Secure data analytics for smart grid systems in a sustainable smart city: Challenges, solutions, and future directions

Abstract

A smart city requires an intelligent infrastructure to improve the quality of life with sustainable environment for its citizens. There is an exponential demand for efficient, secure, reliable, and uninterrupted electricity supply, so there is a need for an intelligent grid, which uses Information and Communications Technology (ICT) to optimize the generation, circulation, and ingestion of electricity. Thus, Smart Grid (SG) acts as an intelligent grid, which plays an important role in the overall growth of any smart city. Further, the Big Data (BD) generated from SG, provides noteworthy information that could significantly benefit different applications of SG, such as demand response and load profiling. However, an insecure technique for decision-making may lead to the breach of SG data where hackers gained full access to consumer data. On the contrary, a secure technique for decision-making can provide satisfaction to all the stakeholders, including consumers and utility providers. Motivated from these facts, this paper presents a comprehensive literature survey and analysis of state-of-the-art proposals for Secure Data Analytics (SDA) in the SG system. However, to achieve SDA for the SG systems is one of the critical tasks. The existing research and development endeavors not fully exploited the SDA in the SG system. In this paper, we discuss the distinctive nature of SDA and its complexity over the SG data. A detailed taxonomy abstracted into a novel process model, which highlights various research challenges such as secure data collection and preprocessing, secure load data processing and storage, load prediction, load management and analysis, data security and privacy issues, and data communication. Finally, a case study is presented to demonstrate the process model.