Paper 2024/1077

Securely Training Decision Trees Efficiently

Abstract

Decision trees are an important class of supervised learning algorithms. When multiple entities contribute data to train a decision tree (e.g. for fraud detection in the financial sector), data privacy concerns necessitate the use of a privacy-enhancing technology such as secure multi-party computation (MPC) in order to secure the underlying training data. Prior state-of-the-art (Hamada et al.) construct an MPC protocol for decision tree training with a communication of $\mathcal{O}(hmN\log N)$, when building a decision tree of height $h$ for a training dataset of $N$ samples, each having $m$ attributes. In this work, we significantly reduce the communication complexity of secure decision tree training. We construct a protocol with communication complexity $\mathcal{O}(mN\log N + hmN + hN\log N)$, thereby achieving an improvement of $\approx \mathsf{min}(h, m, \log N)$ over Hamada et al. At the core of our technique is an improved protocol to regroup sorted private elements further into additional groups (according to a flag vector) while maintaining their relative ordering. We implement our protocol in the MP-SPDZ framework and show that it requires $10\times$ lesser communication and is $9\times$ faster than the state-of-the-art.