Scalar-product based secure multi-party computation

Li-Bao He, Liusheng Huang, Wei Yang +1 more

Yan Huang

Can Wang, Runhua Shi, Jiang-Yuan Lian +1 more

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.

Nigel Paul Smart

Can Wang, Run-Hua SHI, Jiang-Yuan Lian +2 more

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.

Michael J. Collins

We give a protocol for secure multi-party computation of the scalar product of two sparse vectors of length n with O(log(log n)) rounds and polylogarithmic communication.