Privacy Preserving Vertical Federated Learning for Tree-based Models

Summary: Pivot enables privacy-preserving vertical decision-tree training and inference with disjoint features and labels held by one party, without a trusted third party—even against m−1 semi-honest clients. It mitigates plaintext-model leakage and extends to RF/GBDT. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID: h819b6e0e7fd9e061
Venue: VLDB
Year: 2020
Pagerank: 0.00011195435
Overall Rank: 1,284 | 91.40%
DOI: 10.14778/3407790.3407811
PDF: Download (CC BY-NC-ND 4.0)

Incoming Non-self Citations Over Time

Authors

1. Yuncheng Wu (National University of Singapore)
2. Shaofeng Cai (National University of Singapore)
3. Xiaokui Xiao (National University of Singapore)
4. Gang Chen (Zhejiang University)
5. Beng Chin Ooi (National University of Singapore)

BibTeX Citation

@article{wu_vldb20,
        title = {{Privacy Preserving Vertical Federated Learning for Tree-based Models}},
        author = {Wu, Yuncheng and Cai, Shaofeng and Xiao, Xiaokui and Chen, Gang and Ooi, Beng Chin},
        journal = {PVLDB},
        series = {{VLDB} '20},
        volume = {13},
        number = {11},
        pages = {2090--2103},
        doi = {10.14778/3407790.3407811},
        url = {https://doi.org/10.14778/3407790.3407811},
        year = {2020}
}

Incoming Citations (Sorted by Pagerank)

Showing 19 of 19 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
2,020	VF2Boost: Very Fast Vertical Federated Gradient Boosting for Cross-Enterprise Learning	2021	SIGMOD	9.1641863e-05
3,324	BlindFL: Vertical Federated Machine Learning without Peeking into Your Data	2022	SIGMOD	7.4111459e-05
5,273	Enabling SQL-based Training Data Debugging for Federated Learning	2022	VLDB	6.1887043e-05
5,736	Federated Matrix Factorization with Privacy Guarantee	2022	VLDB	5.995594e-05
5,789	OpBoost: A Vertical Federated Tree Boosting Framework Based on Order-Preserving Desensitization	2023	VLDB	5.9762627e-05
6,354	FedTSC: A Secure Federated Learning System for Interpretable Time Series Classification	2022	VLDB	5.78729e-05
6,486	Differentially Private Vertical Federated Clustering	2023	VLDB	5.7522634e-05
6,566	Falcon: A Privacy-Preserving and Interpretable Vertical Federated Learning System	2023	VLDB	5.728236e-05
7,860	ExDRa: Exploratory Data Science on Federated Raw Data	2021	SIGMOD	5.422643e-05
8,104	Incentive-Aware Decentralized Data Collaboration	2023	SIGMOD	5.3739121e-05
8,569	Secure and Verifiable Data Collaboration with Low-Cost Zero-Knowledge Proofs	2024	VLDB	5.2991861e-05
9,689	FEAST: A Communication-efficient Federated Feature Selection Framework for Relational Data	2023	SIGMOD	5.1258854e-05
10,376	Towards Communication-efficient Vertical Federated Learning Training via Cache-enabled Local Updates	2022	VLDB	4.9979062e-05
10,660	Privacy-preserving and Verifiable Causal Prescriptive Analytics	2026	SIGMOD	4.9605343e-05
10,775	Bifrost: A Much Simpler Secure Two-Party Data Join Protocol for Secure Data Analytics	2026	VLDB	4.9605343e-05
11,187	SecureXGB: A Secure and Efficient Multi-party Protocol for Vertical Federated XGBoost	2025	SIGMOD	4.9605343e-05
11,384	Federated Incomplete Tabular Data Prediction with Missing Complementarity	2025	VLDB	4.9605343e-05
11,386	PS-MI: Accurate, Efficient, and Private Data Valuation in Vertical Federated Learning	2025	VLDB	4.9605343e-05
11,607	Performance-Based Pricing for Federated Learning via Auction	2024	VLDB	4.9605343e-05

Outgoing Citations (Sorted by Pagerank)

Showing 5 of 5 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
69	Privacy-Preserving Data Mining	2000	SIGMOD	0.00037815735
121	Boosting the Accuracy of Differentially Private Histograms Through Consistency	2010	VLDB	0.00030972849
2,877	CryptEpsilon: Crypto-Assisted Differential Privacy on Untrusted Servers	2020	SIGMOD	7.8955405e-05
3,082	TitAnt: Online Real-time Transaction Fraud Detection in Ant Financial	2019	VLDB	7.6507218e-05
5,738	An Experimental Evaluation of Large Scale GBDT Systems	2019	VLDB	5.9951855e-05

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#	Overall Rank	Paper	Year	Venue
1	11,187	SecureXGB: A Secure and Efficient Multi-party Protocol for Vertical Federated XGBoost	2025	SIGMOD
2	6,566	Falcon: A Privacy-Preserving and Interpretable Vertical Federated Learning System	2023	VLDB
3	7,546	DeltaBoost: Gradient Boosting Decision Trees with Efficient Machine Unlearning	2023	SIGMOD
4	4,558	Projected Federated Averaging with Heterogeneous Differential Privacy	2022	VLDB
5	11,801	Olive: Oblivious Federated Learning on Trusted Execution Environment Against the Risk of Sparsification	2023	VLDB
6	5,736	Federated Matrix Factorization with Privacy Guarantee	2022	VLDB
7	6,486	Differentially Private Vertical Federated Clustering	2023	VLDB
8	3,324	BlindFL: Vertical Federated Machine Learning without Peeking into Your Data	2022	SIGMOD
9	5,789	OpBoost: A Vertical Federated Tree Boosting Framework Based on Order-Preserving Desensitization	2023	VLDB
10	2,020	VF2Boost: Very Fast Vertical Federated Gradient Boosting for Cross-Enterprise Learning	2021	SIGMOD