Defending against load monitoring in smart metering data through noise addition

Power providers have started replacing traditional electricity meters for Smart Meters, which can transmit current power consumption levels to the provider within short intervals. Based on this data, power providers can perform and improve many service activities such as differential tariffs and load predictions. However, this also threatens consumers' privacy, since specific activity or behavior patterns can be deduced from the finely granular meter readings. Consequently, a serious issue needs to be addressed: how to preserve the privacy of consumers while making the provision of certain services still possible? In this work, we evaluate a lightweight approach for privacy and utility based on noise addition. To validate the privacy using the approach, we perform and evaluate a Non-Intrusive Appliance Load Monitoring attack using real consumers' data. To validate the utility using the approach, we analyze and discuss the benefits that can be provided through the use of Smart Meters.