Abstract
Smart meters are an important component of the smart grid, and the large-scale deployment of meters on the user side generates a large amount of data that brings huge expenses to the smart grid. In addition, attackers can monitor users’ electricity consumption based on the transmitted data, which poses a privacy threat. To solve these problems, this paper proposes a secure and efficient data aggregation scheme for smart meters based on cloud-side collaboration. Based on cloud-edge collaboration, the cloud server uses double Z-Score standardization to process the electricity consumption feature data, and at the same time uses improved Euclidean distance to obtain the number of clusters and classification results of the feature distinctions, and interacts with the edge device to respond. Based on the response information, the power provider generates confusion parameters and perturbation factors with classification information, which are sent to the meter for generating convergence values to minimize Gaussian noise and attached to the meter data to achieve the confusion processing of meter data. The aggregator receives the meter and cloud information for classification and aggregation and generates an aggregation signature to transmit the data. The results show that the scheme has high privacy security and operational efficiency.
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References
Orlando, M., et al.: A smart meter infrastructure for smart grid IoT applications. IEEE Internet Things J. 9(14), 12529–12541 (2022)
Gough, M.B., Santos, S.F., AlSkaif, T., Javadi, M.S., Castro, R., Catalão, J.P.S.: Preserving privacy of smart meter data in a smart grid environment. IEEE Trans. Ind. Inform. 18(1), 707–718 (2022)
Bakkar, M., Bogarra, S., Córcoles, F., Iglesias, J., Al Hanaineh, W.: Multi-layer smart fault protection for secure smart grids. IEEE Trans. Smart Grid 14(4), 3125–3135 (2023)
Kserawi, F., Al-Marri, S., Malluhi, Q.: Privacy-preserving fog aggregation of smart grid data using dynamic differentially-private data perturbation. IEEE Access 10, 43159–43174 (2022)
Prateek, K., Maity, S., Amin, R.: An unconditionally secured privacy-preserving authentication scheme for smart metering infrastructure in smart grid. IEEE Trans. Netw. Sci. Eng. 10(2), 1085–1095 (2023)
Zhang, X., Huang, C., Zhang, Y., Cao, S.: Enabling verifiable privacy-preserving multi-type data aggregation in smart grids. IEEE Trans. Depend. Secure Comput. 19(6), 4225–4239 (2022)
Khwaja, A.S., Anpalagan, A., Naeem, M., Venkatesh, B.: Smart meter data obfuscation using correlated noise. IEEE Internet Things J. 7(8), 7250–7264 (2020)
Alsharif, A., Nabil, M., Tonyali, S., Mohammed, H., Mahmoud, M., Akkaya, K.: Epic: efficient privacy-preserving scheme with ETOE data integrity and authenticity for AMI networks. IEEE Internet Things J. 6(2), 3309–3321 (2019)
Liu, S., Zhang, W., Xia, Z.: A distributed privacy-preserving data aggregation scheme for smart grid with fine-grained access control. J. Inf. Secur. Appl. 103118 (2022)
Yan, Y., Chen, Z., Varadharajan, V., Hossain, M.J., Town, G.E.: Distributed consensus-based economic dispatch in power grids using the Paillier cryptosystem. IEEE Trans. Smart Grid 12(4), 3493–3502 (2021)
Lucas Dias and Tiago Antonio Rizzetti: A review of privacy-preserving aggregation schemes for smart grid. IEEE Lat. Am. Trans. 19(7), 1109–1120 (2021)
Amin Mohammadali and Mohammad Sayad Haghighi: A privacy-preserving homomorphic scheme with multiple dimensions and fault tolerance for metering data aggregation in smart grid. IEEE Trans. Smart Grid 12(6), 5212–5220 (2021)
Zuo, X., Li, L., Peng, H., Luo, S., Yang, Y.: Privacy-preserving multidimensional data aggregation scheme without trusted authority in smart grid. IEEE Syst. J. 15(1), 395–406 (2021)
Qian, J., Cao, Z., Dong, X., Shen, J., Liu, Z., Ye, Y.: Two secure and efficient lightweight data aggregation schemes for smart grid. IEEE Trans. Smart Grid 12(3), 2625–2637 (2021)
Si, C., Shenglan, X., Wan, C., Chen, D., Cui, W., Zhao, J.: Electric load clustering in smart grid: methodologies, applications, and future trends. J. Mod. Power Syst. Clean Energy 9(2), 237–252 (2021)
Song, R., Yang, Y., Xue, Y., Zhang, P., Wang, C., Yang, L.: Research on clustering algorithm of user electricity behavior for identification of typical should scene. In: 2021 International Conference on Wireless Communications and Smart Grid (ICWCSG), pp. 213–216 (2021)
Bulivou, G., Reddy, K.G., Khan, M.G.M.: A novel method of clustering using a stochastic approach. IEEE Access 10, 117925–117943 (2022)
Wang, L., Liu, Y., Li, W., Zhang, J., Xu, L., Xing, Z.: Two-stage power user classification method based on digital feature portraits of power consumption behavior. Dianli Jianshe/Electric Power Constr. 70 (2022)
Acknowledgment
This work was supported in part by the National Natural Science Foundation of China under Grant 52177067, in part by the Hunan Provincial Natural Science Foundation of China under Grant 2023JJ30052.
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Xia, Z., Zhang, L., Sangaiah, A.K. (2024). An Efficient Data Aggregation Solution for Smart Meters Based on Cloud-Edge Collaboration. In: Luo, M., Zhang, LJ. (eds) Services Computing – SCC 2023. SCC 2023. Lecture Notes in Computer Science, vol 14211. Springer, Cham. https://doi.org/10.1007/978-3-031-51674-0_7
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DOI: https://doi.org/10.1007/978-3-031-51674-0_7
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