A Distributed Privacy-Preserving Data Aggregation Scheme for Smart Grid with False Data Detection.

With rising global energy consumption, smart grids require integrating massive, scattered device data, making energy-efficient data aggregation techniques critical. Yet, they face key challenges: balancing data privacy, boosting operational efficiency, and ensuring accurate anomaly detection across more scenarios. The article presents a distributed data aggregation frame for smart grids that guarantees data integrity amid malicious nodes or adversarial attacks. The system, governed by rigorous privacy protections, enables the rapid identification of data that surpasses specified criteria while maintaining the accuracy and uniformity of normal data through integrity verification mechanisms. This is, to our knowledge, the inaugural solution that concurrently incorporates malicious threat models and meter data integrity verification in distributed data gathering environments while preserving anomaly detection functionalities. Assessments of security, computational burden, and privacy safeguarding efficacy indicate that the suggested method attains an optimal balance among these elements, fully fulfilling the stringent demands of contemporary smart grid applications for both performance and security.