通信学报2018，Vol.39Issue(12)：10-17,8.DOI:10.11959/j.issn.1000-436x.2018286
基于Shannon信息熵与BP神经网络的隐私数据度量与分级模型
Metric and classification model for privacy data based on Shannon information entropy and BP neural network
摘要
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.关键词
隐私安全/信息熵/BP神经网络/隐私度量Key words
privacy security/ information entropy/ BP neural network/ privacy metrics分类
信息技术与安全科学引用本文复制引用
俞艺涵,付钰,吴晓平..基于Shannon信息熵与BP神经网络的隐私数据度量与分级模型[J].通信学报,2018,39(12):10-17,8.基金项目
国家自然科学基金资助项目(No.61100042) （No.61100042）
国家社会科学基金资助项目(No.15GJ003-201) （No.15GJ003-201）
