I Have a DREAM! (DiffeRentially privatE smArt Metering)

  • Gergely Ács
  • Claude Castelluccia
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6958)

Abstract

This paper presents a new privacy-preserving smart metering system. Our scheme is private under the differential privacy model and therefore provides strong and provable guarantees. With our scheme, an (electricity) supplier can periodically collect data from smart meters and derive aggregated statistics without learning anything about the activities of individual households. For example, a supplier cannot tell from a user’s trace whether or when he watched TV or turned on heating. Our scheme is simple, efficient and practical. Processing cost is very limited: smart meters only have to add noise to their data and encrypt the results with an efficient stream cipher.

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References

  1. 1.
    Acs, G., Castelluccia, C.: I have a DREAM! (DIffeRentially PrivatE smart Metering). Technical Report (2011), http://planete.inrialpes.fr/~ccastel/PAPERS/IH_TR.pdf
  2. 2.
    Anderson, R., Fuloria, S.: On the security economics of electricity metering. In: Proceedings of the WEIS (June 2010)Google Scholar
  3. 3.
    Anderson, R., Fuloria, S.: Who controls the off switch? In: Proceedings of the IEEE SmartGridComm (June 2010)Google Scholar
  4. 4.
    Bohli, J.-M., Sorge, C., Ugus, O.: A Privacy Model for Smart Metering. In: Proceedings of IEEE ICC (2010)Google Scholar
  5. 5.
    Castelluccia, C., Mykletun, E., Tsudik, G.: Efficient Aggregation of Encrypted Data in Wireless Sensor Networks. In: ACM/IEEE Mobiquitous Conference (2005)Google Scholar
  6. 6.
    Dwork, C., Kenthapadi, K., McSherry, F., Mironov, I., Naor, M.: Our Data, Ourselves: Privacy Via Distributed Noise Generation. In: Vaudenay, S. (ed.) EUROCRYPT 2006. LNCS, vol. 4004, pp. 486–503. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  7. 7.
    Dwork, C., McSherry, F., Nissim, K., Smith, A.: Calibrating Noise to Sensitivity in Private Data Analysis. In: Proceedings of the 3rd IACR TCC (2006)Google Scholar
  8. 8.
    Efthymiou, C., Kalogridis, G.: Smart Grid Privacy via Anonymization of Smart Metering Data. In: Proceedings of IEEE SmartGridComm (October 2010)Google Scholar
  9. 9.
    Fouque, P.A., Poupard, G., Stern, J.: Sharing decryption in the context of voting or lotteries. In: Proceedings of FC, pp. 90–104 (2001)Google Scholar
  10. 10.
    Garcia, F.D., Jacobs, B.: Privacy-friendly Energy-metering via Homomorphic Encryption. In: Proceedings of the STM (2010)Google Scholar
  11. 11.
    Hart, G.: Nonintrusive appliance load monitoring. Proceedings of the IEEE 80(12), 1870–1891 (1992)CrossRefGoogle Scholar
  12. 12.
    Korolova, A., Kenthapadi, K., Mishra, N., Ntoulas, A.: Releasing Search Queries and Clicks Privately. In: Proceedings of WWW 2009 (2009)Google Scholar
  13. 13.
    Kotz, S., Kozubowski, T.J., Podgorski, K.: The Laplace distribution and generalizations. Birkhäuser, Basel (2001)CrossRefMATHGoogle Scholar
  14. 14.
    Lam, H., Fung, G., Lee, W.K.: A novel method to construct taxonomy electrical appliances based on load signatures. IEEE Transactions on Consumer Electronics 53(2), 653–660 (2007)CrossRefGoogle Scholar
  15. 15.
    Molina-Markham, A., Shenoy, P., Fu, K., Cecchet, E., Irwin, D.: Private memoirs of a smart meter. In: Proceedings of ACM Buildsys (2010)Google Scholar
  16. 16.
    Anderson, R., Fuloria, S., Alvarez, F., McGrath, K.: Key Management for Substations: Symmetric Keys, Public Keys or No Keys? In: IEEE PSCE (2011)Google Scholar
  17. 17.
    Rastogi, V., Nath, S.: Differentially Private Aggregation of Distributed Time-Series with Transformation and Encryption. In: Proceedings of the ACM SIGMOD (June 2010)Google Scholar
  18. 18.
    Rial, A., Danezis, G.: Privacy-Preserving Smart Metering. In: Technical Report, MSR-TR-2010-150. Microsoft Research (2010)Google Scholar
  19. 19.
    Richardson, I., Thomson, M., Infield, D., Clifford, C.: Domestic electricity use: A high-resolution energy demand model. Energy and Buildings 42, 1878–1887 (2010)CrossRefGoogle Scholar
  20. 20.
    Shi, E., Chan, T., Rieffel, E., Chow, R., Song, D.: Privacy-Preserving Aggregation of Time-Series Data. In: Proceedings of NDSS (February 2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Gergely Ács
    • 1
  • Claude Castelluccia
    • 1
  1. 1.INRIA Rhone AlpesMontbonnotFrance

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