A multiparty quantum threshold private set intersection and union cardinality protocol based only on single-particle states

Abstract Recent breakthroughs in genome sequencing have revolutionized genetic diagnostics, yet secure sharing of sensitive genomic data remains a critical barrier to clinical collaboration.We address this challenge through Threshold Labeled Private Set Intersection (TLPSI), a novel cryptographic protocol without using computation-heavy homomorphic encryption. enabling confidential diagnostic label exchange between a client with private variants and a server hosting pathogenic variant databases–disclosing labels only when matched variants surpass clinical threshold t . TLPSI integrates three key innovations: threshold-bound label recovery via ( t , n )-Shamir sharing, genomic marker protection through VOLE-based oblivious polynomial evaluation, and optimized O ( n ) complexity operations. Provably secure under the Decisional Diffie–Hellman assumption in semi-honest settings, TLPSI guarantees no leakage of unmatched variants or labels. In BRCA1/2 breast cancer testing, TLPSI processes 65k comparisons in 1.35 s with 53.5% (16-Thread) lower computational overhead than sequential threshold-plus-labeled PSI baselines, confirming clinical readiness. Our source code is available at https://anonymous.4open.science/r/TLPSI-CD5B .

Private set intersection (PSI) is a fundamental cryptographic task that allows two mutually distrusting parties, each holding a private set of elements, to jointly compute the intersection of their sets. It ensures a symmetric information structure where neither party gains any knowledge about the other’s elements beyond those in the shared intersection. Traditional PSI protocols are primarily designed for static settings, which limits their applicability and efficiency in dynamic scenarios where input sets continuously evolve. To address this challenge, the notion of updatable PSI (UPSI) was introduced, enabling repeated PSI computations over changing inputs while preserving the symmetric privacy guarantees between participants. Despite the numerous recent advancements in UPSI research, it still suffers from significant communication overhead. In this paper, we address this challenge by introducing LcUPSI (low-communication UPSI), a new updatable PSI protocol that achieves remarkably low communication overhead. We formally prove that LcUPSI is secure in the semi-honest model. Furthermore, we compare the LcUPSI protocol with the state-of-the-art UPSI protocol BMSTZ24 (ASIACRYPT). The results demonstrate that LcUPSI significantly reduces communication overhead, highlighting its advantages in low-bandwidth conditions.

Secure multiparty computation enables collaborative computations across multiple users while preserving individual privacy, which has a wide range of applications in finance, machine learning and healthcare. Secure multiparty computation can be realized using oblivious transfer as a primitive function. In this paper, we present an experimental implementation of a quantum-secure quantum oblivious transfer (QOT) protocol using an adapted quantum key distribution system combined with a bit commitment scheme, surpassing previous approaches only secure in the noisy storage model. We demonstrate the first practical application of the QOT protocol by solving the private set intersection, a prime example of secure multiparty computation, where two parties aim to find common elements in their datasets without revealing any other information. In our experiments, two banks can identify common suspicious accounts without disclosing any other data. This not only proves the experimental functionality of QOT, but also showcases its real-world commercial applications.

A survey on secure multi-party computation techniques based on private set intersection

Malicious secure lightweight private set intersection

In cloud environments, addressing the optimization problem between security and computational and communication overhead in multi-party private set intersection protocols. This paper proposes a multi-party Private Set Intersection protocol utilizing cloud server-assisted computation, enhancing the protocol's scalability and efficiency while safeguarding data privacy and security. This protocol is designed under the semi-honest security model, Participants process the elements of the set using keyed pseudorandom functions and hash functions, and introduce a pseudorandom generator to randomize participant information. This is combined with an oblivious key-value pair storage structure to complete data encoding, enabling cloud servers to determine intersections solely based on the encoded results, without access to any raw set information from participating parties. Theoretical analysis demonstrates that the proposed protocol correctly implements the computation of the intersection of multiple sets and satisfies privacy protection requirements under the specified security model.

Comparison of information among individuals, companies or government agencies in some instances is unavoidable. In scenarios of the comparison of information, the individual data owners have to willingly or compelled to find the intersection of their private set of information. Cryptographic private set intersection helps in the computation of the intersections securely without the disclosure of any other information not in the intersection. The protocol in this paper helps users securely and efficiently compute their private set intersection without disclosing any other information. The protocol has communication and computation complexities of . The sizes of the communication and computation complexities make the protocol ideal to be used on any device.

A secure multi-party sorting protocol based on private set intersection for sealed-bid auctions

Efficient unbalanced circuit private set intersection for ID alignment of Vertical Federated Learning

A general and lightweight method for private set intersection computation

Many efficient custom protocols have been developed for two-party private set intersection (PSI), that allow the parties to learn the intersection of their private sets. However, these approaches do not yield efficient solutions in the dynamic setting when the parties' sets evolve and the intersection has to be computed repeatedly. In this work we propose a new framework for this problem of updatable PSI — with elements being inserted and deleted — in the semi-honest model based on structured encryption. The framework reduces the problem of updatable PSI to a new variant of structured encryption (StE) for an updatable set datatype, which may be of independent interest. Our final construction is a constant round protocol with worst-case communication and computation complexity that grows linearly in the size of the updates and only poly-logarithmically with the size of the accumulated sets. Our protocol is the first to support arbitrary inserts and deletes for updatable PSI.

