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Privacy-Preserving Energy-Reading for Smart Meter

  • Gianpiero Costantino
  • Fabio Martinelli
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9102)

Abstract

Smart Meters belong to the Advanced Metering Infrastructure (AMI) and allow customers to monitor locally and remotely the current usage of energy. Providers query Smart Meters for billing purpose or to establish the amount of energy needed by houses. However, reading details sent from smart meters to the energy provider can be used to violate customers’ privacy. In this paper, our contribution is two-fold: first, we present an architecture to turn traditional energy meters into Smart Meters, and then we illustrate a privacy-preserving solution, which uses Secure Two-party Computation, to preserve customers’ privacy during energy-readings. In particular, we deployed a Smart Meter built upon an existing energy meter available in Italy. Then, we collected and analysed an energy trace of two months, and we tag customers hourly/daily/monthly habits by observing their consumes. Finally, we provide the feasibility of our solution to protect customers’ privacy.

Keywords

Smart meter Privacy Secure Two-party computation Energy trace Raspberry pi 

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References

  1. 1.
    Andrew, C., Yao, C.: Protocols for secure computations. In: 23rd IEEE Symposium on FOCS, pp. 160–164 (1982)Google Scholar
  2. 2.
    Ben-David, A., Nisan, N., Pinkas,B.: Fairplaymp: a system for secure multi-party computation. In: Proceedings of the CCS Conference, pp. 257–266. ACM, New York, NY (2008)Google Scholar
  3. 3.
    Biselli, A., Franz, E., Coutinho, M.P.: Protection of consumer data in the smart grid compliant with the german smart metering guideline. In: Proceedings of the First ACM Workshop on Smart Energy Grid Security, SEGS 2013, pp. 41–52. ACM, New York, NY (2013)Google Scholar
  4. 4.
    C. P. U. Commission. The future of privacy forum and truste launch a smart grid privacy seal program, May 2011. http://tiny.cc/jlpwhx
  5. 5.
    Costantino, G., Martinelli, F., Santi, P.: Investigating the Privacy vs. Forwarding Accuracy Tradeoff in Opportunistic Interest-Casting. Transactions on mobile computing (2013)Google Scholar
  6. 6.
    Costantino, G., Martinelli, F., Santi, P., Amoruso, D.: An implementation of secure two-party computation for smartphones with application to privacy-preserving interest-cast. In: Proceedings of the 18th International Conference Mobicom, pp. 447–450. ACM (2012)Google Scholar
  7. 7.
    Danezis, G., Fournet, C., Kohlweiss, M., Béguelin, S.Z.: Smart meter aggregation via secret-sharing. In SEGS@CCS, pp. 75–80 (2013)Google Scholar
  8. 8.
    Greveler, U., Justus, B., Loehr, D.: Forensic content detection through power consumption. In: IEEE ICC, pp. 6759–6763, JuneGoogle Scholar
  9. 9.
    Heck, W.: Smart energy meter will not be compulsory, April 2009. http://tiny.cc/9vpwhx
  10. 10.
    Holzer, A., Franz, M., Katzenbeisser, S., Veith, H.: Secure two-party computations in ansi c. In: Proceedings of the CCS Conference, CCS 2012, pp. 772–783, NY, USA (2012)Google Scholar
  11. 11.
    Huang, Y., Chapman, P., Evans, D.: Privacy-preserving applications on smartphones. In: Proceedings of the 6th USENIX Conference on Hot Topics in Security, HotSec 2011, pp. 4–4. USENIX Association, Berkeley, CA (2011)Google Scholar
  12. 12.
    Kolesnikov, V., Schneider, T.: Improved garbled circuit: free XOR gates and applications. In: Aceto, L., Damgård, I., Goldberg, L.A., Halldórsson, M.M., Ingólfsdóttir, A., Walukiewicz, I. (eds.) ICALP 2008, Part II. LNCS, vol. 5126, pp. 486–498. Springer, Heidelberg (2008) CrossRefGoogle Scholar
  13. 13.
    Malkhi, D., Nisan, N., Pinkas, B., Sella, Y.: Fairplay—a secure two-party computation system. In: Proceedings of the 13th Conference on USENIX Security Symposium - vol. 13, SSYM 2004, pp. 20–20. USENIX Association, Berkeley, CA (2004)Google Scholar
  14. 14.
    Mármol, F., Sorge, C., Ugus, O., Pérez, G.: Do not snoop my habits: preserving privacy in the smart grid. IEEE Communications Magazine 50(5), 166–172 (2012)CrossRefGoogle Scholar
  15. 15.
    Molina-Markham, A., Danezis, G., Fu, K., Shenoy, P., Irwin, D.: Designing privacy-preserving smart meters with low-cost microcontrollers. In: Keromytis, A.D. (ed.) FC 2012. LNCS, vol. 7397, pp. 239–253. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  16. 16.
    Molina-Markham, A., Shenoy, P., Fu, K., Cecchet, E., Irwin, D.: Private memoirs of a smart meter. In: Proceedings of the SenSys Workshop (Builsys), BuildSys 2010, pp. 61–66. ACM, New York, NY (2010)Google Scholar
  17. 17.
    Quinn, E.L.: Privacy and the new energy infrastructure, p. 43, February 2009Google 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 2011, pp. 49–60. ACM, New York, NY (2011)Google Scholar
  19. 19.
    Sankar, L., Kar, S., Tandon, R., Poor, H.V.: Competitive privacy in the smart grid: an information-theoretic approach. CoRR, abs/1108.2237 (2011)Google Scholar
  20. 20.
    Thoma, C., Cui, T., Franchetti, F.: Privacy preserving smart metering system based retail level electricity market. In: Power and Energy Society General Meeting (PES), 2013 IEEE, pp. 1–5, July 2013Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Istituto di Informatica e TelematicaCNRPisaItaly

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