单侧假设下的高效模糊私有集合交集
Efficient Fuzzy PSI under One-Sided Assumptions
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中文总结 AI 辅助
本研究提出单侧假设下一般Lₚ距离的高效模糊PSI协议，结合前缀树技术实现O(logδ)复杂度，实验显示其计算速度与通信量较现有工作有显著提升。
中文摘要 AI 辅助
模糊私有集合交集（Fuzzy PSI）使双方能够识别各自输入集合中近似匹配的元素，在给定度量下，若两个元素的距离至多为阈值δ，则视为匹配。尽管已取得显著进展，但现有针对一般闵可夫斯基距离的构造要么依赖强的双侧几何分离假设，要么在单侧假设下产生显著开销。本研究提出了首个在单侧假设下针对一般Lₚ∈[1,∞]距离的具体高效模糊PSI协议，仅依赖轻量级对称密钥原语，支持发送方侧和接收方侧两种场景。我们进一步研究了更稀疏的输入分布，并针对该场景提出了更高效的协议。为降低随δ缩放的开销，我们非平凡地将前缀树（prefix trie）技术融入协议，首次实现一般Lₚ∈[1,∞]距离下O(logδ)的复杂度，优于现有工作的O((logδ)ᵈ)或O(δ)复杂度。在广泛参数设置下的大量实验表明，我们的协议在相同假设下显著优于现有工作：对比van Baarsen和Pu（EUROCRYPT'24）的工作，我们的协议实现了最高239倍的计算速度提升和最高20倍的通信量降低；对比Dang等人（CCS'25）的工作，实现了最高518倍的速度提升和最高63倍的通信量降低；对比Bui等人（ASIACRYPT'25）的工作，实现了最高4818倍的计算速度提升和最高282倍的通信量降低。
英文摘要
Fuzzy private set intersection (PSI) enables two parties to identify approximately matching elements between their input sets, where two elements are considered a match if their distance is at most a threshold $δ$ under a given metric. Although substantial progress has been made, existing constructions for general Minkowski distances either rely on strong two-sided geometric separation assumptions or incur substantial overhead under one-sided assumptions. In this work, we present the first concretely efficient fuzzy PSI protocols for general $L_{p\in[1,\infty]}$ distances under one-sided assumptions, relying solely on lightweight symmetric-key primitives. Our constructions support both sender-sided and receiver-sided settings. We further study sparser input distributions and present more efficient protocols tailored to this case. To reduce the overhead scaling with $δ$, we non-trivially incorporate prefix trie techniques into our protocols, achieving $O(\logδ)$ complexity for general $L_{p\in[1,\infty]}$ distances for the first time, improving upon $O((\logδ)^d)$ or $O(δ)$ complexities of prior works. Extensive experiments, across a wide range of parameter settings, show that our protocols significantly outperform prior works under the same assumptions. Specifically, against van Baarsen and Pu (EUROCRYPT'24), our protocols achieve up to $248\times$ faster computation and up to $20\times$ lower communication. Against Dang et al. (CCS'25), we achieve up to $568\times$ speedup and up to $63\times$ communication reduction. Against Bui et al. (ASIACRYPT'25), we achieve up to $4978\times$ faster computation and up to $282\times$ lower communication.
发表机构
- Nanyang Technological University（南洋理工大学）
- Singapore Management University（新加坡管理大学）
- Illinois Institute of Technology（伊利诺伊理工学院）
- Arizona State University（亚利桑那州立大学）
机构由 AI 辅助整理，请以论文原文为准。
