Review of results on smart‐meter privacy by data manipulation, demand shaping, and load scheduling

Simple analysis of energy consumption patterns recorded by smart meters can be used to deduce household occupancy. With access to higher‐resolution smart‐meter readings, we can infer more detailed information about the household including the use of individual electric appliances through non‐intrusive load monitoring techniques. The extent of privacy concerns caused by smart meters has proved to an obstacle in the roll‐out of smart meters in some countries. This highlights the need for investigating smart‐meter privacy. Mechanisms for ensuring smart‐meter privacy fall in broad categories of data manipulation, demand shaping, and load scheduling. In smart‐meter data manipulation, the smart meter collects real, potentially high‐resolution data about the energy consumption within the house. This data is then manipulated before communication with to utility providers and retailers. The manipulation could be non‐stochastic, such as aggregation, binning, and down‐sampling, or stochastic, such as additive noise. In demand shaping and load scheduling, smart‐meter readings are communicated without any interference but the consumption is manipulated by renewable energy sources, batteries, or shifting loads to render non‐intrusive load monitoring ineffective. In this study, the author reviews these approaches and presents several methods relying on homomorphic encryption, differential privacy, information theory, and statistics for ensuring privacy.