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Identity-Based Batch Provable Data Possession

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Provable Security (ProvSec 2016)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 10005))

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Abstract

Provable Data Possession (PDP) is a technique for checking whether data is correctly stored in remote servers without retrieving the entire data. For many previous PDP schemes, correctly choosing public key for clients relies on the security of Public Key Infrastructure (PKI), but PKI itself still faces many kinds of security vulnerabilities. In addition, the verification of certificates introduces heavy computation and communication cost. In this paper, we propose an Identity-Based Batch Provable Data Possession (ID-BPDP) scheme to eliminate the certificate management. Meanwhile, to the best of our knowledge, it is the first identity-based provable data possession scheme supporting batch verification for multiple owners and multiple clouds simultaneously to reduce computation cost greatly. Our scheme is provably correct and secure based on bilinear pairings and the hardness assumption of Computational Diffie-Hellman problem, and our analyses/simulations show that the scheme is able to verify the integrity of data efficiently.

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Author information

Authors and Affiliations

  1. Software College, Northeastern University, Shenyang, China

    Fucai Zhou, Jian Xu & Zifeng Xu

  2. School of Computer Science and Engineering, Northeastern University, Shenyang, China

    Su Peng

Authors
  1. Fucai Zhou
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  2. Su Peng
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  3. Jian Xu
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  4. Zifeng Xu
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Corresponding author

Correspondence to Fucai Zhou .

Editor information

Editors and Affiliations

  1. University of Surrey , Guildford, United Kingdom

    Liqun Chen

  2. Nanjing University of Finance and Economics, Nanjing, China

    Jinguang Han

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Zhou, F., Peng, S., Xu, J., Xu, Z. (2016). Identity-Based Batch Provable Data Possession. In: Chen, L., Han, J. (eds) Provable Security. ProvSec 2016. Lecture Notes in Computer Science(), vol 10005. Springer, Cham. https://doi.org/10.1007/978-3-319-47422-9_7

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  • DOI: https://doi.org/10.1007/978-3-319-47422-9_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47421-2

  • Online ISBN: 978-3-319-47422-9

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