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

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
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 a secure
training and a secure 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.
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
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 a secure
training and a secure 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.
Scalable Multi-Party Privacy-Preserving Gradient Tree Boosting over Vertically Partitioned Dataset with Outsourced Computations
http://arxiv.org/abs/2202.03245v1