Differential Privacy Preservation for Smart Meter Systems

Springer International Publishing
Publication Type:
Conference Proceeding
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2020, 11944 LNCS, pp. 669-685
Issue Date:
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With the rapid development of IoT and smart homes, smart meters have received extensive attention. The third-party applications, such as smart home controlling, dynamic demand-response, power monitoring, etc., can provide services to users based on consumption data of household electricity collected from smart meters. With the emergence of non-intrusive load monitoring, privacy issues from the data of smart meters become more and more severe. Differential privacy is a recognized concept that has become an important standard of privacy preservation for data with personal information. However, the existing privacy protection methods for the data of smart meters that are based on differential privacy sacrifices the actual energy consumption to protect the privacy of users, thus affecting the charging of power suppliers. To solve this problem, we propose a group-based noise adding method, so as to ensure the correct electricity billing. The experiments with two real-world data sets demonstrate that our approach can not only provide a strict privacy guarantee but also improve performance significantly.
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