Data Clustering Method for Fault‐Tolerant Privacy Protection of Smart Grid Based on <scp>BGN</scp> Homomorphic Encryption Algorithm

ABSTRACT To address the issues of lengthy encryption time, low clustering accuracy, and poor performance in existing privacy‐preserving clustering methods for grid data, this paper proposes a fault‐tolerant data clustering method for smart grids based on the Boneh‐Goh‐Nissim (BGN) homomorphic encryption algorithm. A system architecture is constructed comprising a cloud server layer, a fog node layer, a smart meter layer, and a trusted third party. Private data collected by smart meters are first denoised using robust locally weighted regression. The preprocessed data are then encrypted with the BGN algorithm. K‐means clustering is applied to mine valuable data, with decryption performed at the cloud server layer. A two‐layer fault‐tolerant mechanism—utilizing secure channels to trusted organizations—is implemented across both the cloud servers and smart meters to ensure robust privacy‐preserving clustering. Experimental results in a scenario with 100 smart meters show the proposed method requires only 10 s for encryption, significantly less than conventional methods. Clustering performance is excellent: valid and invalid data are clearly distinguished, the deviation between actual and computed cluster centers is small, and clustering accuracy reaches 89.54%. Furthermore, by integrating a Shamir (3,5) threshold secret sharing scheme and a redundancy strategy for fog node data storage, the method maintains continuous operation and data integrity despite server failures or meter data loss. The data recovery rate exceeds 98%, with less than 4% loss in clustering accuracy. These results demonstrate the method achieves efficient encryption, high clustering accuracy, and strong fault tolerance, effectively enhancing private information security in smart grids.