(SCC 2023) An Efficient Data Aggregation Solution for Smart Meters Based on Cloud-Edge Collaboration

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.