论文标题
可扩展的多方隐私保护梯度树在垂直分区的数据集上使用外包计算提升
Scalable Multi-Party Privacy-Preserving Gradient Tree Boosting over Vertically Partitioned Dataset with Outsourced Computations
论文作者
论文摘要
由于隐私问题，多方梯度树的增强算法已在机器学习研究人员和从业人员中广泛流行。但是，有限的现有作品集中在垂直分区的数据集上，而现有的少数作品要么无法扩展或倾向于泄漏信息。因此，在这项工作中，我们提出了SSXGB，这是一个可扩展且安全的多方梯度促进树的增强框架，用于垂直分区的数据集，其中有部分外包的计算。具体而言，我们采用了安全性同态加密（HE）方案。我们根据HE方案设计了两个子协议，以执行与梯度树增强算法相关的非线性操作。接下来，我们建议在SSXGB框架下进行安全培训和安全的预测算法。然后，我们为拟议框架提供理论安全和通信分析。最后，我们使用两个现实世界数据集通过实验评估了框架的性能。
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.
