Privacy Assessment of Data Flow Graphs for an Advanced Recommender System in the Smart Grid

The smart grid paves the way to a number of novel applications that benefit a variety of stakeholders including network operators, utilities and customers as well as third party developers such as electric vehicle manufacturers. In order to roll out an integrated and connected grid that combines energy and information flows and that fosters bidirectional communications, data and information needs to be exchanged and aggregated. However, collecting, transmitting and combining information from different sources has some severe privacy impacts on customers. Furthermore, customer acceptance and participation is the key to many smart grid applications such as demand response. In this paper we present (i) an approach for the model-based assessment of privacy in the smart grid that draws on a formal use case description (data flow graphs) and allows to asses the privacy impact of such use cases at early design time; and (ii) based on that assessment we introduce a recommender system for smart grid applications that allows users and vendors to make informed decisions on the deployment, use and active participation in smart grid use cases with respect to their individual privacy.

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