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Repairing an aggregation-based smart metering system

  • Ricard Garra
  • Dominik Leibenger
  • Josep M. Miret
  • Francesc SebéEmail author
Regular contribution
  • 18 Downloads

Abstract

Smart meters inform the electricity suppliers about the consumption of their clients in short intervals. Fine-grained electricity consumption information is highly sensitive as it has been proven to permit to infer people’s habits, for instance, the time they leave or arrive home. Hence, appropriate measures have to be taken to preserve clients’ privacy in smart metering systems. In this paper, we first analyze a recent proposal by Busom et al. (Comput Commun 82:95–101, 2016) and show how a corrupted substation is able to get the individual reading of any arbitrarily chosen smart meter without requiring the collaboration of any other party. After that, we propose a way to fix the mentioned security flaw which is based on adding an additional step in which the substation proves that it has properly followed all the protocol steps. Our solution is analyzed and shown to be computationally feasible for realistic parameter choices.

Keywords

Encryption Homomorphism Privacy Smart metering 

Notes

Acknowledgements

This study was funded by the European Regional Development Fund of the European Union in the scope of the “Programa Operatiu FEDER de Catalunya 2014–2020” (project number COMRDI16-1-0060), by the Spanish Ministry of Science, Innovation and Universities (Project No. MTM2017-83271-R), and by the Federal Ministry for Economic Affairs and Energy of Germany in the SINTEG project DESIGNETZ (Project No. 03SIN224).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

