Hu-Fu: Efficient and Secure Spatial Queries over Data Federation
Summary: Hu-Fu: first efficient secure spatial query system for data federations, pushing most work to plaintext and using few dedicated secure operators. It supports SQL and heterogeneous backends; experiments show improved runtime and reduced communication. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Yongxin Tong
- 2. Xuchen Pan
- 3. Yuxiang Zeng
- 4. Yexuan Shi
- 5. Chunbo Xue
- 6. Zimu Zhou
- 7. Xiaofei Zhang
- 8. Lei Chen
- 9. Yi Xu
- 10. Ke Xu
- 11. Weifeng Lv
Incoming Citations (Sorted by Pagerank)
Showing 7 of 7 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,996 | FedKNN: Secure Federated k-Nearest Neighbor Search | 2024 | SIGMOD | 7.7586458e-05 |
| 4,863 | Data-Sharing Markets: Model, Protocol, and Algorithms to Incentivize the Formation of Data-Sharing Consortia | 2023 | SIGMOD | 5.8697471e-05 |
| 7,072 | FedSQ: A Secure System for Federated Vector Similarity Queries | 2024 | VLDB | 4.842703e-05 |
| 9,050 | Hu-Fu: A Data Federation System for Secure Spatial Queries | 2022 | VLDB | 4.4039656e-05 |
| 10,231 | Bifrost: A Much Simpler Secure Two-Party Data Join Protocol for Secure Data Analytics | 2026 | VLDB | 4.1945683e-05 |
| 10,399 | U-DPAP: Utility-aware Efficient Range Counting on Privacy-preserving Spatial Data Federation | 2025 | SIGMOD | 4.1945683e-05 |
| 10,819 | FedVSE: A Privacy-Preserving and Efficient Vector Search Engine for Federated Databases | 2025 | VLDB | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 13 of 13 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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