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author:

Zhang, Y. (Zhang, Y..) [1] | Liu, X. (Liu, X..) [2] | Yin, Y. (Yin, Y..) [3] | Zhang, Q. (Zhang, Q..) [4] | Jia, H. (Jia, H..) [5]

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Scopus

Abstract:

The first lattice-based verifier-local revocation group signature (GS-VLR) was introduced by Langlois et al. in 2014, and subsequently, a full and corrected version was proposed by Ling et al. in 2018. However, zero-knowledge proofs in both schemes are within a structure of Bonsai Tree, and thus have bit-sizes of the group public-key and member secret-key proportional to log N, where N is the group size. On the other hand, the revocation tokens in both schemes are related to the member secret-key and only obtain a weaker security, selfless-anonymity. For the tracing algorithms in both schemes, they just run in the linear time of N. Therefore, for a large group, the zero-knowledge proofs in lattice-based GS-VLR schemes are not that secure and efficient. In this work, we firstly utilize a compact and scalable identity-encoding technique which only needs a constant number of public matrices to encode the member’s identity information and it saves a O(log N) factor in both bit-sizes for the group public-key and member secret-key. Secondly, separating from the member secret-key, we generate revocation token within some public matrix and a short Gaussian vector, and thus obtain the strongest security, full-anonymity. Moreover, the explicit-traceability, to trace the signer’s identity in a constant time, independent of N, for the tracing authority is also satisfied. In particular, a new Stern-type statistical zero-knowledge proof protocol for a fully anonymous lattice-based GS-VLR scheme enjoying the above three advantages is proposed. © Springer Nature Switzerland AG 2020.

Keyword:

Explicit-traceability; Full-anonymity; Lattice-based group signatures; Verifier-local revocation; Zero-knowledge proofs

Community:

  • [ 1 ] [Zhang, Y.]Zhengzhou University of Light Industry, Zhengzhou, 450001, China
  • [ 2 ] [Liu, X.]Fuzhou University, Fuzhou, 350108, China
  • [ 3 ] [Yin, Y.]Zhengzhou University of Light Industry, Zhengzhou, 450001, China
  • [ 4 ] [Zhang, Q.]Zhengzhou University of Light Industry, Zhengzhou, 450001, China
  • [ 5 ] [Jia, H.]Guangzhou University, Guangzhou, 510006, China

Reprint 's Address:

  • [Zhang, Y.]Zhengzhou University of Light IndustryChina

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Source :

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ISSN: 0302-9743

Year: 2020

Volume: 12418 LNCS

Page: 381-399

Language: English

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JCR@2005

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 6

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 0

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