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IJMLC 2019 Vol.9(5): 636-643 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2019.9.5.851

Cryptanalysis and Improvement on Wang et al.’s Attribute-Based Searchable Encryption Scheme

Yi-Fan Tseng, Chun-I Fan, Si-Jing Wu, Hsin-Nan Kuo, and Jheng-Jia Huang

Abstract—Searchable encryption is a powerful and useful primitive when users want to store their encrypted files on cloud storages. In this paper, we demonstrate security flaws of the searchable encryption scheme proposed by Wang et al. in 2017. Furthermore, we propose a solution to fix the flaws, and the improved scheme also largely reduces the length of the ciphertext such that it is independent of the number of the attributes.

Index Terms—Attribute-based encryption, cryptanalysis, hidden policy, searchable encryption.

Yi-Fan Tseng is with the Department of Computer Science, National Chengchi University, Taipei, Taiwan (e-mail: yftseng@cs.nccu.edu.tw).
Chun-I Fan is with the Telecom Technology Center, Department of Computer Science and Engineering, National Sun Yat-sen University, Information Security Research Center, National Sun Yat-sen University, and Intelligent Electronic Commerce Research Center, National Sun Yat-sen University, Kaohsiung, Taiwan (Corresponding author; e-mail: cifan@mail.cse.nsysu.edu.tw).
Si-Jing Wu and Hsin-Nan Kuo are with the Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan (e-mail: jim5566556@gmail.com, bluedunk@gmail.com).
Jheng-Jia Huang is with the Telecom Technology Center, Kaohsiung, Taiwan (e-mail: d013040001@g-mail.nsysu.edu.tw).

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Cite: Yi-Fan Tseng, Chun-I Fan, Si-Jing Wu, Hsin-Nan Kuo, and Jheng-Jia Huang, "Cryptanalysis and Improvement on Wang et al.’s Attribute-Based Searchable Encryption Scheme," International Journal of Machine Learning and Computing vol. 9, no. 5, pp. 636-643, 2019.

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).

 

General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net


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