AI 中文总结
本研究针对一般 $L_p$ 距离的模糊PSI问题，提出两种高效模糊匹配协议及对应低、高维集合的模糊PSI协议，在运行时间和通信成本上较现有方案实现显著优化。
AI 中文摘要
本文提出了适用于一般 $L_{p \in [1, \infty]}$ 距离的可扩展模糊私有集合交集（PSI）协议，支持低维和高维集合。核心技术是两种高效的模糊匹配协议：第一种基于角色反转不经意伪随机函数（OPRF）构建，实现了 $O(d\log \delta)$ 的开销，而此前工作的开销为 $O((\log \delta)^d)$；第二种利用定制不经意传输（OT），开销为 $O(d\ell)$（其中 $\ell$ 是输入的比特长度），特别适用于短输入。基于这些新技术，进一步提出了用于低维集合模糊PSI的双层哈希框架，采用基于OT的模糊匹配实例化，并结合了域缩减优化，该协议的开销随 $n、m、\log \delta、2^d$ 线性增长，无此前工作中的 $O((\log \delta)^d)$ 或 $O(\delta)$ 因子。对于高维集合，基于OPRF和OT的模糊匹配构建了模糊PSI协议，其渐近开销随 $n、m、d、\log \delta$ 线性增长，但依赖强全局不相交假设。大量评估表明，与van Baarsen和Pu（ASIACRYPT'25）的方案相比，本协议的运行时间最高提速145倍，通信成本最高降低20倍；与Piske等人（CCS'25）的方案相比，运行时间最高提速25倍，通信成本最高降低17倍。
英文摘要
In this paper, we present scalable fuzzy PSI protocols for general $L_{p \in [1, \infty]}$ distance, supporting both low- and high-dimensional sets. The core technique is two efficient fuzzy matching protocols. The first is built from a role-reversed oblivious PRF (OPRF) and realizes $O(d\log δ)$ overhead, compared to $O((\log δ)^d)$ in previous works. The second leverages customized oblivious transfer (OT) with $O(d\ell)$ overhead, where $\ell$ is the bit length of inputs, which is particularly suitable for short inputs. With these new techniques, we further propose a new dual-layer hashing framework for fuzzy PSI over low-dimensional sets, instantiated with our OT-based fuzzy matching and enhanced with a domain reduction optimization. The protocols achieve an overhead linear with $n, m, \log δ, 2^d$, without the $O((\log δ)^d)$ or $O(δ)$ factors present in prior works. {For high-dimensional sets, we construct fuzzy PSI protocols based on our OPRF- and OT-based fuzzy matching, which achieve an asymptotic overhead linear with $n, m, d$, and $\log δ$ but rely on the strong globally disjoint assumption.} Extensive evaluations demonstrate that our protocols achieve up to a $145\times$ speedup in running time and a $20\times$ reduction in communication cost compared to van Baarsen and Pu~(ASIACRYPT'25), and achieve up to a $25\times$ speedup in running time and up to a $17\times$ reduction in communication cost compared to Piske et al.~(CCS'25).
CommentsACM CCS 2026
