Attribute-based proxy re-encryption method for revocation in cloud data storage

Yoshiko Yasumura, Hiroki Imabayashi, Hayato Yamana

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    3 Citations (Scopus)

    Abstract

    In the big data era, many users upload data to cloud while security concerns are growing. By using attribute-based encryption (ABE), users can securely store data in cloud while exerting access control over it. Revocation is necessary for real-world applications of ABE so that revoked users can no longer decrypt data. In actual implementations, however, revocation requires re-encryption of data in client side through download, decrypt, encrypt, and upload, which results in huge communication cost between the client and the cloud depending on the data size. In this paper, we propose a new method where the data can be re-encrypted in cloud without downloading any data. The experimental result showed that our method reduces the communication cost by one quarter in comparison with the trivial solution where re-encryption is performed in client side.

    Original languageEnglish
    Title of host publicationProceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages4858-4860
    Number of pages3
    Volume2018-January
    ISBN (Electronic)9781538627143
    DOIs
    Publication statusPublished - 2018 Jan 12
    Event5th IEEE International Conference on Big Data, Big Data 2017 - Boston, United States
    Duration: 2017 Dec 112017 Dec 14

    Other

    Other5th IEEE International Conference on Big Data, Big Data 2017
    CountryUnited States
    CityBoston
    Period17/12/1117/12/14

    Fingerprint

    Revocation
    Data Storage
    Encryption
    Cryptography
    Attribute
    Data storage equipment
    Communication
    Communication Cost
    Access control
    Costs
    Real-world Applications
    Access Control
    Trivial
    Necessary
    Communication cost
    Experimental Results

    Keywords

    • attribute-based encryption (ABE)
    • cloud storage
    • proxy re-encryption

    ASJC Scopus subject areas

    • Computer Networks and Communications
    • Hardware and Architecture
    • Information Systems
    • Information Systems and Management
    • Control and Optimization

    Cite this

    Yasumura, Y., Imabayashi, H., & Yamana, H. (2018). Attribute-based proxy re-encryption method for revocation in cloud data storage. In Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017 (Vol. 2018-January, pp. 4858-4860). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData.2017.8258567

    Attribute-based proxy re-encryption method for revocation in cloud data storage. / Yasumura, Yoshiko; Imabayashi, Hiroki; Yamana, Hayato.

    Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017. Vol. 2018-January Institute of Electrical and Electronics Engineers Inc., 2018. p. 4858-4860.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Yasumura, Y, Imabayashi, H & Yamana, H 2018, Attribute-based proxy re-encryption method for revocation in cloud data storage. in Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017. vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 4858-4860, 5th IEEE International Conference on Big Data, Big Data 2017, Boston, United States, 17/12/11. https://doi.org/10.1109/BigData.2017.8258567
    Yasumura Y, Imabayashi H, Yamana H. Attribute-based proxy re-encryption method for revocation in cloud data storage. In Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017. Vol. 2018-January. Institute of Electrical and Electronics Engineers Inc. 2018. p. 4858-4860 https://doi.org/10.1109/BigData.2017.8258567
    Yasumura, Yoshiko ; Imabayashi, Hiroki ; Yamana, Hayato. / Attribute-based proxy re-encryption method for revocation in cloud data storage. Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017. Vol. 2018-January Institute of Electrical and Electronics Engineers Inc., 2018. pp. 4858-4860
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