Practical Implications of Implementing Local Differential Privacy for Smart Grids

Recent smart grid advancements enable nearreal- time reporting of electricity consumption, raising concerns about consumer privacy. Differential privacy (DP) has emerged as a viable privacy solution, where a calculated amount of noise is added to the data by a trusted third party, or individual users perturb their information locally and only send the randomized data to an aggregator for analysis, safeguarding user's and aggregator's privacy. However, the practical implementation of a local DP-based (LDP) privacy model for smart grids has its own challenges. In this article, we discuss the challenges of implementing an LDP-based model for smart grids. We compare existing LDP mechanisms in smart grids for the privacy preservation of numerical data and discuss different methods for selecting privacy parameters in the existing literature, their limitations, and the non-existence of an optimal method for selecting the privacy parameters. We also discuss the challenges of translating theoretical models of LDP into a practical setting for smart grids for different utility functions, the impact of the size of the data set on privacy and accuracy, and the vulnerability of LDP-based smart grids to manipulation attacks. Finally, we discuss future directions in research for better practical applications in LDP-based models for smart grids.