Privacy-Preservation Mechanisms for Smart Energy Metering Devices Based on Differential Privacy

Abstract

This paper investigates the application of differential privacy to the Smart Grid. Due to the nature of the Smart Grid, data drawn from it can be used to predict individuals behaviours and actions inside their homes which is a huge privacy violation. We show an attack that is able to predict behaviour with up to 94.3% precision. Differential privacy masks the distinguishable features of the Smart Grid data, protecting users privacy and making their behaviour unpredictable. We propose a method which applies differential privacy without splitting the billing, and charges concurrently while keeping the cost unchanged.