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 techniques

such as directly deleting or replacing sensitive fields. However

these methods do not fully consider the inherent 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 prediction 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 privacy protection and data availability. The test results show that after using the method proposed in this paper for anonymizing power data

the iterative value of data information loss is 0.25

which has a relatively ideal anonymization effect.