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

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Information Systems Security and Privacy (ICISSP 2015)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 576))

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Abstract

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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Notes

  1. 1.

    http://www.en-trust.at/downloads/sgam-toolbox/.

  2. 2.

    http://www.w3.org/TR/owl-features/.

  3. 3.

    http://www.w3.org/TR/owl2-manchester-syntax/.

  4. 4.

    http://www.w3.org/TR/sparql11-query/.

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Acknowledgements

The financial support of the Josef Ressel Center by the Austrian Federal Ministry of Economy, Family and Youth and the Austrian National Foundation for Research, Technology and Development is gratefully acknowledged. Funding by the Austrian Marshall Plan Foundation is gratefully acknowledged. The authors would like to thank Norbert Egger for his contribution to the prototypical implementation. Funding by the Federal State of Salzburg is gratefully acknowledged.

This material is based upon work supported by the United States Department of Energy under Award Number number DE-OE0000192, and the Los Angeles Department of Water and Power (LA DWP). The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof, the LA DWP, nor any of their employees.

Author information

Authors and Affiliations

  1. Josef Ressel Center for User-Centric Smart Grid Privacy, Security and Control, Salzburg University of Applied Sciences, Urstein Sued 1, 5412, Puch/Salzburg, Austria

    Fabian Knirsch, Dominik Engel & Cristian Neureiter

  2. Department of Computer Sciences, Salzburg University, Jakob-Haringerstr. 2, Salzburg, Austria

    Fabian Knirsch

  3. Ming-Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, USA

    Marc Frincu & Viktor Prasanna

Authors
  1. Fabian Knirsch
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  2. Dominik Engel
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  3. Cristian Neureiter
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  4. Marc Frincu
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  5. Viktor Prasanna
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Corresponding author

Correspondence to Fabian Knirsch .

Editor information

Editors and Affiliations

  1. MODESTE/ESEO, Angers, France

    Olivier Camp

  2. SBA Research, Wien, Austria

    Edgar Weippl

  3. Supélec, Cesson, France

    Christophe Bidan

  4. Université de Montréal, Montreal, Québec, Canada

    Esma Aïmeur

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Knirsch, F., Engel, D., Neureiter, C., Frincu, M., Prasanna, V. (2015). Privacy Assessment of Data Flow Graphs for an Advanced Recommender System in the Smart Grid. In: Camp, O., Weippl, E., Bidan, C., Aïmeur, E. (eds) Information Systems Security and Privacy. ICISSP 2015. Communications in Computer and Information Science, vol 576. Springer, Cham. https://doi.org/10.1007/978-3-319-27668-7_6

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  • DOI: https://doi.org/10.1007/978-3-319-27668-7_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27667-0

  • Online ISBN: 978-3-319-27668-7

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