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An Efficient Public Batch Auditing Scheme for Data Integrity in Standard Model

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Machine Learning for Cyber Security (ML4CS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 12486))

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Abstract

The cloud storage auditing constructions derived from the primitives of homomorphic linear authenticator and polynomial-based authentication tag outperform other types of constructions in terms of the efficiency in verifier’s side. However, the batch auditing overheads regarding the storage and the computation in known constructions can be further reduced. And these constructions are improper for the standard batch auditing model. In this paper, we propose an efficient cloud storage auditing scheme supporting the batch auditing in standard model. To this end, the only nonce in the existing constructions is replaced with multiple nonces that are corresponding to each involved data owner. And the extended Euclidean algorithm is employed to generate the aggregated proof for batch auditing. In the proposed scheme, the overheads regarding storage and computation are both reduced to be as approximately large as the number of the involved data owners. The security analysis and the performance evaluation show that the proposed scheme is secure and efficient as expected.

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Acknowledgments

This work is supported by National Natural Science Foundation of China under Grant (No. 61772311) and China Scholarship Council (No. 201906220077).

Author information

Authors and Affiliations

  1. School of Mathematics, Shandong University, Jinan, 250100, China

    Haining Yang, Ye Su & Jing Qin

  2. State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing, 100093, China

    Jing Qin

  3. Fujian Provincial Key Laboratory of Network Security and Cryptology, College of Mathematics and Informatics, Fujian Normal University, Fuzhou, 350117, China

    Jinhua Ma

  4. Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore, Singapore

    Huaxiong Wang

Authors
  1. Haining Yang
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  2. Ye Su
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  3. Jing Qin
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  4. Jinhua Ma
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  5. Huaxiong Wang
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Corresponding author

Correspondence to Jing Qin .

Editor information

Editors and Affiliations

  1. Xidian University, Xi'an, China

    Xiaofeng Chen

  2. Guangzhou University, Guangzhou, China

    Hongyang Yan

  3. Michigan State University, East Lansing, MI, USA

    Qiben Yan

  4. Division of Computer, Electrical and Mathematical Sciences and Engineering, King Abdullah University of Science, Thuwal, Saudi Arabia

    Xiangliang Zhang

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Yang, H., Su, Y., Qin, J., Ma, J., Wang, H. (2020). An Efficient Public Batch Auditing Scheme for Data Integrity in Standard Model. In: Chen, X., Yan, H., Yan, Q., Zhang, X. (eds) Machine Learning for Cyber Security. ML4CS 2020. Lecture Notes in Computer Science(), vol 12486. Springer, Cham. https://doi.org/10.1007/978-3-030-62223-7_51

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  • DOI: https://doi.org/10.1007/978-3-030-62223-7_51

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-62222-0

  • Online ISBN: 978-3-030-62223-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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