Enabling Fine-Grained Aggregation With Fault Tolerance and Rich Statistical Analysis in Smart Grids

In smart grids, fine-grained data aggregation is a key technology for efficient power management and demand-side response. However, large-scale collection of high-precision electricity usage data poses significant privacy risks. Although various privacy-preserving fine-grained data aggregation schemes have been proposed, they still face several challenges, including limited support for statistical functions, poor stability, and lack of adaptability. To address these challenges, we propose a versatile fine-grained data aggregation (FPDA) scheme based on cloud-edge collaboration. First, we adopt the Qin Jiushao algorithm to process multidimensional data, and combine it with an enhanced Paillier encryption algorithm to securely aggregate the data while supporting multiple statistical functions. Second, we optimize the Boneh–Lynn–Shacham (BLS) signature scheme to enable batch verification of data integrity. Third, our scheme supports dynamic user management and fault tolerance, enabling scalability and robustness. The theoretical analysis shows that FPDA meets the security requirements of smart grids, while performance evaluations demonstrate its efficiency and practical applicability.