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Research on Privacy Protection of Large-Scale Network Data Aggregation Process

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Abstract

Data privacy should be protected by law. Based on the analysis of data privacy protection situation at home and abroad, it is proposed that our country can protect data privacy by improving personnel quality, establishing relevant legal system and adopting technical prevention strategies. These strategies have certain guiding significance for exploring data privacy protection suitable for our national conditions. In order to solve the privacy protection problem in the process of large-scale network data aggregation, this paper proposes an Privacy Protection Algorithms (PPA) based on large-scale network data aggregation for the shortcomings of the existing standard large-scale network data aggregation algorithm with low time efficiency and poor reversibility. Converting the original network database into a large-scale network data aggregation form, performing network compression according to the Hamming weight of each network vector after conversion, using the matrix column vector to perform an AND operation, and calculating the support degree of the candidate set, thereby obtaining frequent itemsets. Experimental results show that compared with the original algorithm, the algorithm can improve the time efficiency while ensuring the false positive rate, has good reversibility and security, and is more practical.

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Authors and Affiliations

  1. State Grid Xinjiang Information & Telecommunication Company, Ürümqi, 830000, China

    Yuelin Zou, Wei He & Longjun Zhang

  2. State Grid Xinjiang Electric Power Co., Ltd., Ürümqi, 830000, China

    Jiati Ni

  3. State Grid Info-telecom Great Power Science and Technology Co., Ltd., Fuzhou, 350001, China

    Qiang Chen

Authors
  1. Yuelin Zou
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  2. Wei He
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  3. Longjun Zhang
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  4. Jiati Ni
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  5. Qiang Chen
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Correspondence to Yuelin Zou.

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Zou, Y., He, W., Zhang, L. et al. Research on Privacy Protection of Large-Scale Network Data Aggregation Process. Int J Wireless Inf Networks 26, 193–200 (2019). https://doi.org/10.1007/s10776-019-00443-w

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  • DOI: https://doi.org/10.1007/s10776-019-00443-w

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