References

  1. 1.
    Hart, G.W.: Nonintrusive appliance load monitoring. Proc. IEEE 80, 1870–1891 (1992)CrossRefGoogle Scholar
  2. 2.
    Rubio, J.E., Alcaraz, C., López, J.: Recommender system for privacy-preserving solutions in smart metering. Pervas. Mob. Comput. 41, 205–218 (2017).  https://doi.org/10.1016/j.pmcj.2017.03.008 CrossRefGoogle Scholar
  3. 3.
    Busom, N., Petrlic, R., Sebé, F., Sorge, C., Valls, M.: Efficient smart metering based on homomorphic encryption. Comput. Commun. 82, 95–101 (2016).  https://doi.org/10.1016/j.comcom.2015.08.016 CrossRefGoogle Scholar
  4. 4.
    Stegelmann, M., Kesdogan, D.: GridPriv: a smart metering architecture offering \(k\)-anonymity, 11th Trust, Security and Privacy in Computing and Communications, TrustCom’12, pp. 419–426 (2012).  https://doi.org/10.1109/TrustCom.2012.170
  5. 5.
    Rial, A., Danezis, G.: Privacy-preserving smart metering, Technical Report MSR-TR-2010-150, Microsoft Research (2010)Google Scholar
  6. 6.
    Molina-Markham, A., Shenoi, P., Fu, K., Cecchet, E., Irwin, D.: Private memoirs of a smart meter, 2nd ACM W. on Embedded Sensing Systems for Energy-Efficiency in Building, pp. 61–66 (2010)Google Scholar
  7. 7.
    Efthymiou, C., Kalogridis, G.: Smart grid privacy via anonymization of smart metering data. In: 1st IEEE International Conference on Smart Grid Communications (SmartGridComm), pp. 238–243 (2010).  https://doi.org/10.1109/SMARTGRID.2010.5622050
  8. 8.
    Finster, S., Baumgart, I.: Pseudonymous smart metering without a trusted third party, 12th Trust, Security and Privacy in Computing and Communications, TrustCom’13, pp. 1723–1728 (2013).  https://doi.org/10.1109/TrustCom.2013.234
  9. 9.
    Petrlic, R.: A privacy-preserving concept for smart grids. Sicherheit in vernetzten Systemen 18, B1–B14 (2010)Google Scholar
  10. 10.
    Ács, G., Castelluccia, C.: I have a dream! (differentially private smart metering), Information Hiding (LNCS), vol. 6958. Springer, Berlin, pp. 118–132 (2011).  https://doi.org/10.1007/978-3-642-24178-9_9
  11. 11.
    Bohli, J.-M., Sorge, C., Ugus, O.: A privacy model for smart metering. In: Proceedings of the 1st IEEE International W. on Smart Grid Communications (in conjunction with IEEE ICC) (2010)Google Scholar
  12. 12.
    García, F., Jacobs, B.: Privacy-friendly energy-metering via homomorphic encryption. In: Proceedings of 6th International Conference on Security and Trust Management (LNCS), vol. 6710. Springer, Berlin, pp. 226–238 (2011).  https://doi.org/10.1007/978-3-642-22444-7_15
  13. 13.
    Kursawe, K., Danezis, G., Kohlweiss, M.: Privacy-friendly aggregation for the smart-grid. In: Proceedings of Privacy Enhancing Technologies (LNCS), vol. 6794. Springer, Berlin, pp. 175–191 (2011).  https://doi.org/10.1007/978-3-642-22263-4_10
  14. 14.
    Castelluccia, C., Mykletun, E., Tsudik, G.: Efficient aggregation of encrypted data in wireless sensor networks, Proc. of The Second Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services, pp. 109–117 (2005).  https://doi.org/10.1145/1525856.1525858
  15. 15.
    Gómez Mármol, F., Sorge, C., Petrlic, R., Ugus, O., Westhoff, D., Martínez Pérez, G.: Privacy-enhanced architecture for smart metering. Int. J. Inf. Secur. 12(2), 67–82 (2013).  https://doi.org/10.1007/s10207-012-0181-6 CrossRefGoogle Scholar
  16. 16.
    Vetter, B., Ugus, O., Westhoff, D., Sorge, C.: Homomorphic primitives for a privacy-friendly smart metering architecture. In: Proceedings of the International Conference on Security and Cryptography, pp. 102–112 (2012)Google Scholar
  17. 17.
    Erkin, Z., Tsudik, G.: Private computation of spatial and temporal power consumption with smart meters. In: Proceedings of Applied Cryptography and Network Security (LNCS) , vol. 7341. Springer, Berlin, pp. 561–577 (2012)Google Scholar
  18. 18.
    Shi, E., Chow, R., Chan, T.-H.H., Song, D., Rieffel, E.: Privacy-preserving aggregation of time-series data. In: Proceedings of Network and Distributed System Security Symposium. The Internet Society (2011)Google Scholar
  19. 19.
    Li, F., Luo, B., Liu, P.: Secure information aggregation for smart grids using homomorphic encryption. In: First IEEE International Conference on Smart Grid Communications, pp. 327–332 (2010).  https://doi.org/10.1109/SMARTGRID.2010.5622064
  20. 20.
    Lu, R., Liang, X., Li, X., Lin, X., Shen, X.: An efficient and privacy-preserving aggregation scheme for secure smart grid communications. IEEE Trans. Paral. Distrib. Syst. 23(9), 1621–1631 (2012).  https://doi.org/10.1109/TPDS.2012.86
  21. 21.
    Ni, J., Zhang, K., Lin, X., Shen, X.: EDAT: efficient data aggregation without TTP for privacy-assured smart metering. IEEE Int. Conf. Commun (2016).  https://doi.org/10.1109/ICC.2016.7510611 Google Scholar
  22. 22.
    Kalogridis, G., Efthymiou, C., Denic, S.Z., Lewis, T.A., Cepeda, R.: Privacy for smart meters: Towards undetectable appliance load signatures. In: 1st IEEE International Conference on Smart Grid Communications (SmartGridComm), pp. 232–237 (2010)Google Scholar
  23. 23.
    Egarter, D., Prokop, C., Elmenreich, W.: Load hiding of household’s power demand. In: IEEE International Conference on Smart Grid Communications, pp. 854–859 (2014)Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Departament de MatemàticaUniversitat de LleidaLleidaSpain
  2. 2.CISPASaarland UniversitySaarbrückenGermany

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