PriXGB: Secure and accurate XGBoost training with function secret sharing for vertical and horizontal data partitioning

Abstract Extreme Gradient Boosting (XGBoost) is widely used due to its excellent learning performance. In real-world scenarios, data is often held by multiple owners, the sample size of a single data owner is usually insufficient for effectively training the model. Therefore, how to integrate data resources from different owners to collaboratively train a higher-performing model while ensuring data privacy has become an urgent problem that needs to be solved. To solve the above problems, we propose a privacy-preserving XGBoost scheme (PriXGB) with wide applicability and design a series of secure protocols for its key steps. We proposed a SecureArgmax protocol based on function secret sharing, which significantly improves the efficiency of securely computing the maximum value in the secret sequence while ensuring that intermediate information is not leaked. We designed a privacy sigmoid function protocol based on secret sharing to improve the accuracy of the model. In the vertical data partition scenario, PriXGB is roughly 30% more efficient than existing solutions. Finally, we provide security proofs for designed protocols and verify the advantages of the scheme through experiments. The experimental results show that PriXGB effectively protects data privacy while maintaining accuracy comparable to non-private XGBoost, thereby demonstrating strong security and effectiveness.