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BlindFL: Vertical Federated Machine Learning without Peeking into Your Data

Summary: BlindFL introduces federated source layers to securely unite heterogeneous features across parties in vertical FL. It delivers secure, accurate training and inference with formal privacy guarantees under ideal–real simulation. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6503
Venue
SIGMOD
Year
2022
Pagerank
7.6089416e-05
Overall Rank
3,237 | 77.80%
DOI
10.1145/3514221.3526127

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{fu_sigmod22,
        title = {{BlindFL: Vertical Federated Machine Learning without Peeking into Your Data}},
        author = {Fu, Fangcheng and Xue, Huanran and Cheng, Yong and Tao, Yangyu and Cui, Bin},
        series = {{SIGMOD} '22},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3514221.3526127},
        url = {https://dl.acm.org/doi/10.1145/3514221.3526127},
        year = {2022}
}

Incoming Citations (Sorted by Pagerank)

Showing 10 of 10 citing papers.

Rank Citing Paper Year Venue Pagerank
6,366 Practical Differentially Private and Byzantine-resilient Federated Learning 2023 SIGMOD 5.8983442e-05
6,421 Falcon: A Privacy-Preserving and Interpretable Vertical Federated Learning System 2023 VLDB 5.8819368e-05
7,504 SecretFlow-SCQL: A Secure Collaborative Query pLatform 2024 VLDB 5.6029996e-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
10,934 PS-MI: Accurate, Efficient, and Private Data Valuation in Vertical Federated Learning 2025 VLDB 5.093636e-05
10,959 Federated and Balanced Clustering for High-dimensional Data 2025 VLDB 5.093636e-05
11,420 F3 KM: Federated, Fair, and Fast k-means 2023 SIGMOD 5.093636e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 8 of 8 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
2,485 HET: Scaling out Huge Embedding Model Training via Cache-enabled Distributed Framework 2022 VLDB 8.5145736e-05
4,083 SketchML: Accelerating Distributed Machine Learning with Data Sketches 2018 SIGMOD 6.9160949e-05
4,956 Heterogeneity-Aware Distributed Machine Learning Training via Partial Reduce 2021 SIGMOD 6.4290135e-05
5,589 An Experimental Evaluation of Large Scale GBDT Systems 2019 VLDB 6.1559057e-05
5,908 Differentially Private Binary- and Matrix-Valued Data Query: An XOR Mechanism 2021 VLDB 6.0447537e-05
9,672 DimBoost: Boosting Gradient Boosting Decision Tree to Higher Dimensions 2018 SIGMOD 5.2386185e-05
Previous Page 1 / 1 Next

Semantically Similar Papers

# Overall Rank Paper Year Venue
1 10,114 Towards Communication-efficient Vertical Federated Learning Training via Cache-enabled Local Updates 2022 VLDB
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5 8,456 Secure and Verifiable Data Collaboration with Low-Cost Zero-Knowledge Proofs 2024 VLDB
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7 11,252 Uldp-FL: Federated Learning with Across-Silo User-Level Differential Privacy 2024 VLDB
8 11,438 Olive: Oblivious Federated Learning on Trusted Execution Environment Against the Risk of Sparsification 2023 VLDB
9 6,421 Falcon: A Privacy-Preserving and Interpretable Vertical Federated Learning System 2023 VLDB
10 1,249 Privacy Preserving Vertical Federated Learning for Tree-based Models 2020 VLDB