Efficient Arbitrated Quantum Digital Signature with Multi-Receiver Verification

IF 4.3 Q1 OPTICS Advanced quantum technologies Pub Date : 2024-09-02 DOI:10.1002/qute.202400110
Siyu Xiong, Bangying Tang, Hui Han, Jinquan Huang, Mingqiang Bai, Fangzhao Li, Wanrong Yu, Zhiwen Mo, Bo Liu
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Abstract

Quantum digital signature is used to authenticate the identity of the signer with information theoretical security while providing non-forgery and non-repudiation services. In traditional multi-receiver quantum digital signature schemes without an arbitrator, the transferability of one-to-one signature is always required to achieve unforgeability, with complicated implementation and heavy key consumption. In this article, an arbitrated quantum digital signature scheme is proposed, in which the signature can be verified by multiple receivers simultaneously, and meanwhile, the transferability of the signature is still kept. This scheme can be simplified performed to various quantum secure networks, due to the proposed efficient signature calculation procedure with low secure key consumption and low computation complexity, by employing one-time universal hashing algorithm and a one-time pad encryption scheme. The evaluation results show that this scheme uses at least two orders of magnitude less key than existing signature schemes with transferability when signing files of the same length with the same number of receivers and security parameter settings.

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多接收器验证的高效仲裁量子数字签名
量子数字签名用于验证签名者的身份,具有信息理论上的安全性,同时提供不可伪造和不可抵赖服务。在没有仲裁器的传统多接收器量子数字签名方案中,为了实现不可伪造性,总是要求一对一签名的可转移性,实现复杂,密钥消耗大。本文提出了一种仲裁式量子数字签名方案,该方案中的签名可同时由多个接收器验证,同时仍保持签名的可转移性。由于采用了一次性通用哈希算法和一次性垫加密方案,该方案的签名计算过程高效、安全密钥消耗少、计算复杂度低,可简化到各种量子安全网络中。评估结果表明,在相同接收者数量和安全参数设置下签署相同长度的文件时,该方案比现有的签名方案至少少用两个数量级的密钥,并具有可转移性。
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Issue Information (Adv. Quantum Technol. 12/2025) Inside Front Cover: Quantum-Enhanced Simulated Annealing Using Rydberg Atoms (Adv. Quantum Technol. 12/2025) Inside Back Cover: Method for Noise-Induced Regularization in Quantum Neural Networks (Adv. Quantum Technol. 12/2025) Back Cover: Quantum-Noise-Driven Generative Diffusion Models (Adv. Quantum Technol. 12/2025) Front Cover: Intelligent Generative Models for Quantum Neural Networks (Adv. Quantum Technol. 12/2025)
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