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Enabling Hidden Frequency Keyword-Based Auditing on Distributed Architectures for a Smart Government

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Frontiers in Cyber Security (FCS 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1726))

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Abstract

Public auditing checks the integrity of outsourced data via random sampling and verifying sample data blocks. In practice, however, users do not pay attention to the entire data set but focus on the integrity of only the part of the data containing keywords of interest. Therefore, the keyword-based auditing paradigm is proposed; it depends entirely on the subjective choice or access habits, which makes it possible for malicious storage servers to analyze the auditing frequency, or reduce redundant backups. For government data, auditing frequency privacy leakage or corruption of any file could be catastrophic. In this paper, we propose a hidden frequency keyword-based auditing scheme for a smart government named HFKA, which is compatible with distributed storage architecture. HFKA leverages a Bloom filter, which adjusts the false positive rate to consider auditing files corresponding to specified keywords and auditing random files obtained via fuzzy matching. To obtain privacy-preserving fuzzy matching, HFKA constructs an index table embedded with update times to retrieve a wide range of files to be audited. This approach is secure against the replay attack and supports the index table update through structure iteration instead of recalculation. HFKA provides storage robustness, privacy protection of hidden frequencies, and data security. Additionally, HFKA can reduce audit computation overhead by 32.6% compared to the probabilistic public auditing.

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Notes

  1. 1.

    Hyperledger Fabric architecture is a scalable architectural design, an open interface style, and pluggable components. It first introduced authority management into the blockchain field, and its authority management function was completed by its independent Fabric certificate authority module. Hyperledger Fabric provides an important architectural reference for the design and implementation of distributed platforms.

  2. 2.

    The Reed Solomon erase-code technique is chosen to redundantly segment F, and its security and efficiency have been widely proven.

  3. 3.

    Time frequency inverse document frequency is a keyword extraction technology that sorts all words according to the frequency of each word in the document, which is used to extract the top-\(\mathcal {K}\) words as the keywords of a file in HFKA.

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Acknowledgements

This work was supported in part by the Southwest Petroleum University “Set Sail” Project (Grant No.0202667680-2021QHZ017); in part by the National Natural Science Foundation of China (Grant No.61902327); in part by the China Postdoctoral Science Foundation (Grant No.2020M681316); in part by the Chengdu Key R & D project (Grant No.2021-YF05-00965-SN); and in part by the Southwest Petroleum University Graduate Teaching and Research Reform Project (Grant No.JY20ZD06).

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Authors and Affiliations

  1. School of Computer Science, Research Center for Cyber Security, Southwest Petroleum University, Chengdu, 610599, China

    Jingting Xue, Shuqin Luo, Lingjie Shi & Xiaojun Zhang

  2. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 610097, China

    Jingting Xue & Chunxiang Xu

  3. School of Cyber Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200030, China

    Xiaojun Zhang

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  1. Jingting Xue
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  2. Shuqin Luo
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  3. Lingjie Shi
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Correspondence to Jingting Xue or Shuqin Luo .

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  1. Kwame Nkrumah University of Science and Technology, Kumasi, Ghana

    Emmanuel Ahene

  2. University of Electronic Science and Technology of China, Chengdu, China

    Fagen Li

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Xue, J., Luo, S., Shi, L., Zhang, X., Xu, C. (2022). Enabling Hidden Frequency Keyword-Based Auditing on Distributed Architectures for a Smart Government. In: Ahene, E., Li, F. (eds) Frontiers in Cyber Security. FCS 2022. Communications in Computer and Information Science, vol 1726. Springer, Singapore. https://doi.org/10.1007/978-981-19-8445-7_4

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  • DOI: https://doi.org/10.1007/978-981-19-8445-7_4

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