Privacy protection method of power metering data in clustering based on differential privacy

Xiaowen Yan, Yu Zhou, Fuxing Huang, Xiaofen Wang, Peisen Yuan
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Abstract

Power companies can use the power grid big data platform to cluster analysis of power metering data, which can improve the personalized service quality of power grid companies for different users and discover the power stealing behavior of users to protect the interests of power grid companies. However, in the cluster analysis of power measurement data, the privacy information of power users may also be disclosed. To defend the privacy information of power users, the article applies differential privacy technology to cluster analysis of power metering data to avoid power users’ privacy leakage. First, the article presents the attack model that exists in the cluster analysis of power metering data. Then, the article add Laplacian noise to the power metering data to defend against attacks in the cluster analysis of attackers. Next, to enhance the data availability of noise-added power measurement data in cluster analysis, the article limits noise distance based on the results of the cluster analysis. Experiments show that method proposed in article can guarantee the privacy information of power data during the cluster analysis of power metering data, and ensure the data quality of the power metering data after privacy protection.
基于差分隐私的聚类电能计量数据隐私保护方法
电力公司可以利用电网大数据平台对电力计量数据进行聚类分析,提高电网公司对不同用户的个性化服务质量,发现用户的偷电行为,维护电网公司的利益。然而,在电力测量数据的聚类分析中,也可能会泄露电力用户的隐私信息。为了保护电力用户的隐私信息,本文采用差分隐私技术对电力计量数据进行聚类分析,避免电力用户的隐私泄露。本文首先介绍了电能计量数据聚类分析中存在的攻击模型。然后,在攻击者的聚类分析中,在电能计量数据中加入拉普拉斯噪声来防御攻击。其次,为了提高加噪功率测量数据在聚类分析中的数据可用性,本文根据聚类分析结果对噪声距离进行了限制。实验表明,本文提出的方法能够在电能计量数据聚类分析过程中保证电能数据的隐私信息,保证电能计量数据隐私保护后的数据质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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