Metric and classification model for privacy data based on Shannon information entropy and BP neural network

Yihan YU; Yu FU; Xiaoping WU

doi:10.11959/j.issn.1000-436x.2018286

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Metric and classification model for privacy data based on Shannon information entropy and BP neural network

Papers | 更新时间：2024-06-05

- Metric and classification model for privacy data based on Shannon information entropy and BP neural network
- Journal on Communications Vol. 39, Issue 12, Pages: 10-17(2018)
- 作者机构：
  
  海军工程大学信息安全系，湖北武汉 430033
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(No.61100042);The National Social Science Foundation of China(No.15GJ003-201)
- DOI：10.11959/j.issn.1000-436x.2018286
  CLC： TP301
- Online First：2018-12，
  
  Published：25 December 2018
- 稿件说明：
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Yihan YU, Yu FU, Xiaoping WU. Metric and classification model for privacy data based on Shannon information entropy and BP neural network[J]. Journal on Communications, 2018, 39(12): 10-17.
DOI：

Yihan YU, Yu FU, Xiaoping WU. Metric and classification model for privacy data based on Shannon information entropy and BP neural network[J]. Journal on Communications, 2018, 39(12): 10-17. DOI： 10.11959/j.issn.1000-436x.2018286.

摘要

针对当前网络环境下由隐私数据识别困难问题所引出的隐私度量与分级需求，提出了一种基于 Shannon信息熵与BP神经网络的隐私数据度量与分级模型。该模型从3个维度建立了两层隐私度量要素，基于数据集本身，利用Shannon信息熵为二级隐私要素定权，并由此计算数据集中各条记录在一级隐私度量要素下的隐私量；利用BP神经网络在不预设度量权值的情况下，输出隐私数据分级结果。实验表明，该模型能够在极低的误判率和较小的误判偏差下实现对隐私数据的度量与分级。

Abstract

Aiming at the requirements of privacy metric and classification for the difficulty of private data identification in current network environment

a privacy data metric and classification model based on Shannon information entropy and BP neural network was proposed. The model establishes two layers of privacy metrics from three dimensions. Based on the dataset itself

Shannon information entropy was used to weight the secondary privacy elements

and the privacy of each record in the dataset under the first-level privacy metrics was calculated. The trained BP neural network was used to output the classification result of privacy data without pre-determining the metric weight. Experiments show that the model can measure and classify private data with low false rate and small misjudged deviation.

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references

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