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Hyperparameter Optimization for SecureBoost via Constrained Multi-Objective Federated Learning
April 9, 2024, 4:41 a.m. | Yan Kang, Ziyao Ren, Lixin Fan, Linghua Yang, Yongxin Tong, Qiang Yang
cs.LG updates on arXiv.org arxiv.org
Abstract: SecureBoost is a tree-boosting algorithm that leverages homomorphic encryption (HE) to protect data privacy in vertical federated learning. SecureBoost and its variants have been widely adopted in fields such as finance and healthcare. However, the hyperparameters of SecureBoost are typically configured heuristically for optimizing model performance (i.e., utility) solely, assuming that privacy is secured. Our study found that SecureBoost and some of its variants are still vulnerable to label leakage. This vulnerability may lead the …
abstract algorithm arxiv boosting cs.cr cs.lg data data privacy encryption federated learning fields finance healthcare homomorphic encryption however hyperparameter multi-objective optimization performance privacy protect tree type variants via
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