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: 12289
Venue: VLDB
Year: 2020
Pagerank: 0.00011495357
Overall Rank: 1,249 | 91.44%
DOI: 10.14778/3407790.3407811

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
1,959	VF2Boost: Very Fast Vertical Federated Gradient Boosting for Cross-Enterprise Learning	2021	SIGMOD	9.4090198e-05
3,237	BlindFL: Vertical Federated Machine Learning without Peeking into Your Data	2022	SIGMOD	7.6089416e-05
5,131	Enabling SQL-based Training Data Debugging for Federated Learning	2022	VLDB	6.3537809e-05
5,588	Federated Matrix Factorization with Privacy Guarantee	2022	VLDB	6.1564686e-05
5,645	OpBoost: A Vertical Federated Tree Boosting Framework Based on Order-Preserving Desensitization	2023	VLDB	6.1366186e-05
6,212	FedTSC: A Secure Federated Learning System for Interpretable Time Series Classification	2022	VLDB	5.9425753e-05
6,353	Differentially Private Vertical Federated Clustering	2023	VLDB	5.9042628e-05
6,421	Falcon: A Privacy-Preserving and Interpretable Vertical Federated Learning System	2023	VLDB	5.8819368e-05
7,687	ExDRa: Exploratory Data Science on Federated Raw Data	2021	SIGMOD	5.5671645e-05
7,911	Incentive-Aware Decentralized Data Collaboration	2023	SIGMOD	5.5181056e-05
8,456	Secure and Verifiable Data Collaboration with Low-Cost Zero-Knowledge Proofs	2024	VLDB	5.4217837e-05
9,473	FEAST: A Communication-efficient Federated Feature Selection Framework for Relational Data	2023	SIGMOD	5.2634238e-05
10,114	Towards Communication-efficient Vertical Federated Learning Training via Cache-enabled Local Updates	2022	VLDB	5.1319012e-05
10,391	Privacy-preserving and Verifiable Causal Prescriptive Analytics	2026	SIGMOD	5.093636e-05
10,519	Bifrost: A Much Simpler Secure Two-Party Data Join Protocol for Secure Data Analytics	2026	VLDB	5.093636e-05
10,678	SecureXGB: A Secure and Efficient Multi-party Protocol for Vertical Federated XGBoost	2025	SIGMOD	5.093636e-05
10,932	Federated Incomplete Tabular Data Prediction with Missing Complementarity	2025	VLDB	5.093636e-05
10,934	PS-MI: Accurate, Efficient, and Private Data Valuation in Vertical Federated Learning	2025	VLDB	5.093636e-05
11,214	Performance-Based Pricing for Federated Learning via Auction	2024	VLDB	5.093636e-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
70	Privacy-Preserving Data Mining	2000	SIGMOD	0.0003804755
121	Boosting the Accuracy of Differentially Private Histograms Through Consistency	2010	VLDB	0.00031639377
2,818	CryptEpsilon: Crypto-Assisted Differential Privacy on Untrusted Servers	2020	SIGMOD	8.0932359e-05
3,203	TitAnt: Online Real-time Transaction Fraud Detection in Ant Financial	2019	VLDB	7.6402128e-05
5,589	An Experimental Evaluation of Large Scale GBDT Systems	2019	VLDB	6.1559057e-05

Semantically Similar Papers

#	Overall Rank	Paper	Year	Venue
1	10,678	SecureXGB: A Secure and Efficient Multi-party Protocol for Vertical Federated XGBoost	2025	SIGMOD
2	6,421	Falcon: A Privacy-Preserving and Interpretable Vertical Federated Learning System	2023	VLDB
3	7,381	DeltaBoost: Gradient Boosting Decision Trees with Efficient Machine Unlearning	2023	SIGMOD
4	4,446	Projected Federated Averaging with Heterogeneous Differential Privacy	2022	VLDB
5	11,438	Olive: Oblivious Federated Learning on Trusted Execution Environment Against the Risk of Sparsification	2023	VLDB
6	5,588	Federated Matrix Factorization with Privacy Guarantee	2022	VLDB
7	6,353	Differentially Private Vertical Federated Clustering	2023	VLDB
8	3,237	BlindFL: Vertical Federated Machine Learning without Peeking into Your Data	2022	SIGMOD
9	5,645	OpBoost: A Vertical Federated Tree Boosting Framework Based on Order-Preserving Desensitization	2023	VLDB
10	1,959	VF2Boost: Very Fast Vertical Federated Gradient Boosting for Cross-Enterprise Learning	2021	SIGMOD