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FedSQ: A Secure System for Federated Vector Similarity Queries

Summary: FedSQ performs privacy-preserving federated vector similarity queries across multiple data owners via secure multi-party computation. Uniquely combines MPC with indexing and sampling optimizations to trade off efficiency and accuracy for high-dimensional embeddings. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
13678
Venue
VLDB
Year
2024
Pagerank
4.842703e-05
Overall Rank
7,072 | 50.81%
DOI
10.14778/3685800.3685895

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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
495 Milvus: A Purpose-Built Vector Data Management System 2021 SIGMOD 0.00021767688
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
9,050 Hu-Fu: A Data Federation System for Secure Spatial Queries 2022 VLDB 4.4039656e-05
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