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Big Data-Based User Data Intelligent Encryption Method in Electronic Case System

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Advanced Hybrid Information Processing (ADHIP 2019)

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Abstract

When the user data of the conventional electronic case system was encrypted, there was a shortage of low analysis accuracy. To this end, an intelligent encryption method for user data of the electronic case system based on big data was proposed. Introducing the big data technology, building a framework for intelligent encryption of user data of electronic case system, and realizing the construction of intelligent encryption of user data of electronic case system; Relying on the determination of the data intelligent encryption algorithm, the electronic case system model was embedded to realize the intelligent encryption of the user data of the electronic case system. The experimental data showed that the proposed big data modeling and analysis method was 61.64% more accurate than the conventional method, which was suitable for intelligent encryption of user data in electronic case system.

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Authors and Affiliations

  1. School of Railway Operation and Management, Hunan Railway Professional Technology College, Zhuzhou, China

    Xin Liu

Authors
  1. Xin Liu
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Correspondence to Xin Liu .

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Editors and Affiliations

  1. Nanjing University, Nanjing, China

    Guan Gui

  2. Harbin Engineering University, Harbin, China

    Lin Yun

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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Liu, X. (2019). Big Data-Based User Data Intelligent Encryption Method in Electronic Case System. In: Gui, G., Yun, L. (eds) Advanced Hybrid Information Processing. ADHIP 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 301. Springer, Cham. https://doi.org/10.1007/978-3-030-36402-1_32

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  • DOI: https://doi.org/10.1007/978-3-030-36402-1_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-36401-4

  • Online ISBN: 978-3-030-36402-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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