Expressive attribute-based keyword search with constant-size ciphertext

Jinguang Han, Ye Yang, Joseph K. Liu, Jiguo Li, Kaitai Liang, Jian Shen

    Research output: Contribution to journalArticleResearchpeer-review

    4 Citations (Scopus)

    Abstract

    Prior to outsourcing sensitive data, users need to encrypt the data and outsource the ciphertexts to cloud servers. Therefore, efficient search techniques on encrypted data are required. Attribute-based keyword search (ABKS) is a public key encryption with keyword search scheme where a legal user can authorize a proxy to test whether a ciphertext includes some exact keywords without exposing the content of sensitive data to the proxy. However, the main obstacle to implementing existing ABKS schemes is that the computation cost is very heavy, namely linear with the number of required attributes. In this paper, we propose a new ABKS scheme with following features: (1) It supports AND and NOT gates; (2) the size of the searchable ciphertexts is constant; (3) the size of the trapdoors generated by legal users for the proxy is constant; (4) to encrypt a message, the computation cost is constant; and (5) the computation cost of the test algorithm is constant. To the best of our knowledge, it is the first ABKS which has constant-size ciphertext and supports AND and NOT gates.

    Original languageEnglish
    Pages (from-to)5163-5177
    Number of pages15
    JournalSoft Computing
    Volume22
    Issue number15
    DOIs
    Publication statusPublished - Aug 2018

    Keywords

    • Access control
    • Attribute-based keyword search
    • Cloud computing
    • Revocation
    • Security

    Cite this

    Han, Jinguang ; Yang, Ye ; Liu, Joseph K. ; Li, Jiguo ; Liang, Kaitai ; Shen, Jian. / Expressive attribute-based keyword search with constant-size ciphertext. In: Soft Computing. 2018 ; Vol. 22, No. 15. pp. 5163-5177.
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    Expressive attribute-based keyword search with constant-size ciphertext. / Han, Jinguang; Yang, Ye; Liu, Joseph K.; Li, Jiguo; Liang, Kaitai ; Shen, Jian.

    In: Soft Computing, Vol. 22, No. 15, 08.2018, p. 5163-5177.

    Research output: Contribution to journalArticleResearchpeer-review

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