Practical Privacy-Preserving Scheme With Fault Tolerance for Smart Grids
Chang Yuan, Jiliang Li, Ning Lu, Wenbo Shi, Zhou Su, Weizhi Meng
Abstract
In smart grid services, the leakage of crowdsourced consumption data on smart meters (SMs) poses potential risks of privacy disclosure and data misuse. Existing solutions, which rely on complex encrypted computations, are often impractical for resource-limited SMs due to their high computation and storage resource requirements. To address these challenges, this article proposes a practical privacy-preserving scheme with fault tolerance for smart grid services named 3PFT. In our scheme, we employ a masking approach that ensures user privacy preservation on SMs while consuming minimal resources. Unlike existing masking schemes, 3PFT provides fault tolerance, supports complex data analysis tasks, and mitigates vulnerabilities to key leakage attacks. To achieve these objectives, we incorporate a secret sharing technique into the masking approach, enabling the recovery of the master key using only a portion of the data. Additionally, we design a flexible data aggregation protocol for 3PFT, facilitating the execution of diverse data analysis missions, such as load forecasting, in smart grids. Furthermore, we introduce a negotiation-based key update method to enhance the protocol’s forward security and alleviate the additional overhead on SMs. Finally, we provide a rigorous proof of privacy preservation and fault tolerance for our scheme and validate its feasibility and effectiveness through extensive simulations.