Scalable Multi-Party Privacy-Preserving Gradient Tree Boosting over Vertically Partitioned Dataset with Outsourced Computations

Kennedy Edemacu and Jong Wook Kim
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Kennedy Edemacu: Department of Computer Science and Electrical Engineering, Muni University, Arua P.O. Box 725, Uganda
Jong Wook Kim: Department of Computer Science, Sangmyung University, Seoul 03016, Korea

Mathematics, 2022, vol. 10, issue 13, 1-20

Abstract: Due to privacy concerns, multi-party gradient tree boosting algorithms have become widely popular amongst machine learning researchers and practitioners. However, limited existing works have focused on vertically partitioned datasets, and the few existing works are either not scalable or tend to leak information. Thus, in this work, we propose SSXGB, which is a scalable and acceptably secure multi-party gradient tree boosting framework for vertically partitioned datasets with partially outsourced computations. Specifically, we employ an additive homomorphic encryption (HE) scheme for security. We design two sub-protocols based on the HE scheme to perform non-linear operations associated with gradient tree boosting algorithms. Next, we propose secure training and prediction algorithms under the SSXGB framework. Then, we provide theoretical security and communication analysis for the proposed framework. Finally, we evaluate the performance of the framework with experiments using two real-world datasets.

Keywords: gradient tree boosting; multi-party machine learning; privacy-preservation; homomorphic encryption; vertically partitioned dataset (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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