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Practical Implications of Implementing Local Differential Privacy for Smart grids

Khadija Hafeez, Mubashir Husain Rehmani, Sumita Mishra, Donna OShea

TL;DR

The paper addresses privacy risks from granular smart-grid data and evaluates Local Differential Privacy (LDP) as a decentralized privacy approach. It analyzes two canonical LDP mechanisms—Laplace and Randomized Response (RR)—and reviews how they've been adapted to smart-grid contexts, highlighting limitations in parameter tuning, utility loss, and continual privacy loss over time. The authors identify practical challenges, including sensitivity selection, optimal privacy budget, aggregation-group effects, and vulnerability to manipulation, and propose future directions such as private sensitivity determination, mechanism optimization, and hybrid privacy solutions. The work provides a structured assessment to guide researchers and practitioners toward more practical LDP implementations for smart grids, with implications for privacy guarantees, data utility, and system integrity.

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.

Practical Implications of Implementing Local Differential Privacy for Smart grids

TL;DR

The paper addresses privacy risks from granular smart-grid data and evaluates Local Differential Privacy (LDP) as a decentralized privacy approach. It analyzes two canonical LDP mechanisms—Laplace and Randomized Response (RR)—and reviews how they've been adapted to smart-grid contexts, highlighting limitations in parameter tuning, utility loss, and continual privacy loss over time. The authors identify practical challenges, including sensitivity selection, optimal privacy budget, aggregation-group effects, and vulnerability to manipulation, and propose future directions such as private sensitivity determination, mechanism optimization, and hybrid privacy solutions. The work provides a structured assessment to guide researchers and practitioners toward more practical LDP implementations for smart grids, with implications for privacy guarantees, data utility, and system integrity.

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.

Paper Structure

This paper contains 24 sections, 5 figures, 1 table.

Table of Contents

  1. Introduction
  2. Preliminary Knowledge
  3. Laplace mechanism
  4. Randomized Response (RR)
  5. Comparative Analysis of Existing LDP Solutions for Smart Grids
  6. Laplace as LDP for SG
  7. RR mechanism as LDP for SG
  8. Challenges in Applying Theoretical LDP-based Models to Smart Grids Applications
  9. Lower Data Utility
  10. Vulnerability to Manipulation attacks
  11. Challenge of setting the Sensitivity Parameter
  12. Optimal Epsilon value
  13. Impact of Aggregation Group Size
  14. Continual Privacy Loss in Time Series Data
  15. Limitations of RR
  16. ...and 9 more sections

Figures (5)

  • Figure 1: Vulnerability of LDP mechanism to manipulation attack as attacker can not be identified due to noise.
  • Figure 2: Impact of selecting different privacy parameters on accuracy.
  • Figure 3: Impact of aggregation group size on MAPE.
  • Figure 4: Impact of decreasing granularity in data aggregation for smaller group aggregation.
  • Figure 5: Comparison of different LDP models for smart grids.