Paper 2025/996

Distance-Aware OT with Application to Fuzzy PSI

Abstract

A two-party fuzzy private set intersection (PSI) protocol between Alice and Bob with input sets $A$ and $B$ allows Alice to learn nothing more than the points of Bob that are ``$\delta$-close'' to its points in some metric space $\texttt{dist}$. More formally, Alice learns only the set $\{ b\ |~\texttt{dist}{(a,b)} \leq \delta , a \in A,b\in B\}$ for a predefined threshold $\delta$ and distance metric $\texttt{dist}$, while Bob learns nothing about Alice's set. Fuzzy PSI is a valuable privacy tool in scenarios where private set intersection needs to be computed over imprecise or measurement-based data, such as GPS coordinates or healthcare data. Previous approaches to fuzzy PSI rely on asymmetric cryptographic primitives, generic two-party computation (2PC) techniques like garbled circuits, or function secret sharing methods, all of which are computationally intensive and lead to poor concrete efficiency. This work introduces a new modular framework for fuzzy PSI, {primarily built on efficient symmetric key primitives}. Our framework reduces the design of efficient fuzzy PSI to a novel variant of oblivious transfer (OT), which we term distance-aware random OT (da-ROT). This variant enables the sender to obtain two random strings $(r_0, r_1)$, while the receiver obtains one of these values $r_b$, depending on whether the receiver’s input keyword $a$ and the sender’s input keyword $b$ are close in some metric space i.e., $\texttt{dist}{(a,b)} \leq \delta$. The da-ROT can be viewed as a natural extension of traditional OT, where the condition (choice bit) is known to the receiver. We propose efficient constructions for da-ROT based on standard OT techniques tailored for small domains, supporting distance metrics such as the Chebyshev norm, the Euclidean norm, and the Manhattan norm. By integrating these da-ROT constructions, our fuzzy PSI framework achieves up to a $14\times$ reduction in communication cost and up to a $54\times$ reduction in computation cost compared to previous state-of-the-art protocols, across input set sizes ranging from $2^8$ to $2^{16}$. Additionally, we extend our framework to compute fuzzy PSI cardinality and fuzzy join from traditional PSI-related functionalities. All proposed protocols are secure in the semi-honest model.