Privacy-enhanced architecture for smart metering

  • Félix Gómez Mármol
  • Christoph Sorge
  • Ronald Petrlic
  • Osman Ugus
  • Dirk Westhoff
  • Gregorio Martínez Pérez
Regular Contribution


The recent deployment of smart grids promises to bring numerous advantages in terms of energy consumption reduction in both homes and businesses. A more transparent and instantaneous measurement of electricity consumption through smart meters utilization leads to an enhancement in the ability of monitoring, controlling and predicting energy usage. Nevertheless, it also has associated drawbacks related to the privacy of customers, since such management might reveal their personal habits, which electrical appliances they are using at each moment, whether they are at home or not, etc. In this work, we present a privacy-enhanced architecture for smart metering aimed at tackling this threat by means of encrypting individual measurements while allowing the electricity supplier to access the aggregation of the corresponding decrypted values.


Homomorphic encryption transformation Privacy Smart meters Smart grid 

Supplementary material

10207_2012_181_MOESM1_ESM.pdf (373 kb)
ESM 1 (PDF 373 kb)


  1. 1.
    Wood, G., Newborough, M.: Dynamic energy-consumption indicators for domestic appliances: Environment, behaviour and design. Energy Build. 35(8), 821–841 (2003). doi:10.1016/S0378-7788(02)00241-4 CrossRefGoogle Scholar
  2. 2.
    Cheng, S.-T., Wang, C.-H.: An adaptive scenario-based reasoning system across smart houses. Wirel. Pers. Commun. 64(2), 287–304 (2012). doi:10.1007/s11277-010-0199-x CrossRefGoogle Scholar
  3. 3.
    Bañares Hernández, S.: Smart grid for electricity efficiency. In: Workshop On ICT For Innovation and Economy Recovery. University of Murcia (Spain) (2010)Google Scholar
  4. 4.
    Wood, G., Newborough, M.: Dynamic energy-consumption indicators for domestic appliances: Environment, behaviour and design. Elsevier Energy Build. 35(8), 821–841 (2003)CrossRefGoogle Scholar
  5. 5.
    McDaniel, P., McLaughlin, S.: Security and privacy challenges in the smart grid. IEEE Secur. Priv. 7, 75–77 (2009)Google Scholar
  6. 6.
    Cavoukian, A., Polonetsky, J., Wolf, C.: SmartPrivacy for the smart grid: Embedding privacy into the design of electricity conservation. Identity Inf. Soc. 3(2), 275–294 (2010)CrossRefGoogle Scholar
  7. 7.
    Rivest, R., Adleman, L., Dertouzos, M.: Foundations of Secure Computation. Academic Press, pp. 169–177. Ch. On data banks and privacy homomorphisms (1978)Google Scholar
  8. 8.
    Armknecht, F., Westhoff, D., Girao, J., Hessler, A.: A lifetime-optimized end-to-end encryption scheme for sensor networks allowing in-network processing. Comput. Commun. 31(4), 734–749 (2008)CrossRefGoogle Scholar
  9. 9.
    Mykletun, E., Girao, J., Westhoff, D.: Public key based cryptoschemes for data concealment in wireless sensor networks. In: IEEE International Conference on Communications, ICC2006. Istanbul, Turkey (2006)Google Scholar
  10. 10.
    García, F.D., Jacobs, B.: Privacy-friendly energy-metering via homomorphic encryption. In: 6th Workshop on Security and Trust Management (STM 2010) (2010)Google Scholar
  11. 11.
    Paillier, P.: Public-key cryptosystems based on composite degree residuosity classes. In: Proceedings of EUROCRYPT’99, pp. 223–238 (1999)Google Scholar
  12. 12.
    Li, F., Luo, B., Liu, P.: Secure information aggregation for smart grids using homomorphic encryption. In: First IEEE International Conference on Smart Grid Communications. IEEE Communications Society, Gaithersburg, USA, pp. 327–332 (2010)Google Scholar
  13. 13.
    Finster, S., Conrad, M.: Privacy-aware realtime smart metering. In: VDE Kongress 2010—E-Mobility: Technologien-Infrastruktur-Märkte (2010)Google Scholar
  14. 14.
    Petrlic, R.: A privacy-preserving concept for smart grids. In: Sicherheit in vernetzten Systemen: 18. DFN Workshop. Books on Demand GmbH, pp. B1–B14 (2010)Google Scholar
  15. 15.
