# Ents: An Efficient Three-party Training Framework for Decision Trees by Communication Optimization

arXiv: https://arxiv.org/abs/2406.07948 · HTML: https://mlsanity.com/paper/2406.07948/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 54 references that are in the map.
- First posted on arXiv 12 Jun 2024.

## Builds on (most cited first, 10 of 10)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [CryptGPU: Fast Privacy-Preserving Machine Learning on the GPU](https://mlsanity.com/paper/2104.10949/) (2021)
- [SIRNN: A Math Library for Secure RNN Inference](https://mlsanity.com/paper/2105.04236/) (2021)
- [Privacy-Preserving Video Classification with Convolutional Neural Networks](https://mlsanity.com/paper/2102.03513/) (2021)
- [Private, Efficient, and Accurate: Protecting Models Trained by Multi-party Learning with Differential Privacy](https://mlsanity.com/paper/2208.08662/) (2022)
- [Privacy-Preserving Training of Tree Ensembles over Continuous Data](https://mlsanity.com/paper/2106.02769/) (2021)
- [Quantum homomorphic encryption for polynomial-sized circuits](https://mlsanity.com/paper/1603.09717/) (2016)
- [MPC-enabled Privacy-Preserving Neural Network Training against Malicious Attack](https://mlsanity.com/paper/2007.12557/) (2020)
- [Efficient decision tree training with new data structure for secure multi-party computation](https://mlsanity.com/paper/2112.12906/) (2021)
- [Bicoptor: Two-round Secure Three-party Non-linear Computation without Preprocessing for Privacy-preserving Machine Learning](https://mlsanity.com/paper/2210.01988/) (2022)

## Built on by (most cited first, 1 of 1)

- [Practical Anonymous Two-Party Gradient Boosting Decision Tree](https://mlsanity.com/paper/2605.26903/) (2026)

## Abstract (arXiv)

Multi-party training frameworks for decision trees based on secure multi-party computation enable multiple parties to train high-performance models on distributed private data with privacy preservation. The training process essentially involves frequent dataset splitting according to the splitting criterion (e.g. Gini impurity). However, existing multi-party training frameworks for decision trees demonstrate communication inefficiency due to the following issues: (1) They suffer from huge communication overhead in securely splitting a dataset with continuous attributes. (2) They suffer from huge communication overhead due to performing almost all the computations on a large ring to accommodate the secure computations for the splitting criterion. In this paper, we are motivated to present an efficient three-party training framework, namely Ents, for decision trees by communication optimization. For the first issue, we present a series of training protocols based on the secure radix sort protocols to efficiently and securely split a dataset with continuous attributes. For the second issue, we propose an efficient share conversion protocol to convert shares between a small ring and a large ring to reduce the communication overhead incurred by performing almost all the computations on a large ring. Experimental results from eight widely used datasets show that Ents outperforms state-of-the-art frameworks by 5.5× ~ 9.3× in communication sizes and 3.9× ~ 5.3× in communication rounds. In terms of training time, Ents yields an improvement of 3.5× ~ 6.7×. To demonstrate its practicality, Ents requires less than three hours to securely train a decision tree on a widely used real-world dataset (Skin Segmentation) with more than 245,000 samples in the WAN setting.