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: 7128
Venue: SIGMOD
Year: 2025
Pagerank: 5.093636e-05
Overall Rank: 10,678 | 26.74%
DOI: 10.1145/3709723

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Authors

1. Zongda Han (Beijing Institute of Technology)
2. Xiang Cheng (Beijing Institute of Technology)
3. Wenhong Zhao (Beijing Institute of Technology)
4. Jiaxin Fu (Beijing Institute of Technology)
5. Zhaofeng He (Beijing Institute of Technology)
6. Sen Su (Beijing Institute of Technology)

BibTeX Citation

@inproceedings{han_sigmod25,
        title = {{SecureXGB: A Secure and Efficient Multi-party Protocol for Vertical Federated XGBoost}},
        author = {Han, Zongda and Cheng, Xiang and Zhao, Wenhong and Fu, Jiaxin and He, Zhaofeng and Su, Sen},
        series = {{SIGMOD} '25},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3709723},
        url = {https://dl.acm.org/doi/10.1145/3709723},
        year = {2025}
}

Incoming Citations (Sorted by Pagerank)

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Rank	Citing Paper	Year	Venue	Pagerank

Outgoing Citations (Sorted by Pagerank)

Showing 3 of 3 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
1,249	Privacy Preserving Vertical Federated Learning for Tree-based Models	2020	VLDB	0.00011495357
1,959	VF2Boost: Very Fast Vertical Federated Gradient Boosting for Cross-Enterprise Learning	2021	SIGMOD	9.4090198e-05
5,645	OpBoost: A Vertical Federated Tree Boosting Framework Based on Order-Preserving Desensitization	2023	VLDB	6.1366186e-05

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