Measurement Algorithm for Risk Tolerance of Big Data Leakage of Intelligent Submission Information
In order to accurately calculate the risk tolerance of data leakage of discipline inspection information and ensure the se-curity of discipline inspection information,this paper researches the measurement algorithm of risk tolerance of big data leakage of intelligent submission information.Starting from the perspectives of disciplinary inspection information data collection and association rule mining,a disciplinary inspection reporting information big data leakage perception architecture is constructed.Based on this architecture,the sensitivity of disciplinary inspection reporting information big data is calculated from three as-pects:the content of disciplinary inspection reporting information big data leakage,the objects of disciplinary inspection infor-mation big data leakage,and how the leaked disciplinary inspection information big data is used.Grey correlation analysis and optimal basis point method are used to obtain the probability of leakage and analyze the risk tolerance.The optimized BDM mechanism is used to complete the conversion between leakage risk tolerance and compensation willingness,and the risk toler-ance value is determined based on the typical inverse correlation between compensation willingness and sensitive data leakage risk tolerance.Experimental results show that the algorithm can accurately mine sensitive data in discipline inspection informa-tion,high-precision risk tolerance value is obtained,which improves the security of big data of discipline inspection informa-tion.
intelligent submission informationbig data leakagerisk tolerancemeasurement dimensionsensitive data min-ingBDM mechanism
万方数据
维普