Vehicular crowdsensing enables Connected and Autonomous Vehicles (CAVs) to jointly contribute driving-related data to support applications such as traffic management and accident analysis. Ensuring the reliability of such data requires identifying observations that have been corroborated by multiple vehicles. However, achieving this corroboration typically necessitates comparing each vehicle’s private set of observations, which can inadvertently reveal sensitive trajectory information. To address this privacy challenge, we introduce Edge-Assisted Private Set Intersection (EA-PSI), a scheme that enables secure computation of the intersection among CAV observation sets without disclosing individual data elements. Our design begins with a protocol that leverages polynomial-based set encoding and additive secret sharing, in which each vehicle encodes its observation set as a polynomial and divides it into two shares, delegating one share to a roadside unit (RSU) while retaining the other locally. The RSU then performs intersection computation on the collected shares using randomized encoding techniques, without learning any private observations or intersection results. To support necessary polynomial operations over secret-shared data, we develop a secure multiplication mechanism based on Beaver triples, enabling the RSU and vehicles to jointly compute polynomial products without reconstructing underlying values. In addition, we design a key-distribution protocol that facilitates secure communication among vehicles through the RSU, eliminating the need for direct vehicle-to-vehicle exchange. We analyze the security of EA-PSI under the simulation-based paradigm and formally prove privacy against semi-honest adversaries. Experimental evaluation of computational and communication costs demonstrates the efficiency and practicality of the proposed EA-PSI scheme.

The growing Internet of Vehicles (IoV) requires efficient edge data management, where collaborative cache sharing between Roadside Units (RSU) and vehicles is crucial for reducing latency in applications like real-time navigation and collision avoidance. However, direct sharing of sensitive data such as vehicle locations and requests introduces serious privacy risks. To address this, we propose a lightweight privacy-preserving cache sharing scheme with label protection for vehicular networks. We first design an Oblivious Homomorphic XOR-PRF (Ohx-PRF) that employs key pre-distribution and homomorphic aggregation to eliminate oblivious transfer interactions. Building on this, we construct a non-interactive labeled delegated multi-party private set intersection (LDMPSI) protocol, where each data subset carries a classification label. Finally, we introduce a cache sharing scheme based on LDMPSI that uses efficient multi-point OPRF with only symmetric cryptography. By delegating computations to servers and processing data and labels in ciphertext, RSU can identify and store frequently requested content with minimal overhead. Formal analysis proves the proposed protocol and scheme are secure under the semi-honest model. Experiments show our scheme achieves over 85% cache hit rate, while LDMPSI reduces total runtime by 79% and communication overhead by over 80% compared to existing protocols, enhancing resource utilization and response capabilities in vehicular networks without compromising privacy.

A blind signature-based authorization scheme for enhancing the privacy of Cloud-Assisted private set intersection

In this letter, we identify critical vulnerabilities in both protocols proposed by Zhao et al. (published in IEEE TIFS, doi: 10.1109/TIFS.2025.3574993), showing that they are susceptible to collusion attacks. In the first protocol, the leader <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P₁</i> can determine whether its items are held by any honest party by colluding with other participants. In the second protocol, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P₁</i> can infer whether all honest parties hold a specific item without any collusion, violating the fundamental privacy guarantees of multi-party private set intersection.

Industrial Internet of Things (IIoT) systems in sectors like manufacturing, energy, and healthcare are increasingly deployed in cloud-assisted operational environments, where network telemetry and security analytics are routinely processed in the cloud. However, these systems remain highly vulnerable to cyber threats from shared threat actors. Among these, low-rate Denial of Service (LDoS) attacks, marked by subtle periodic traffic patterns, are particularly challenging to detect when analyzed in isolation. Cross-organization collaborative detection across cloud platforms can improve identification accuracy, but sharing threat intelligence risks exposing sensitive operational information. To tackle this challenge, we propose Labelled-Threshold Private Set Intersection (LT-PSI), a cryptographic framework that allows two organizational clouds to securely identify common elements whose associated label vectors satisfy a similarity threshold, without revealing any additional data. Our LT-PSI protocol introduces an innovative combination of position encoding, Diffie-Hellman Oblivious Pseudorandom Functions (DH-OPRF), and Bloom filters, effectively transforming threshold-based label similarity matching into efficient and privacy-preserving set membership tests. Particularly, our protocol achieves sublinear online complexity and is well-suited for cloud execution, integrating an adaptive early termination strategy that significantly reduces the number of OPRF invocations. We provide formal security proofs under the semi-honest model and validate the protocol through extensive experiments across diverse similarity thresholds and dataset sizes. Results show that LT-PSI is significantly more efficient than brute-force threshold matching while preserving privacy. The framework naturally supports cloud-to-cloud collaborative security analytics and generalizes to broader cloud and edge threat intelligence scenarios requiring private, threshold-based feature matching.

Efficient structure-aware private set intersection with distributed interval function

PCPSI: Efficient Unbalanced Private Set Intersection Using Homomorphic Encryption with Plaintext–Ciphertext Multiplication

Threshold private set intersection is a basic primitive of secure multiparty computation, which has many important applications in the field of privacy preservation and information security, especially in a ride sharing system, one of the most transforming and innovative technologies enabled by the Internet of Things. In this paper, we propose a new proposal for threshold private set intersection based on quantum homomorphic encryption, in which both the number of users and the designated trusted party are flexible. Furthermore, the complicated computing task can be delegated to a server for processing contributing to the speciality of quantum homomorphic encryption. More importantly, the users’ privacy is perfectly guaranteed because all the calculations are performed on the quantum encrypted data. Therefore, this proposal is more practical compared with the prior work.