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)
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Authors
- 1. Zeheng Fan
- 2. Yuxiang Zeng
- 3. Zhuanglin Zheng
- 4. Yongxin Tong
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,996 | FedKNN: Secure Federated k-Nearest Neighbor Search | 2024 | SIGMOD | 7.7586458e-05 |
| 5,746 | Hu-Fu: Efficient and Secure Spatial Queries over Data Federation | 2022 | VLDB | 5.3420687e-05 |
| 7,072 | FedSQ: A Secure System for Federated Vector Similarity Queries | 2024 | VLDB | 4.842703e-05 |
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