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To bridge this gap, this paper introduces FedVSE, a privacy-preserving vector search engine for federated databases. FedVSE supports both KNN and hybrid queries, matching the versatility of modern vector databases. It leverages Intel SGX for hardware-enabled security and offers highly optimized query processing via indexing and pruning. Conference audiences can interact with FedVSE in real time and observe how it enables real-world services like cross-platform trajectory similarity search.<\/jats:p>","DOI":"10.14778\/3750601.3750674","type":"journal-article","created":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T13:38:05Z","timestamp":1758029885000},"page":"5371-5374","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["FedVSE: A Privacy-Preserving and Efficient Vector Search Engine for Federated Databases"],"prefix":"10.14778","volume":"18","author":[{"given":"Zeheng","family":"Fan","sequence":"first","affiliation":[{"name":"SKLCCSE Lab, Beihang University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuxiang","family":"Zeng","sequence":"additional","affiliation":[{"name":"SKLCCSE Lab, Beihang University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhuanglin","family":"Zheng","sequence":"additional","affiliation":[{"name":"SKLCCSE Lab, Beihang University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongxin","family":"Tong","sequence":"additional","affiliation":[{"name":"SKLCCSE Lab, Beihang University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,9,16]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"2024. gRPC. https:\/\/grpc.io\/"},{"key":"e_1_2_1_2_1","unstructured":"2024. 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