Lightweight privacy for smart metering data by adding noise

With a Smart Metering infrastructure, there are many motivations for power providers to collect high-resolution data of energy usage from consumers. However, this collection implies very detailed information about the energy consumption of consumers being monitored. Consequently, a serious issue needs to be addressed: how to preserve the privacy of consumers but making the provision of certain services still possible? Clearly, this is a tradeoff between privacy and utility. There are approaches for preserving privacy in various ways, but many of them affect the data usefulness or are computationally expensive. In this paper, we propose and evaluate a lightweight approach for privacy and utility based on the addition of noise. Furthermore, using real consumers' data, we discuss the influence of the technique in various Smart Grid scenarios. Finally, we also design and evaluate possible attacks to our solution.