Blockchain and homomorphic encryption-based privacy-preserving data aggregation model in smart grid

Abstract

In recent years, rapid advancements in smart grid technology and smart metering systems have raised serious privacy concerns about the collection of customers’ real-time energy usage behaviors. Due to cybersecurity attacks and threats, data aggregation operations in a smart grid are challenging. The majority of existing techniques have high computation and communication costs and are still vulnerable to various security and privacy concerns. This paper proposes a deep learning and homomorphic encryption-based privacy-preserving data aggregation model to mitigate the negative impact of a flash workload on the accuracy of prediction models. The model also ensures a secure data aggregation process with low computational overhead. The proposed model is 80% more effective than the traditional approach in detecting smart meter manipulation, and the computation cost is 20% to 80% less than existing techniques. Thus, the proposed blockchain and homomorphic encryption-based data aggregation (BHDA) scheme shows a significant improvement in performance and privacy preservation with minimal computation overhead for data aggregation in smart grids.