基于隐私等级的能源大数据隐私保护方法

A Privacy Level-based Protection Approach for Energy Big Data

随着能源大数据的广泛应用,保护用户用电隐私成为一项重要任务。为避免隐私泄露和提高数据安全性,提出一种基于隐私等级的能源大数据隐私保护方法。首先,通过对原始数据集进行隐私等级标记和筛选,选择部分属性作为特征集,并利用皮尔逊相关系数选出与特征集相关性高的数据共同形成输出数据集。最后,针对电网数据的特点,采用非均匀的隐私保护策略对数据进行隐私保护,实现电力数据的匿名化。实验结果表明,所提出的方法能够根据数据的敏感程度和重要性提供更细粒度的隐私保护,有效提高发布数据的可用性和隐私性。

With the wide application of energy big data,protecting users'electricity privacy has emerged as a crucial undertaking.To prevent privacy breaches and enhance data security,a privacy protection method based on different privacy levels is proposed for energy big data.Firstly,the original dataset is labeled and filtered based on the privacy levels,and specific attributes are selected as the feature set.Subsequently,we utilize Pearson correlation coefficient to select data that has a high correlation with the feature set in order to form the output dataset.Finally,a non-uniform privacy protection strategy is used to protect the privacy of the data and anonymize the power data according to characteristics of grid data.Experimental results demonstrate that the proposed method can offer more precise privacy protection based on the sensitivity and importance of the data,effectively enhancing both usability and privacy of published data.