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: 6441
Venue: SIGMOD
Year: 2022
Pagerank: 7.0291192e-05
Overall Rank: 3,506 | 75.62%
DOI: 10.1145/3514221.3526127

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 10 of 10 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
6,459	Practical Differentially Private and Byzantine-resilient Federated Learning	2023	SIGMOD	5.0556005e-05
6,502	Falcon: A Privacy-Preserving and Interpretable Vertical Federated Learning System	2023	VLDB	5.0361846e-05
6,899	SecretFlow-SCQL: A Secure Collaborative Query pLatform	2024	VLDB	4.8925595e-05
7,487	Incentive-Aware Decentralized Data Collaboration	2023	SIGMOD	4.7180617e-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
10,686	PS-MI: Accurate, Efficient, and Private Data Valuation in Vertical Federated Learning	2025	VLDB	4.1945683e-05
10,716	Federated and Balanced Clustering for High-dimensional Data	2025	VLDB	4.1945683e-05
11,219	F3 KM: Federated, Fair, and Fast k-means	2023	SIGMOD	4.1945683e-05

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
1,143	Privacy Preserving Vertical Federated Learning for Tree-based Models	2020	VLDB	0.00013710269
1,895	VF2Boost: Very Fast Vertical Federated Gradient Boosting for Cross-Enterprise Learning	2021	SIGMOD	0.00010180896
2,677	HET: Scaling out Huge Embedding Model Training via Cache-enabled Distributed Framework	2022	VLDB	8.3268401e-05
3,808	SketchML: Accelerating Distributed Machine Learning with Data Sketches	2018	SIGMOD	6.7455428e-05
4,754	Differentially Private Binary- and Matrix-Valued Data Query: An XOR Mechanism	2021	VLDB	5.9468785e-05
4,975	An Experimental Evaluation of Large Scale GBDT Systems	2019	VLDB	5.79026e-05
5,333	Heterogeneity-Aware Distributed Machine Learning Training via Partial Reduce	2021	SIGMOD	5.5656575e-05
9,469	DimBoost: Boosting Gradient Boosting Decision Tree to Higher Dimensions	2018	SIGMOD	4.3342363e-05

Semantically Similar Papers

Overall Rank	Paper	Year	Venue	Pagerank
9,966	Towards Communication-efficient Vertical Federated Learning Training via Cache-enabled Local Updates	2022	VLDB	4.2269436e-05
4,805	Projected Federated Averaging with Heterogeneous Differential Privacy	2022	VLDB	5.9102798e-05
1,895	VF2Boost: Very Fast Vertical Federated Gradient Boosting for Cross-Enterprise Learning	2021	SIGMOD	0.00010180896
6,700	Differentially Private Vertical Federated Clustering	2023	VLDB	4.9563668e-05
8,459	Secure and Verifiable Data Collaboration with Low-Cost Zero-Knowledge Proofs	2024	VLDB	4.5065275e-05
5,775	Federated Matrix Factorization with Privacy Guarantee	2022	VLDB	5.3310992e-05
11,043	Uldp-FL: Federated Learning with Across-Silo User-Level Differential Privacy	2024	VLDB	4.1945683e-05
11,238	Olive: Oblivious Federated Learning on Trusted Execution Environment Against the Risk of Sparsification	2023	VLDB	4.1945683e-05
6,502	Falcon: A Privacy-Preserving and Interpretable Vertical Federated Learning System	2023	VLDB	5.0361846e-05
1,143	Privacy Preserving Vertical Federated Learning for Tree-based Models	2020	VLDB	0.00013710269