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English(EN) Holo-NN: Holographic Nearest Neighbor Search via Compressive Sensing

Holo-NN:用于私有最近邻搜索的新代数框架

研究人员推出了一种新颖的代数框架 Holo-NN,用于高维嵌入中的隐私保护近似最近邻 (ANN) 搜索。该方法用单一的线性投影取代了传统的分支操作,而分支操作难以用全同态加密实现。通过将压缩感知与保持局部性的特征变换相结合,Holo-NN 将数据库折叠成一个紧凑的全息传感矩阵,从而能够通过单一的矩阵-向量乘法进行加密搜索。这种方法避免了复杂的密码学自举和多轮交互,使得私有检索成为一种高效、可并行化的计算,适用于 CPU 和 GPU。 AI

影响 引入了一种新颖的隐私保护搜索方法,可以增强处理敏感数据的 AI 应用的安全性。

排序理由 该集群包含一篇详细介绍一种新的最近邻搜索方法的 ist 研究论文。

在 arXiv cs.IR (Information Retrieval) 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

Holo-NN:用于私有最近邻搜索的新代数框架

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该集群包含一篇详细介绍一种新的最近邻搜索方法的 ist 研究论文。
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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Bryant Gipson ·

    Holo-NN:通过压缩感知实现全息最近邻搜索

    arXiv:2610.33224v1 Announce Type: cross Abstract: In the age of AI, privacy-preserving approximate nearest neighbor (ANN) search over high-dimensional embeddings has emerged as a critical yet frustratingly challenging domain, dominated by tradeoffs between speed, privacy, and acc…

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Bryant Gipson ·

    Holo-NN:通过压缩感知实现全息最近邻搜索

    In the age of AI, privacy-preserving approximate nearest neighbor (ANN) search over high-dimensional embeddings has emerged as a critical yet frustratingly challenging domain, dominated by tradeoffs between speed, privacy, and accuracy. Traditional tree and graph-based indexing s…