Performance Evaluation of Differential Privacy Mechanisms in Blockchain based Smart Metering

TL;DR

This paper compares four differential privacy mechanisms in blockchain-based smart metering, evaluating their effectiveness in protecting user data privacy while maintaining data utility under various parameters.

Contribution

It provides a comparative analysis of Laplace, Gaussian, Uniform, and Geometric differential privacy variants specifically for blockchain smart metering data.

Findings

Geometric mechanism better protects high peak values.

Laplace mechanism more effective for low peak values.

High privacy achieved at low epsilon and delta values.

Abstract

The concept of differential privacy emerged as a strong notion to protect database privacy in an untrusted environment. Later on, researchers proposed several variants of differential privacy in order to preserve privacy in certain other scenarios, such as real-time cyber physical systems. Since then, differential privacy has rigorously been applied to certain other domains which has the need of privacy preservation. One such domain is decentralized blockchain based smart metering, in which smart meters acting as blockchain nodes sent their real-time data to grid utility databases for real-time reporting. This data is further used to carry out statistical tasks, such as load forecasting, demand response calculation, etc. However, in case if any intruder gets access to this data it can leak privacy of smart meter users. In this context, differential privacy can be used to protect privacy of this data. In this chapter, we carry out comparison of four variants of differential privacy (Laplace, Gaussian, Uniform, and Geometric) in blockchain based smart metering scenario. We test these variants on smart metering data and carry out their performance evaluation by varying different parameters. Experimental outcomes shows at low privacy budget ($\varepsilon$) and at low reading sensitivity value ($\delta$), these privacy preserving mechanisms provide high privacy by adding large amount of noise. However, among these four privacy preserving parameters Geometric parameters is more suitable for protecting high peak values and Laplace mechanism is more suitable for protecting low peak values at ($\varepsilon$ = 0.01).