Power automation sensitive data classification algorithm based on trusted privacy computing technology

Abstract

Traditional power automation sensitive data classification algorithms are prone to generalization errors, resulting in low geometric mean of data classification. Therefore, it is necessary to design a sensitive data classification algorithm for power automation based on trusted privacy computing technology. A power automation sensitive data classification model was constructed using trusted privacy computing technology, and an optimization framework for power automation sensitive data classification was generated, achieving the classification of power automation sensitive data. The experimental results show that the designed power automation sensitive data classification algorithm based on trusted privacy technology has a high geometric mean of data classification, proving that the designed power automation sensitive data classification algorithm has good classification performance, effectiveness, and certain application value.