FedVSE: A Privacy-Preserving and Efficient Vector Search Engine for Federated Databases
Summary: FedVSE: SGX-backed privacy-preserving vector search for federated databases, using indexed pruning to scale high-dimensional KNN and hybrid queries. Enables rich cross-party queries with practical scalability; demo validates real-time cross-platform trajectory similarity search. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Zeheng Fan (Beihang University)
- 2. Yuxiang Zeng (Beihang University)
- 3. Zhuanglin Zheng (Beihang University)
- 4. Yongxin Tong (Beihang University)
BibTeX Citation
@article{fan_vldb25,
title = {{FedVSE: A Privacy-Preserving and Efficient Vector Search Engine for Federated Databases}},
author = {Fan, Zeheng and Zeng, Yuxiang and Zheng, Zhuanglin and Tong, Yongxin},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {12},
pages = {5371--5374},
doi = {10.14778/3750601.3750674},
url = {https://doi.org/10.14778/3750601.3750674},
year = {2025}
}Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,810 | V3DB: Audit-on-Demand Zero-Knowledge Proofs for Verifiable Vector Search over Committed Snapshots | 2026 | VLDB | 4.9793485e-05 |
| 10,952 | FedBridge: A Federated Query Engine over Embedding-Heterogeneous Vector Databases | 2026 | VLDB | 4.9793485e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 3 of 3 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,306 | FedKNN: Secure Federated k-Nearest Neighbor Search | 2024 | SIGMOD | 7.4422756e-05 |
| 4,883 | Hu-Fu: Efficient and Secure Spatial Queries over Data Federation | 2022 | VLDB | 6.3710505e-05 |
| 6,909 | FedSQ: A Secure System for Federated Vector Similarity Queries | 2024 | VLDB | 5.6490341e-05 |
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