Abstract
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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Acknowledgements
This work is supported in part by National Natural Science Foundation of China under grant No. 61772221 and in part by the Shenzhen Fundamental Research Program under Grant JCYJ20170413114215614.
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Wu, J., Qiang, W., Zhu, T., Jin, H., Xu, P., Shen, S. (2020). Differential Privacy Preservation for Smart Meter Systems. In: Wen, S., Zomaya, A., Yang, L. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2019. Lecture Notes in Computer Science(), vol 11944. Springer, Cham. https://doi.org/10.1007/978-3-030-38991-8_44
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