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Method of sensitive data mining based on Pan-Bull algebra

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Abstract

In order to improve the transmission stability of sensor networks, a sensitive data mining method based on Pan Boolean algebra is proposed. According to the output correctness, reliability and operation efficiency of wireless sensor network, this paper analyzes the characteristics of sensitive data, extracts and clusters the associated features of sensitive data, establishes the information clustering model of sensitive data in sensor network, and detects the fuzzy factor of sensitive data in sensor network with grid block clustering method, The Pan Boolean algebra analysis model is used to realize the hybrid deep learning of sensor network sensitive data detection and realize the optimization of sensor network sensitive data mining. The simulation results show that this method has high precision in mining sensitive data of WSN, and improves the reliability of WSN.

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The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Authors and Affiliations

  1. College of Computer and Information Engineering, Xiamen University of Technology, Xiamen, 361024, China

    Ruijin Lin & Yuanrong He

  2. College of Electrical Engineering and Automation, Xiamen University of Technology, Xiamen, 361024, China

    Min Xu

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  1. Ruijin Lin
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  2. Yuanrong He
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  3. Min Xu
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Correspondence to Ruijin Lin.

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Lin, R., He, Y. & Xu, M. Method of sensitive data mining based on Pan-Bull algebra. Wireless Netw 28, 2733–2741 (2022). https://doi.org/10.1007/s11276-021-02725-9

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