Title: SecureBoost: A Lossless Federated Learning Framework,
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 with data sharing without violating 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 a learning process to
be jointly conducted over multiple parties with partially common user samples
but different feature sets, which corresponds to a vertically partitioned
virtual 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, reveal no information of each private data provider. We theoretically
prove that the SecureBoost framework is as accurate as other non-federated
gradient tree-boosting algorithms that bring the data into one place. In
addition, along with a proof of security, we discuss what would be required to
make the protocols completely secure.