基于Shannon信息熵与BP神经网络的隐私数据度量与分级模型

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

Abstract

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.

Key words: privacy security, information entropy, BP neural network, privacy metrics

CLC Number:

TP301

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.

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References 17

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维度（P）	一级要素（L）	二级要素（l）
隐私内容（P₁）	人口统计学信息（L₁）	证件（l₁₁）
		姓名（l₁₂）
		年龄（l₁₃）
		身高（l₁₄）
		体重（l₁₅）
		民族（l₁₆）
		籍贯（l₁₇）
	偏好信息（L₂）	兴趣特长（l₂₁）
		宗教信仰（l₂₂）
		政治倾向（l₂₃）
		购物偏好（l₂₄）
	财务信息（L₃）	信贷资料（l₃₁）
		收入（l₃₂）
		银行卡号（l₃₃）
		网银等虚拟账号（l₃₄）
	通信信息（L₄）	社交信息（l₄₁）
		家庭、工作地址（l₄₂）
		电子邮箱（l₄₃）
		手机、电话号码（l₄₄）
隐私状态（P₂）	静态信息（L₅）	家庭（l₅₁）
		工作（l₅₂）
		学历（l₅₃）
		社会关系（l₅₄）
	动态信息（L₆）	Cookies（l₆₁）
		位置移动信息（l₆₂）
		网络行为信息（l₆₃）
隐私详情（P₃）	精确信息（L₇）	时刻（l₇₁）
		坐标（l₇₂）
	模糊信息（L₈）	时间（l₈₁）
		地区（l₈₂）

样本隐私等级（P）	样本数量/个	误判数/个	误判率	误判偏差率
1	97	1	1.03%	2.80%
2	79	1	1.26%	3.44%
3	120	1	0.83%	2.27%
4	183	7	3.82%	12.95%
5	165	6	3.64%	9.88%
6	114	4	3.51	9.53%
7	98	1	1.02%	2.77%
8	144	1	0.69%	1.89%
1~8	1 000	22	2.20%	6.45%

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

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