algorithm can produce better model sparsity and accuracy than other differentially private ensemble classifiers.