Practical Implications of Implementing Local Differential Privacy for Smart grids

2025-03-14

AI Summary

This paper addresses the practical deployment challenges of Local Differential Privacy (LDP) for real-time electricity consumption data privacy in smart grids. It systematically identifies four key bottlenecks: the absence of principled guidelines for selecting the privacy parameter ε; misalignment between theoretical LDP models and engineering implementations; insufficient understanding of how data scale affects utility; and limited robustness of existing LDP mechanisms against manipulation attacks. To bridge this gap, the authors construct a numerical data perturbation experimental framework, conducting sensitivity analysis of the privacy–utility trade-off and modeling multiple classes of manipulation attacks. This yields the first comprehensive characterization of LDP’s engineering constraints in smart grid settings. Key contributions include: (i) a practical, adaptive principle for ε selection; (ii) a quantitative evaluation paradigm for attack robustness; and (iii) a systematic benchmark and methodology to guide the design of lightweight, adaptive, and attack-resilient LDP mechanisms.

Technology Category

• Machine Learning: Privacy
• Search and Optimization: Local Search
• Natural Language Processing: Safety and Robustness

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments

Abstract

Recent smart grid advancements enable near-realtime 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 users and aggregators privacy. However, the practical implementation of a Local DP-based (LDP) privacy model for smart grids has its own challenges. In this paper, we discuss the challenges of implementing an LDP-based model for smart grids. We compare existing LDP mechanisms in smart grids for 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 data set on privacy and accuracy, and 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.

Problem

• Challenges in implementing Local Differential Privacy for smart grids.
• Comparison of LDP mechanisms for privacy preservation in numerical data.
• Impact of dataset size on privacy and accuracy in LDP models.

Innovation

• Local Differential Privacy for smart grids.
• Noise addition by users or third parties.
• Challenges in practical LDP implementation.