Accurate Monitoring and Simulation of Anonymous Big Data Access Risks
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 con-frontation learning network,and took the relevant data of risk factors as the network input to extract the risk character-istics.Moreover,we used the information entropy algorithm to calculate the risk value and set a risk threshold.Final-ly,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.
Deep confrontation learningAnonymous big dataAccess risk monitoringInformation entropy algo-rithmDiscriminant function
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