Towards Scalable Fuzzy PSI via Efficient Fuzzy Matching

2026-08-11

AI Summary

This work addresses the scalability bottleneck of existing fuzzy private set intersection (PSI) protocols, which incur high computational and communication overheads in both high- and low-dimensional settings. To overcome this limitation, the authors propose two efficient protocols—one based on role-reversing oblivious pseudorandom functions and the other on customized oblivious transfer—unified under a novel two-layer hashing framework that seamlessly supports diverse dimensionalities. For the first time, they achieve linear-cost fuzzy PSI protocols for general Lp-distance metrics, breaking away from prior dependencies on factors such as (log δ)^d or δ. Combined with domain reduction and other optimizations, their approach yields up to a 145× speedup in runtime and reduces communication costs by up to 20× compared to the state-of-the-art.

Abstract

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).

Problem

• fuzzy PSI
• scalability
• fuzzy matching
• private set intersection
• efficient protocols

Innovation

• fuzzy PSI
• oblivious PRF
• oblivious transfer
• scalable protocols
• dual-layer hashing