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Fuzzy Logic Application to Searchable Cryptography

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Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference (EANN 2020)

Part of the book series: Proceedings of the International Neural Networks Society ((INNS,volume 2))

Abstract

Public Key Encryption with Keyword Search (PEKS) allows users to search encrypted files by a specific keyword without compromising the original data security. Almost all current PEKS schemes enable users to search exact keyword only instead of imprecise keyword (such as “latest”, “biggest”, etc.). Therefore, if the keyword is fuzzy, these PEKS schemes will be terminated and then report errors. Besides, some PEKS schemes are not secure mainly because they are vulnerable to Off-line Keyword Guessing Attack (OKGA). This research paper incorporates with Mamdani Fuzzy Inference System to PEKS for supporting Fuzzy Keyword Search. Secondly, the proposed scheme is proved to be semantic secure under the random oracle models so that it is able to resist OKGA. In addition, the new scheme allows users to search multiple keywords and therefore, it could be applied to the general public networks.

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Author information

Authors and Affiliations

  1. School of Computing and Digital Media, London Metropolitan University, London, UK

    Hassan B. Kazemian

  2. Underwriters Laboratories, UK Security Lab, Basingstoke, UK

    Yang Ma

Authors
  1. Hassan B. Kazemian
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  2. Yang Ma
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Correspondence to Hassan B. Kazemian .

Editor information

Editors and Affiliations

  1. School of Engineering, Department of Civil Engineering, Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  2. Lancaster University, Lancaster, UK

    Plamen Parvanov Angelov

  3. School of Computing and Digital Technologies, Teesside University, Middlesbrough, UK

    Chrisina Jayne

  4. University of the West of England, Bristol, UK

    Elias Pimenidis

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Kazemian, H.B., Ma, Y. (2020). Fuzzy Logic Application to Searchable Cryptography. In: Iliadis, L., Angelov, P., Jayne, C., Pimenidis, E. (eds) Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference. EANN 2020. Proceedings of the International Neural Networks Society, vol 2. Springer, Cham. https://doi.org/10.1007/978-3-030-48791-1_32

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  • DOI: https://doi.org/10.1007/978-3-030-48791-1_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-48790-4

  • Online ISBN: 978-3-030-48791-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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