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IJML 2019 Vol.9(1): 75-82
doi: 10.18178/ijmlc.2019.9.1.768

Recursive General Secret Sharing Scheme Based on Authorized Subsets

Utako Itoh , Kouya Tochikubo

  • Department of Mathematical Information Engineering, Graduate School of Industrial Technology, Nihon University, Narashino-shi, Chiba 275-8575, Japan.

Abstract

The basic idea of secret sharing is that a dealer distributes a piece of information about a secret to each participant in such a way that authorized subsets of participants can reconstruct the secret but unauthorized subsets of participants cannot determine the secret. We propose a new secret sharing scheme realizing general access structures, which is based on authorized subsets. The proposed scheme is perfect and can reduce the number of shares distributed to one specified participant. In the implementation of secret sharing schemes for general access structures, an important issue is the number of shares distributed to each participant. We can apply the proposed scheme to the same access structure recursively. That is, the proposed scheme can reduce the number of shares distributed to another participant once again by applying the proposed scheme recursively. We apply the proposed scheme to all access structures on five participants in order to evaluate the efficiency of the proposed scheme.

Keywords

  • Secret sharing scheme
  • general access structure
  • (<em>k</em>
  • <em>n</em>)-threshold scheme
768-E05

How to Cite

Copied

Utako Itoh and Kouya Tochikubo, "Recursive General Secret Sharing Scheme Based on Authorized Subsets," International Journal of Machine Learning, vol. 9, no. 1, pp. 75-82, 2019. https://doi.org/10.18178/ijmlc.2019.9.1.768

Copyright & License

Copyright © 2019 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • DOI: 10.18178/IJML
  • Frequency: Quarterly
  • Average Days to Accept: 68 days
  • Acceptance Rate: 27%
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • DOI: 10.18178/IJML
  • Abstracing/Indexing: Google Scholar, Crossref, ProQuest, Electronic Journals Library, CNKI.
  • E-mail: editor@ijml.org
  • APC: 500USD