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English(EN) Efficient Privacy-Preserving Range Filtered Approximate Nearest Neighbor Search

新隐私保护RFANNS方法用于外包向量数据库

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种新颖的隐私保护范围过滤近似最近邻搜索（RFANNS）方法，专门用于外包向量数据库。这种新方法通过将范围定位与加密向量搜索分离，解决了保护敏感数据和查询免受云服务器侵害的关键需求。该系统将查询范围映射到本地属性树，允许服务器仅搜索相关的加密向量子索引，从而提高了与现有安全RFANNS改编相比的每秒查询召回率权衡。 AI

影响 增强了外包向量数据库的安全性，可能促进敏感数据分析的更广泛应用。

排序理由 详细介绍一种新的隐私保护搜索技术方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.IR (Information Retrieval) 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新隐私保护RFANNS方法用于外包向量数据库

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详细介绍一种新的隐私保护搜索技术方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Jiangtao Cui ·

    高效隐私保护的范围过滤近似最近邻搜索

    Range-filtered approximate nearest neighbor search (RFANNS) is an important primitive for vector databases; it retrieves vectors that are similar to a query and satisfy a numerical range predicate, but existing RFANNS indexes expose vectors, attributes, and queries in plaintext. …

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