ESecDT: Communication-Efficient and Secure Decision Tree Training Framework

Decision trees are widely used in various domains, such as user behavior analysis and financial risk assessment. Recently, increasing concerns about data privacy have driven the development of secure decision tree training frameworks. We propose ESecDT, a novel framework that enables collaborative decision tree training while preserving participants’ training data. ESecDT integrates the computational advantages of Function Secret Sharing (FSS) and Replicated Secret Sharing (RSS) through a co-design approach. We first introduce new protocols based on this co-design for tree training building blocks, including GroupSum, GroupPrefixSum, and VecMMax. Subsequently, we design a bit-width-aware training framework that manages data with different bit-widths and supports FSS key reuse. These designs enable ESecDT to ensure strong privacy guarantees, preserving the training data and all intermediate variables throughout the training process, while maintaining practical efficiency. Extensive experiments on nine real-world and synthetic datasets demonstrate that ESecDT reduces online communication overhead by $4.25\times $ – $5.56\times $ versus state-of-the-art frameworks. In the WAN setting, ESecDT achieves $1.78\times $ – $4.48\times $ speedup in training runtime and completes a 30,000-sample training task in less than half an hour, demonstrating strong potential for practical deployment.