低碳背景下数字电网大数据安全匿名化处理技术研究OA

In the context of low-carbon development,the secure anonymization of big data in digital power grids faces technical challenges.Traditional methods mainly rely on simple data anonymization or generalization tech-niques,such as directly deleting or replacing sensitive fields.However,these methods do not fully consider the in-herent correlation between data and the differences in attribute sensitivity,resulting in a significant decrease in the usability of processed data.In this regard,this paper proposes research on the secure anonymization processing technology of big data in digital power grids under the low-carbon background.The random forest algorithm is used to predict the sensitivity values of power grid data attributes,multiple decision trees are constructed and their pre-diction results are integrated to effectively capture nonlinear relationships and complex patterns in the data,thereby accurately identifying the sensitivity of each attribute.Subsequently,the K-means clustering algorithm is used to cluster the attributes of the power grid big data,grouping attributes with similar sensitive characteristics into one category.On this basis,the K-anonymity algorithm is used for generalization and suppression operations to conceal the attribute clusters of power grid big data to varying degrees,thereby balancing the relationship between data pri-vacy protection and data availability.The test results show that after using the method proposed in this paper for an-onymizing power data,the iterative value of data information loss is 0.25,which has a relatively ideal anonymiza-tion effect.