RPMDA: Robust and Privacy-Enhanced Multidimensional Data Aggregation Scheme for Fog-Assisted Smart Grids

Jingwei Liu, Haoze Wang, Jiajia Bao, Rong Sun, Xiaojiang Du, Mohsen Guizani

Abstract: The increasing demand for intelligent management in modern power systems has emphasized the importance of smart grids, which facilitate real-time analysis and management through data aggregation. Fog computing provides efficient data processing and low-latency transmission for data aggregation. However, fog-assisted smart grids still face significant challenges, including privacy leakage, calculation limitations, and system stability issues. In response to these obstacles, we propose a robust and privacy-enhanced multidimensional data aggregation (RPMDA) scheme. Specifically, the Chinese remainder theorem is used to improve the efficiency of processing multidimensional data, combined with an innovative double-masking method to cope with secure data aggregation. For the purpose of reliable authentication, a conditional anonymous certificateless signature algorithm is designed in RPMDA, where the pseudonym generation mechanism ensures the conditional anonymity of smart meters (SMs). Besides, our scheme incorporates robustness, ensuring that the aggregated results remain unaffected even if SMs malfunction. Compared to the existing solutions, RPMDA shows superior performance while meeting security requirements.