LiPSG: Lightweight Privacy-Preserving Q-Learning-Based Energy Management for the IoT-Enabled Smart Grid
Zhuzhu Wang, Yang Liu, Zhuo Ma, Ximeng Liu, Jianfeng Ma
Abstract
As the largest Internet-of-Things (IoT) deployment in the world, the smart grid implements extremely reduction in the energy dissipation for the operation of the smart city. However, the electricity data produced by the smart grid contain massive sensitive information, such as dispatching instructions and bills. The data are always revealed to cloud servers in the plaintext format for the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning-based energy strategy making, which gives the chance for the adversary to abuse the user data. Therefore, in this article, we propose a lightweight privacy-preserving <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning framework (LiPSG) for the energy management strategy making of the smart grid. Before being sent to the control center, the electricity data of each power supply region in LiPSG are first split into uniformly random secret shares. During completion of the computation task of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning, the data are kept in the random share format all the time to avoid the data privacy disclosure. The computation feature is implemented by the newly proposed additive secret-sharing protocols. The edge computing technology is also deployed to further improve efficiency. Moreover, comprehensive theoretic analysis and experiments are given to prove the security and efficiency of LiPSG. Compared with the existing privacy-preserving schemes of the smart grid, LiPSG first provides a general <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning-based privacy-preserving power strategy making architecture with high efficiency and low-performance loss.