Abstract With the rapid development of distributed collaborative computing, privacy leakage risks in multi-source data joint analysis are increasing, and classical secure scalar product (SSP) protocols are vulnerable to quantum attacks. Existing quantum secure scalar product schemes mostly rely on complex quantum resources or third parties, only support two-party computation, and have poor scalability. To address these issues, this paper proposes a secure multi-party quantum scalar product protocol based on single-photon resources without a third party. The protocol adopts a two-layer architectural design, integrating pairwise secure product operation and privacy-preserving aggregation mechanism. It realizes result blinding via data splitting and reconstruction to fully guarantee data confidentiality. By adopting ring interaction structure and lightweight secure computing modes, it completes collaborative computation of multi-party private data and effectively prevents information leakage in interactive processes. Performance analysis shows that the protocol requires no more than single-photon and single-particle measurements, with complexity within a reasonable range, making it feasible with current quantum technology. This protocol provides an efficient and secure primitive for multi-party privacy-preserving collaborative computing in quantum-resistant scenarios.
Abstract With the rapid development of distributed collaborative computing, privacy leakage risks in multi-source data joint analysis are increasing, and classical secure scalar product (SSP) protocols are vulnerable to quantum attacks. Existing quantum secure scalar product schemes mostly rely on complex quantum resources or third parties, only support two-party computation, and have poor scalability. To address these issues, this paper proposes a secure multi-party quantum scalar product protocol based on single-photon resources without a third party. The protocol adopts a two-layer architectural design, integrating pairwise secure product operation and privacy-preserving aggregation mechanism. It realizes result blinding via data splitting and reconstruction to fully guarantee data confidentiality. By adopting ring interaction structure and lightweight secure computing modes, it completes collaborative computation of multi-party private data and effectively prevents information leakage in interactive processes. Performance analysis shows that the protocol requires no more than single-photon and single-particle measurements, with complexity within a reasonable range, making it feasible with current quantum technology. This protocol provides an efficient and secure primitive for multi-party privacy-preserving collaborative computing in quantum-resistant scenarios.
Secure two-party scalar product (S2SP) is a promising research area within secure multiparty computation (SMC), which can solve a range of SMC problems, such as intrusion detection, data analysis, and geometric computations. However, existing quantum S2SP protocols are not efficient enough, and the complexity is usually close to exponential level. In this paper, a novel secure two-party quantum scalar product (S2QSP) protocol based on Fourier entangled states is proposed to achieve higher efficiency. Firstly, the definition of unconditional security under malicious models is given. And then, an honesty verification method called Entanglement Bondage is proposed, which is used in conjunction with the modular summation gate to resist malicious attacks. The property of Fourierentangled states is used to calculate the scalar product with polynomial complexity. The unconditional security of our protocol is proved, which guarantees the privacy of all parties. In addition, we design a privacy-preserving quantum matrix multiplication protocol based on S2QSP protocol. By transforming matrix multiplication into a series of scalar product processes, the product of two private matrices is calculated without revealing any privacy. Finally, we show our protocol’s feasibility in IBM Qiskit simulator.
Secure computation of scalar product is of considerable importance due to its central role in many practical computation scenarios with privacy and security requirements. This paper includes new results about the secure two-party scalar product. Specifically, a perfectly secure and universally composable two-party split scalar product (SSP) protocol is proposed in the preprocessing model. In addition to full security, the proposed SSP protocol enjoys the advantage of optimal efficiency. To show the optimality of this SSP protocol, information theoretic lower bounds on the amount of communicated data in a secure two-party computation in the preprocessing model are derived. These bounds are not limited to SSP functionality but apply to a large class of two-party functionalities. A part of this paper is devoted to applications of the proposed SSP protocol in secure cloud computing. Specifically, based on this protocol, a cloud-assisted privacy-preserving profile-matching scheme and a secure remote health monitoring scheme are proposed. Both of the solutions are highly efficient and significantly improve the previous work.
With the rapid advancement of information technology, data sharing has become increasingly accessible, leading to a heightened need for robust personal data protection. One important application in privacy-preserving computing is the aggregation of information when collaboratively establishing AI models through public distributed networks. To counter the threat posed by quantum computing to encrypted data, various quantum private summation (QPS) protocols have been proposed thus far. However, some of these existing protocols operate solely under modulo 2, while other approaches for modulo d often rely on impractically high-dimensional qudits. Therefore, this study proposes an innovative multiparty QPS method that balances participant data sharing and privacy without requiring high-dimensional photons. The proposed QPS protocol enables participants to contribute aggregated information to third parties without disclosing individual data. A security analysis further demonstrates that the proposed QPS effectively counters common eavesdropping attacks, ensuring reliable protection of personal data.
With the rapid expansion of the digital economy and smart grids, secure and fair pricing mechanisms have become essential for data trading. However, existing approaches remain vulnerable to quantum attacks, exhibit poor scalability in multi-party settings, and demand high resource consumption. To overcome these limitations, we introduce, for the first time, a Quantum Secure Joint Pricing (QSJP) mechanism, which supports multi-party joint pricing and ensures fair and privacy-preserving pricing in power data trading while protecting participants’ sensitive information and incentivizing their active participation. At the core of QSJP, we propose a novel Secure Multi-Party Quantum Scalar Product (SMQSP) protocol, supported by a Secure Multi-Party Quantum Summation (SMQS) scheme that enables privacy-preserving and efficient aggregation of intermediate results. The SMQSP protocol leverages Fourier entangled states and GHZ states to achieve scalable and efficient scalar product computation with polynomial complexity. We further validate the correctness and practicality of the proposed framework through circuit simulations on IBM Qiskit.
Secure multi-party computation (SMC) enables multiple parties to collaboratively perform computational tasks without revealing their private data. A specific application scenario within SMC is Private Set Intersection with Correlated Sum (PSI-CS), where two participating parties compute the sum of associated elements in their intersection while preserving the privacy of their individual sets. This problem arises in various contexts, including calculating conversion amounts between suppliers and platforms, such as advertising conversion amounts and agency business conversion amounts. The paper proposes a protocol that employs quantum single-photon technology to compute the PSI-CS, provides four technical solutions suitable for various scenarios ranging from small to large data volumes, sparse to dense datasets, and without revealing the cardinality of the intersection. Additionally, the method extends to computing the union set with correlated sums. We experimentally validate the proposed protocol, conduct correctness and security analyses, and demonstrate its practical significance and application value. By utilizing single-photon technology, quantum gate operations, and measurements become more accessible, simplifying the protocol’s implementation. Notably, our approach provides unconditional security against quantum computational attacks, representing a substantial improvement in both efficiency and practical applicability compared to classical domain protocols.
With the advent of the big data era, crossorganizational collaborative data computing faces challenges in privacy protection. Traditional secure multi-party computation (MPC) protocols suffer from security insufficiency under the threat of quantum computing against classical encryption algorithms. To address this issue, this paper proposes a secure quantum protocol based on mutually unbiased bases (MUBs) in dlevel quantum systems. By leveraging quantum entanglement and quantum measurement techniques, the protocol accurately calculates the Euclidean distance between participants while ensuring privacy protection during the computation. Experimental results demonstrate that the protocol not only enhances computational efficiency but also effectively resists external attacks, participant attacks, and semi-honest third-party attacks, providing a new approach for the application of quantumsafe computing in privacy protection.