Approximate nearest neighbor (ANN) search, which retrieves vectors close to a given query vector, is a fundamental operation in vector databases. However, emerging applications, such as selecting source images for photo-based animation and video synthesis, may require matching content while allowing a query-specified a...
This is the first study that systematically formulates and evaluates privacy-preserving RFANNS over outsourced encrypted vector databases, and improves the QPS-Recall trade-off over representative secure adaptations of existing RFANNS approaches, scaling effectively to large datasets.
Haoyu Wang, Yandi Zhang, Jia-Dong Xie et al.· 0 citations
FROG is a GPU-oriented RFANNS index that replaces multiple locally optimal substructure building with a globally aware, vertex-centric design and organizes diverse expansion neighbor candidates for each vertex in a GPU-friendly structure and rapidly identifies the expansion neighbors used for computation at query time.
Xiao-Kun Cui, Peng Liu, Jia-Dong Xie et al.· 0 citations
Experiments show that KNNG-CS achieves accuracy comparable to representative gradient-approximation coreset methods, while reducing selection time by $2.3\times$-$41.2\times$ and peak memory to $0.3\%$-$7.5\%$ of the baselines.
Yingfan Liu, Lei-Yu Zhang, Jia-Dong Xie et al.· 1 citation
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