SecureXGB: A Secure and Efficient Multi-party Protocol for Vertical Federated XGBoost

Summary: SecureXGB: secure, efficient multi-party protocol for vertical federated XGBoost via secret sharing on partitioned data. Parallel permutation to conceal samples, division-free linear gain, and synchronous best-split selection to cut data-oblivious overhead. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 7066
Venue: SIGMOD
Year: 2025
Pagerank: 4.1945683e-05
Overall Rank: 10,391 | 27.72%
DOI: 10.1145/3709723

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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
1,143	Privacy Preserving Vertical Federated Learning for Tree-based Models	2020	VLDB	0.00013710269
1,895	VF2Boost: Very Fast Vertical Federated Gradient Boosting for Cross-Enterprise Learning	2021	SIGMOD	0.00010180896
5,507	OpBoost: A Vertical Federated Tree Boosting Framework Based on Order-Preserving Desensitization	2023	VLDB	5.4724291e-05

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