文献类型：期刊文献

英文题名：Quantum Secure Joint Pricing Mechanism for Power Data Trading

作者：Wang, Can[1];Shi, Run-Hua[1];Lian, Jiang-Yuan[1];Gao, Wei[1];Hou, Tong[2]

机构：[1]North China Elect Power Univ, Sch Control & Comp Engn, Beijing 102206, Peoples R China;[2]East China Univ Sci & Technol, Dept Elect & Commun Engn, Shanghai 200237, Peoples R China

年份：2026

卷号：72

期号：1

起止页码：755

外文期刊名：IEEE TRANSACTIONS ON CONSUMER ELECTRONICS

收录：;EI(收录号：20255319839452);WOS:【SCI-EXPANDED(收录号:WOS:001723028600049)】；

基金：This work was supported in part by the National KeyResearch and Development Program of China under Grant 2024YFF1206200,in part by the National Natural Science Foundation of China under Grant62502038 and Grant 61772001, and in part by Beijing Municipal Nat-ural Science Foundation under Grant 4242030.

语种：英文

外文关键词：Protocols; Pricing; Vectors; Quantum entanglement; Privacy; Logic gates; Scalability; Quantum circuit; Consumer electronics; Blockchains; Quantum computation; multi-party scalar product; pricing mechanism; data trading

摘要：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.