FedKNN: Secure Federated k-Nearest Neighbor Search
Summary: FedKNN enables secure federated kNN with diverse similarity measures, tackling privacy-preserving computation for hard-to-compute distances (graph/sequence). It introduces DANN and DANN* (differentially oblivious) to minimize local work, offering privacy–efficiency trade-offs with up to 4.8x/2.7x gains on graph/sequence kNN. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Xinyi Zhang (Hong Kong Baptist University)
- 2. Qichen Wang (Hong Kong Baptist University)
- 3. Cheng Xu (Hong Kong Baptist University)
- 4. Yun Peng (Guangdong University of Technology)
- 5. Jianliang Xu (Hong Kong Baptist University)
BibTeX Citation
@inproceedings{zhang_sigmod24,
title = {{FedKNN: Secure Federated k-Nearest Neighbor Search}},
author = {Zhang, Xinyi and Wang, Qichen and Xu, Cheng and Peng, Yun and Xu, Jianliang},
series = {{SIGMOD} '24},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3639266},
url = {https://dl.acm.org/doi/10.1145/3639266},
year = {2024}
}Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,810 | FedSQ: A Secure System for Federated Vector Similarity Queries | 2024 | VLDB | 5.766208e-05 |
| 10,221 | Differentially Oblivious Multi-way Join | 2026 | SIGMOD | 5.093636e-05 |
| 10,817 | OpenFGL: A Comprehensive Benchmark for Federated Graph Learning | 2025 | VLDB | 5.093636e-05 |
| 11,046 | FedVSE: A Privacy-Preserving and Efficient Vector Search Engine for Federated Databases | 2025 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 9 of 9 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 926 | Improving Approximate Nearest Neighbor Search through Learned Adaptive Early Termination | 2020 | SIGMOD | 0.00013181732 |
| 1,115 | Comparing Stars: On Approximating Graph Edit Distance | 2009 | VLDB | 0.00012117375 |
| 2,045 | Efficient Oblivious Database Joins | 2020 | VLDB | 9.2632279e-05 |
| 2,417 | Towards Practical Oblivious Join | 2022 | SIGMOD | 8.6061949e-05 |
| 2,573 | Secure Yannakakis: Join-Aggregate Queries over Private Data | 2021 | SIGMOD | 8.4015654e-05 |
| 3,859 | Adore: Differentially Oblivious Relational Database Operators | 2023 | VLDB | 7.0670506e-05 |
| 5,714 | Hu-Fu: Efficient and Secure Spatial Queries over Data Federation | 2022 | VLDB | 6.1117296e-05 |
| 7,152 | Boosting Graph Similarity Search through Pre-Computation | 2021 | SIGMOD | 5.6872619e-05 |
| 7,165 | Flare: A Fast, Secure, and Memory-Efficient Distributed Analytics Framework | 2023 | VLDB | 5.6847858e-05 |
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