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A Quantum Blind Multi-Signature Method for the Industrial Blockchain.
Cai, Zhengying; Liu, Shi; Han, Zhangyi; Wang, Rui; Huang, Yuehua.
Afiliação
  • Cai Z; College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China.
  • Liu S; College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China.
  • Han Z; College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China.
  • Wang R; College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China.
  • Huang Y; College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, China.
Entropy (Basel) ; 23(11)2021 Nov 15.
Article em En | MEDLINE | ID: mdl-34828218
ABSTRACT
Traditional anti-quantum methods and multi-signature technologies to secure the blockchain against quantum attacks will quickly reduce the efficiency and scalability of the industrial blockchain, where the computational resources will experience a polynomial rise with the increasing number of traders. Here, a quantum blind multi-signature method is proposed for the multi-party transaction to provide anti-quantum security. First, the proposed multi-party transaction frame and quantum key distribution in the industrial blockchain are introduced. It integrates a novel quantum blind multi-signature algorithm that is based on the quantum entanglement mechanism, and it is absolutely secure in theory. Second, the anti-quantum multi-signature algorithm is illustrated, where there are four phases, i.e., initialization, signing, verification, and implementation. Third, the security and complexity of the proposed framework are analyzed and compared with related methods in references, and our proposed method is verified to be able to offer good computational performance and blockchain scalability for multi-party transaction. Last, the paper is summarized and future research directions are proposed.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Ano de publicação: 2021 Tipo de documento: Article