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A Secure Data Learning Scheme for Big Data Applications in the Smart Grid

This chapter proposes a secure data learning scheme for big data applications in the information and communication technology infrastructure of the smart grid. The proposed scheme allows multiple parties to find the predictive models from their overall data, while not revealing their own private data to one another at the same time. The chapter also proposes an associated secure scheme to guarantee the privacy of learning results during the information reassembly and value‐response process. The proposed scheme was targeted at improving the potential computation and security drawbacks of the centralized training task of big data applications. An evaluation is performed to verify the privacy of the training data set, as well as the accuracy of learning weights. The chapter presents a case study by adopting the proposed scheme to perform a regression problem and investigate the smart metering data in the smart grid.