Privacy in Smart Grids

Electricity grids are evolving to “smart grids”. Smart grids employ communication of supply and demand between participants of the grid to achieve better efficiency, availability, resilience, etc. than traditional grids. Consumer households are connected to the smart grid using smart metering and demand response. Smart metering communicates the household’s electricity consumption at high resolution to the smart grid. Demand response enables service providers to affect the household’s eletricity demand by the means of different incentives. The communication of a household’s electricity consumption leaks information about the household and its inhabitants. Thus, it poses the inhabitants’ privacy at risk. Existing smart grid deployments address this conflict between utility and privacy unsatisfactorily. They either accept the privacy risk or forfeit utility. This dissertation provides an alternative solution to mitigate this conflict. The solution retains utility of the smart grid without compromising consumer privacy. In particular, this dissertation first identifies that the smart grid poses a privacy risk to consumers. This privacy risk originates from the collection and central storage of consumption traces in naive implementations. Once consumption data is centrally stored there are few techniques one can employ to protect it from various attacks. The only viable, i.e. general and utility-preserving, technique is pseudonymization of stored data in combination with proper access control. However, this dissertation shows experimentally on real smart metering data that pseudonymization of consumption data is not effective. This result drives the main idea of the remainder: Consumption data must not be collected, i.e. leave the household, in the first place. This dissertation provides three privacypreserving protocols that support essential smart grid computations (aggregation, billing, compliance verification) on consumption data, respectively. For each computation the respective protocol only transports the minimal amount of required information out of the household. Furthermore, to the service provider that interacts with the household, they guarantee computation results that are as trustworthy as those from non-private protocols.