Data Clustering Method for Fault-Tolerant Privacy Protection of Smart Grid Based on BGN 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.