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Achieving public verifiability and data dynamics for cloud data in the standard model

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As an important cloud service, cloud storage can provide flexible data outsourcing services for data users. After the data are outsourced to the cloud, data user no longer physical controls over the stored data. To ensure these data to be kept intact at the cloud servers, many different solutions have been proposed. Whereas most of existing solutions can only deal with static data. To support dynamic data, some schemes solve it by adopting authenticated data structure. To the best of our knowledge, these schemes may exist the following flaws: (1) they bring heavy communication/computation burdens to the auditor; (2) they exist some security attack; (3) they are only proven to be secure in the random orale model; (4) data may be leaked in the auditing. Motivated by the above problems, we propose two novel public auditing schemes by introducing rb23Tree data structure. They can not only achieve public verification, but also support dynamics data updating. Furthermore, our second scheme also supports data privacy. As for the auditor, to reduce its computational cost and communication cost, our scheme migrates the partial auditing metadata from the cloud server to the auditor, it makes that communication overhead between the auditor and cloud server is constant. Finally, we show that our schemes are proven to be secure in the standard model, and evaluate the auditing performance by simulation experiment and comparison with Wang et al.’s scheme. The results demonstrate that our schemes outperforms Wang et al.’s scheme in terms of computation costs and communication overhead.

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This work was supported by Beijing Municipal Natural Science Foundation (Nos. 4162020, 4132056), Research Fund of Guangxi Key Lab of Multi-source Information Mining & Security (No. MIMS16-01) and the Fundamental Research Funds for the Central Universities under Grant ZYGX2015J059, GK201702004.

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Correspondence to Yong Yu.




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Zhang, J., Meng, H. & Yu, Y. Achieving public verifiability and data dynamics for cloud data in the standard model. Cluster Comput 20, 2641–2653 (2017). https://doi.org/10.1007/s10586-017-0804-9

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  • Data integrity checking
  • Dynamics data
  • Data privacy
  • rb23Tree
  • Standard model