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Privacy Preserving Vertical Federated Learning for Tree-based Models

Summary: Pivot: privacy-preserving vertical FL for tree models with disjoint feature owners and one label holder, no TTP, secure against semi-honest m-1. Also mitigates plaintext leakage; extends to RF/GBDT by composing trees; results show efficiency. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
12101
Venue
VLDB
Year
2020
Pagerank
0.00013710269
Overall Rank
1,143 | 92.06%
DOI
10.14778/3407790.3407811

Incoming Non-self Citations Over Time

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Incoming Citations (Sorted by Pagerank)

Showing 19 of 19 citing papers.

Rank Citing Paper Year Venue Pagerank
1,895 VF2Boost: Very Fast Vertical Federated Gradient Boosting for Cross-Enterprise Learning 2021 SIGMOD 0.00010180896
3,506 BlindFL: Vertical Federated Machine Learning without Peeking into Your Data 2022 SIGMOD 7.0291192e-05
4,290 FedTSC: A Secure Federated Learning System for Interpretable Time Series Classification 2022 VLDB 6.2885419e-05
5,222 Enabling SQL-based Training Data Debugging for Federated Learning 2022 VLDB 5.6210545e-05
5,507 OpBoost: A Vertical Federated Tree Boosting Framework Based on Order-Preserving Desensitization 2023 VLDB 5.4724291e-05
5,775 Federated Matrix Factorization with Privacy Guarantee 2022 VLDB 5.3310992e-05
6,502 Falcon: A Privacy-Preserving and Interpretable Vertical Federated Learning System 2023 VLDB 5.0361846e-05
6,700 Differentially Private Vertical Federated Clustering 2023 VLDB 4.9563668e-05
7,487 Incentive-Aware Decentralized Data Collaboration 2023 SIGMOD 4.7180617e-05
7,704 ExDRa: Exploratory Data Science on Federated Raw Data 2021 SIGMOD 4.6733838e-05
8,459 Secure and Verifiable Data Collaboration with Low-Cost Zero-Knowledge Proofs 2024 VLDB 4.5065275e-05
9,323 FEAST: A Communication-efficient Federated Feature Selection Framework for Relational Data 2023 SIGMOD 4.3556432e-05
9,966 Towards Communication-efficient Vertical Federated Learning Training via Cache-enabled Local Updates 2022 VLDB 4.2269436e-05
10,101 Privacy-preserving and Verifiable Causal Prescriptive Analytics 2026 SIGMOD 4.1945683e-05
10,231 Bifrost: A Much Simpler Secure Two-Party Data Join Protocol for Secure Data Analytics 2026 VLDB 4.1945683e-05
10,391 SecureXGB: A Secure and Efficient Multi-party Protocol for Vertical Federated XGBoost 2025 SIGMOD 4.1945683e-05
10,684 Federated Incomplete Tabular Data Prediction with Missing Complementarity 2025 VLDB 4.1945683e-05
10,686 PS-MI: Accurate, Efficient, and Private Data Valuation in Vertical Federated Learning 2025 VLDB 4.1945683e-05
11,003 Performance-Based Pricing for Federated Learning via Auction 2024 VLDB 4.1945683e-05
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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
40 Privacy-Preserving Data Mining 2000 SIGMOD 0.00074232718
178 Boosting the Accuracy of Differentially Private Histograms Through Consistency 2010 VLDB 0.00037697111
2,806 CryptEpsilon: Crypto-Assisted Differential Privacy on Untrusted Servers 2020 SIGMOD 8.0911177e-05
2,873 TitAnt: Online Real-time Transaction Fraud Detection in Ant Financial 2019 VLDB 7.9754477e-05
4,975 An Experimental Evaluation of Large Scale GBDT Systems 2019 VLDB 5.79026e-05
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Overall Rank Paper Year Venue Pagerank
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7,489 DeltaBoost: Gradient Boosting Decision Trees with Efficient Machine Unlearning 2023 SIGMOD 4.7180617e-05
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5,775 Federated Matrix Factorization with Privacy Guarantee 2022 VLDB 5.3310992e-05
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3,506 BlindFL: Vertical Federated Machine Learning without Peeking into Your Data 2022 SIGMOD 7.0291192e-05
5,507 OpBoost: A Vertical Federated Tree Boosting Framework Based on Order-Preserving Desensitization 2023 VLDB 5.4724291e-05
1,895 VF2Boost: Very Fast Vertical Federated Gradient Boosting for Cross-Enterprise Learning 2021 SIGMOD 0.00010180896