Privacy-preserving data analytics in advanced metering infrastructure utilizing TEE
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Date
2024-10
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University of New Brunswick
Abstract
With the rise of the smart grid, modern electrical grids now support two-way communication of energy and data, enabling system optimization through data analytics. However, this also introduces cybersecurity vulnerabilities. While research has focused on using smart meter data to enhance grid performance, security and privacy concerns remain underexplored. This research proposes a secure and privacy-preserving framework for smart meter data transmission and analytics. It combines lightweight cryptography and transport layer security for end-to-end data protection, while Intel SGX ensures private data processing in the cloud. We implemented an efficient LSTM model for energy consumption prediction, demonstrating the framework’s practicality. Our approach balances security, privacy, and functionality, allowing data owners to retain control while leveraging third-party cloud resources.