低碳背景下数字电网大数据安全匿名化处理技术研究

罗程; 鲁玕; 钟德龙; 李婷; 史云

罗程,鲁玕,钟德龙,李婷,史云.低碳背景下数字电网大数据安全匿名化处理技术研究[J].电测与仪表,2026,63(1):34-44.
LUO Cheng,LU Gan,ZHONG Delong,LI Ting,SHI Yun.Research on secure anonymization processing techniques for digital power grid big data under low carbon background[J].Electrical Measurement & Instrumentation,2026,63(1):34-44.

低碳背景下数字电网大数据安全匿名化处理技术研究

Research on secure anonymization processing techniques for digital power grid big data under low carbon background

DOI：10.19753/j.issn1001-1390.2026.01.004

中文关键词: 数字电网数据匿名化处理数据属性敏感度 K-匿名化算法

英文关键词:digital power grid data, anonymization processing, data attribute, sensitivity, K-anonymization algorithm

基金项目:南方电网建设项目（编号072900HK24030026）

作者中文名	作者英文名	单位
罗程	LUO Cheng	海南电网有限责任公司, 海口 570204
鲁玕	LU Gan	海南电网有限责任公司, 海口 570204
钟德龙	ZHONG Delong	海南电网有限责任公司,海口 570204
李婷	LI Ting	海南电网有限责任公司, 海口 570204
史云	SHI Yun	海南电网有限责任公司,海口 570204

摘要点击次数: 47

中文摘要:

在低碳发展背景下，数字电网大数据的安全匿名化处理面临技术挑战。传统方法主要依赖简单的数据脱敏或泛化技术，如直接删除或替换敏感字段，然而这类方法因未充分考虑数据间的内在关联性与属性敏感度的差异性，导致处理后的数据在可用性方面显著降低。对此，文章提出低碳背景下数字电网大数据安全匿名化处理技术研究。采用随机森林算法对电网数据的属性敏感值进行预测，通过构建多个决策树并集成其预测结果，有效捕捉数据中的非线性关系与复杂模式，从而准确识别出各属性的敏感程度。随后，运用K-means 聚类算法对电网大数据的属性进行集群划分，将具有相似敏感特性的属性归为一类。在此基础上，采用 K-匿名化算法的泛化和抑制操作，对电网大数据属性集群进行不同程度地隐匿处理，从而平衡数据隐私保护与数据可用性之间的关系。测试结果表明，采用所提出的方法进行电力数据匿名化处理后，数据信息损失度迭代值为0.25，具备较为理想的匿名化效果。

英文摘要:

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.

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