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.
关键词
智慧报送信息/大数据泄露/风险容忍度/计量维度/敏感数据挖掘/BDM机制
Key words
intelligent submission information/big data leakage/risk tolerance/measurement dimension/sensitive data min-ing/BDM mechanism
维普
万方数据