    Lim, H.W., Paterson, K.G.: Identity-based cryptography for grid security. Int. J. Inf. Secur. 10(1), 15–32 (2011)Google Scholar
  16. 16.
    Bohli, J.-M., Sorge, C., Ugus, O.: A privacy model for smart metering. In:IEEE International Conference on Communications (2010)Google Scholar
  17. 17.
    McLaughlin, S., McDaniel, P., Aiello, W.: Protecting consumer privacy from electric load monitoring. In: Proceedings of the 18th ACM Conference on Computer and Communications Security. ACM, New York, NY, USA, pp. 87–98 (2011)Google Scholar
  18. 18.
    Rial, A., Danezis, G.: Privacy-preserving smart metering. In: Proceedings of the 10th Annual ACM Workshop on Privacy in the Electronic Society, WPES ’11. ACM, New York, NY, USA, pp. 49–60 (2011)Google Scholar
  19. 19.
    Gómez Mármol, F., Sorge, C., Ugus, O., Martínez Pérez, G.: Do not snoop my habits. Preserving privacy in the smart grid. IEEE Commun. Mag. 50(5), 166–172 (2012)CrossRefGoogle Scholar
  20. 20.
    Minami, K., Lee, A.J., Winslett, M., Borisov, N.: Secure aggregation in a publish-subscribe system. In: WPES ’08: Proceedings of the 7th ACM Workshop on Privacy in the Electronic, Society, pp. 95–104 (2008)Google Scholar
  21. 21.
    Dierks, T., Rescorla, E.: The Transport Layer Security (TLS) Protocol Version 1.2. RFC 5246 (Proposed Standard) (Aug. 2008).
  22. 22.
    Camenisch, J., Lysyanskaya, A.: Signature schemes and anonymous credentials from bilinear maps. In: Advances in Cryptology CRYPTO’04, pp. 56–72 (2004)Google Scholar
  23. 23.
    Ateniese, G., Camenisch, J., Joye, M., Tsudik, G.: A practical and provably secure coalition-resistant group signature scheme. In: Proceedings of the 20th Annual International Cryptology Conference on Advances in Cryptology, pp. 255–270 (2000)Google Scholar
  24. 24.
    Zsolt Berta, I., Buttyan, L., Vajda, I.: A framework for the revocation of unintended digital signatures initiated by malicious terminals. IEEE Trans. Dependable Secur. Comput. 2, 268–272 (2005)Google Scholar
  25. 25.
    Fonseca, E., Festag, A., Baldessari, R., Aguiar, R.L.: Support of anonymity in vanets-putting pseudonymity into practice. In: Wireless Communications and Networking Conference 2007. WCNC 2007. IEEE. pp. 3400–3405 (2007) doi:10.1109/WCNC.2007.625
  26. 26.
    Dingledine, R., Mathewson, N., Syverson, P.: Tor: the second-generation onion router. In: Proceedings of the 13th Conference on USENIX Security Symposium, vol. 13, SSYM’04. pp. 21–21 (2004)Google Scholar
  27. 27.
    Peter, S., Westhoff, D., Castelluccia, C.: A survey on the encryption of convergecast-traffic with in-network processing. IEEE Trans. Dependable Secur. Comput. 4, 20–34 (2010). doi:10.1109/TDSC.2008.23 CrossRefGoogle Scholar
  28. 28.
    Castelluccia, C., Mykletun, E., Tsudik, G.: Efficient aggregation of encrypted data in wireless sensor networks. In: The Second Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services, pp. 109–117 (2005)Google Scholar
  29. 29.
    Sirivianos, M., Westhoff, D., Armknecht, F., Girao, J.: Non-manipulable aggregator node election protocols for wireless sensor networks. In: Proceedings of the International Symposium on Modeling and Optimization in Mobile. Ad Hoc, and Wireless Networks (WiOpt) (2007)Google Scholar
  30. 30.
    Rebollo-Monedero, D., Forné, J., Solanas, A., Martínez-Ballesté, A.: Private location-based information retrieval through user collaboration. Comput. Commun. 33(6), 762–774 (2010). doi:10.1016/j.comcom.2009.11.024 Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Félix Gómez Mármol
    • 1
  • Christoph Sorge
    • 2
  • Ronald Petrlic
    • 2
  • Osman Ugus
    • 3
  • Dirk Westhoff
    • 4
  • Gregorio Martínez Pérez
    • 5
  1. 1.NEC Europe LtdHeidelbergGermany
  2. 2.Institut für InformatikUniversity of PaderbornPaderbornGermany
  3. 3.Hamburg University of Applied SciencesHamburgGermany
  4. 4.Hochschule FurtwangenFurtwangenGermany
  5. 5.Departamento de Ingeniería de la Información y las ComunicacionesUniversity of MurciaMurciaSpain

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