匿名大数据访问风险精准监测与仿真  

Accurate Monitoring and Simulation of Anonymous Big Data Access Risks

作　　者：陈云云 刘永山[2] CHEN Yun-yun;LIU Yong-shan(Department of Network Security,Shanxi Police College,Taiyuan Shanxi 030400,China;Department of Information Science and Engineering,Yanshan University,Qinhuangdao Hebei 066044,China)

机构地区：[1]山西警察学院网络安全保卫系,山西太原030400 [2]燕山大学信息科学与工程学院,河北秦皇岛066044

出　　处：《计算机仿真》2024年第5期450-454,共5页Computer Simulation

基　　金：山西省哲学社会科学课题:2022年党的十九届六中全会及省第十二次党代会专项课题(2022YD165)。

摘　　要：大数据已经成为一种经济资产,其包含大量的信息数据,服务器一旦遭到侵袭,就可能导致大量用户私人信息泄露。为实现大数据的安全共享与利用,提出基于深度对抗学习的匿名大数据访问风险监测研究。从主体、客体和环境三方面分析访问风险因素,主要包括访问时间、权限、数据敏感性、网络延时等因素;利用生成器和判别器生成深度对抗学习网络,将风险因素相关数据作为网络输入,提取风险特征;利用信息熵算法计算风险值,设定风险阈值,建立判别函数,利用该函数即可实现匿名大数据访问风险监测。实验结果表明,所提方法具备较强的特征学习能力,避免了监测过程中系统吞吐量过高,且监测结果准确。Big data has become an economic asset,which contains a large amount of information data.Once the server is attacked,massive private information will be leaked.In order to achieve the safe sharing and utilization of big data,this paper proposed a method of monitoring anonymous big data access risk based on deep confrontation learning.Firstly,we analyzed the access risk factors from three aspects:subject,object and environment,including access time,authority,data sensitivity and network delay.Then,we used the generator and discriminator to generate a deep confrontation learning network,and took the relevant data of risk factors as the network input to extract the risk characteristics.Moreover,we used the information entropy algorithm to calculate the risk value and set a risk threshold.Finally,we constructed a discriminant function to monitor the risk of anonymous big data access.Experimental results show that the proposed method has strong feature learning ability,avoiding high system throughput in the monitoring process.And the monitoring results are accurate.

关 键 词：深度对抗学习 匿名大数据 访问风险监测 信息熵算法 判别函数 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]