SecureBoost: A Lossless Federated Learning Framework

TLDR: The SecureBoost framework is shown to be as accurate as other nonfederated gradient tree-boosting algorithms that require centralized data, and thus, it is highly scalable and practical for industrial applications such as credit risk analysis.

Abstract: The protection of user privacy is an important concern in machine learning, as evidenced by the rolling out of the General Data Protection Regulation (GDPR) in the European Union (EU) in May 2018 The GDPR is designed to give users more control over their personal data, which motivates us to explore machine learning frameworks for data sharing that do not violate user privacy To meet this goal, in this paper, we propose a novel lossless privacy-preserving tree-boosting system known as SecureBoost in the setting of federated learning This federated-learning system allows the learning process to be jointly conducted over multiple parties with partially common user samples but different feature sets, which corresponds to a vertically partitioned data set An advantage of SecureBoost is that it provides the same level of accuracy as the non privacy-preserving approach while at the same time, reveals no information of each private data provider We formally prove that the SecureBoost framework is as accurate as other non-federated gradient tree-boosting algorithms that concentrate data in one place In addition, we describe information leakage during the protocol execution and propose ways to provably reduce it