English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

The concept of differential privacy emerged as a strong notion to protectdatabase privacy in an untrusted environment. Later on, researchers proposedseveral variants of differential privacy in order to preserve privacy incertain other scenarios, such as real-time cyber physical systems. Since then,differential privacy has rigorously been applied to certain other domains whichhas the need of privacy preservation. One such domain is decentralizedblockchain based smart metering, in which smart meters acting as blockchainnodes sent their real-time data to grid utility databases for real-timereporting. This data is further used to carry out statistical tasks, such asload forecasting, demand response calculation, etc. However, in case if anyintruder gets access to this data it can leak privacy of smart meter users. Inthis context, differential privacy can be used to protect privacy of this data.In this chapter, we carry out comparison of four variants of differentialprivacy (Laplace, Gaussian, Uniform, and Geometric) in blockchain based smartmetering scenario. We test these variants on smart metering data and carry outtheir performance evaluation by varying different parameters. Experimentaloutcomes shows at low privacy budget ($\varepsilon$) and at low readingsensitivity value ($\delta$), these privacy preserving mechanisms provide highprivacy by adding large amount of noise. However, among these four privacypreserving parameters Geometric parameters is more suitable for protecting highpeak values and Laplace mechanism is more suitable for protecting low peakvalues at ($\varepsilon$ = 0.01).

Performance Evaluation of Differential Privacy Mechanisms in Blockchain based Smart Metering