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The privacy of user-related data is of paramount importance in Smart Grid scenarios: the increasing diffusion of Automatic Meter Reading (AMR) and the possibility to open the system to third party services has raised many concerns about the protection of personal data related to energy consumption. On one hand, information regarding the personal habits of the customers can be inferred by analyzing metering data; on the other hand, the detailed knowledge of consumption measurements is crucial for the timely management of energy distribution, provisioning, and forecasting. This work proposes a privacy-preserving infrastructure and communication protocols for the secure collection of metering data, which allow utilities and third parties to obtain time and/or space aggregated energy consumption measurements or disaggregated but pseudonymized meter readings, thus making them unable to associate the individual measurements with the identity of the customer (i.e., the meter) that generated the data. Two different design approaches have been considered: in the first, the aggregation/pseudonymization procedure is performed by a set of functional nodes placed in the domain of the Distribution System Operator (DSO), namely the Privacy Preserving Nodes (PPNs), which could be operated by independent parties or regulation authorities. However, this approach increases the complexity of the Smart Grid ecosystem. Therefore, an alternative solution requiring no additional nodes beyond those already present in the Smart Grid architecture is described: data aggregation can be performed in a distributed way by relying on communication Gateways located at the customer’s premises, thus realizing a peer-to-peer overlay network. The deployment of the communication flows between the nodes can be done either in a centralized or distributed fashion, using a variant of the Chord overlay protocol. Moreover, the work discusses how the proposed infrastructure can be integrated with data obfuscation techniques relying on noise addition, as inspired by the framework of differential privacy, and how it can be adapted to allow the coordination of energy consumption within a neighborhood by performing privacy-friendly load scheduling of deferrable domestic electrical appliances.
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