Research Area: Blockchain Technology
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.
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Author(s) Name: Parminder Singh, Mehedi Masud, M. Shamim Hossain, Avinash Kaur
Journal name: Computers & Electrical Engineering
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Publisher name: Elsevier
DOI: 10.1016/j.compeleceng.2021.107209
Volume Information: Volume 93, July 2021, 107209
