  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).
