PRIVACY-PRESERVING METERING AND LOAD CONTROL IN SMART GRID

In the last decade, electrical grids have experienced an impressive evolution towards the so called Smart Grids, inspired by the Internet model. Indeed, the main driver for this evolution is the exploitation of communication networks for enabling the integration of renewable energy sources and making the energy demand closer to the time-varying production. While witnessing to the incremental deployment of Smart Grids and novel services for Smart Grids, we note a growing awareness on the risks associated to the collection of many sensitive users data. For example, monitoring the temporal trace of the energy consumption data of a given residential customer, may reveal a lot of information on user habits, presences at home, number of people living in the same apartment, and so on. In this context, the general goal of this thesis is designing some mechanisms for deploying Smart Grid services (and in particular, load control and profiling applications), while guaranteeing data privacy. For user privacy we mean ”the right to control who knows what about you and under what conditions”, which is obviously different from person to person and, most of all, it assumes meaning only in a given context. Our goal is defining which data need to be protected in different application scenarios and under which conditions, and which mechanisms can be introduced for building applications supporting an intrinsic Privacy by Design model. The thesis contribution is two-fold: first we discuss a multi-provider architecture in the energy market, suitable to extract information from high-frequency meter readings in an aggregate form, without giving access to each individual reading. In this context, we take advantage of existing cryptographic tools traditionally applied to ICT contexts for different problems, such as the anonymization of DNS queries. Second, we focus on the problem of matching energy demand and production in the Smart Grid, which is actually an hard problem because of the low predictability nature of renewable sources. We here propose and discuss a series of approaches based on load control (i.e. on the shaping of energy demand), where we gradually introduce mechanisms for protecting user data. The message we want to deliver in this thesis is that the pervasive adoption of ICT technologies for enabling novel services on Smart Grids (but also in more general Smart City scenarios) is creating novel risks for user data, that are often shared by different providers and monitored without a real user awareness. It is therefore important, as system designers, to consider these consequences during the same design phase of novel applications.