Efficient decision tree training with new data structure for secure multi-party computation

TL;DR

This paper introduces a new data structure for secure multi-party decision tree training that reduces the number of comparisons from exponential to linear in the tree height, improving efficiency and practicality.

Contribution

The authors develop a novel secure data structure that enables efficient decision tree training in MPC without dummy rows, reducing comparisons from exponential to linear in tree height.

Findings

Training a decision tree with height 5 takes 33 seconds on 100,000 rows.

The new protocol requires only O(hmn log n) comparisons, improving over previous exponential methods.

Implementation demonstrates practical feasibility for large datasets.

Abstract

We propose a secure multi-party computation (MPC) protocol that constructs a secret-shared decision tree for a given secret-shared dataset. The previous MPC-based decision tree training protocol (Abspoel et al. 2021) requires $O(2^hmn\log n)$ comparisons, being exponential in the tree height $h$ and with $n$ and $m$ being the number of rows and that of attributes in the dataset, respectively. The cause of the exponential number of comparisons in $h$ is that the decision tree training algorithm is based on the divide-and-conquer paradigm, where dummy rows are added after each split in order to hide the number of rows in the dataset. We resolve this issue via secure data structure that enables us to compute an aggregate value for every group while hiding the grouping information. By using this data structure, we can train a decision tree without adding dummy rows while hiding the size of the intermediate data. We specifically describes a decision tree training protocol that requires only $O(hmn\log n)$ comparisons when the input attributes are continuous and the output attribute is binary. Note that the order is now \emph{linear} in the tree height $h$. To demonstrate the practicality of our protocol, we implement it in an MPC framework based on a three-party secret sharing scheme. Our implementation results show that our protocol trains a decision tree with a height of 5 in 33 seconds for a dataset of 100,000 rows and 10 attributes.