Efficient Fuzzy PSI under One-Sided Assumptions

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 $\delta$ 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 $\delta$, we non-trivially incorporate prefix trie techniques into our protocols, achieving $O(\log\delta)$ complexity for general $L_{p\in[1,\infty]}$ distances for the first time, improving upon $O((\log\delta)^d)$ or $O(\delta)$ 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.