Consumer Privacy on Distributed Energy Markets

  • Niklas BüscherEmail author
  • Stefan Schiffner
  • Mathias Fischer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9857)


Recently, several privacy-enhancing technologies for smart grids have been proposed. However, most of these solutions presume the cooperation of all smart grid participants. Hence, the privacy protection of consumers depends on the willingness of the suppliers to deploy privacy-enhancing technologies. Since electrical energy is essential for our modern life, it is impossible for consumers to opt out. We propose a novel consumer-only (do-it-yourself) privacy-enhancing approach under the assumption that users can obtain their energy from multiple suppliers on a distributed market. By splitting the demand over multiple suppliers, the information each of them can collect about a single consumer is reduced. In this context, we suggest two different buying strategies: a time and a sample diversification strategy. To measure their provided level of privacy protection, we introduce a new indistinguishability metric \(\lambda \)-Indistinguishability (\(\lambda \text {-IND}\)) that measures how relative consumption changes can be hidden in the total consumption. We evaluate the presented strategies with \(\lambda \text {-IND}\) and derive first privacy boundaries. The evaluation of our buying strategies on real-world energy data sets indicates their ability to hide load profiles of privacy sensitive appliances at low communication and computational overhead.


Smart Grid Privacy Protection Load Sample Statistical Distance Load Profile 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work has been co-funded by the German Federal Ministry of Education and Research (BMBF) within CRISP, by the DFG as part of project A.1 within the RTG 2050 “Privacy and Trust for Mobile Users” and by the Hessian LOEWE excellence initiative within CASED. At the time this research was conducted, Stefan Schiffner and Mathias Fischer were part of CASED at TU Darmstadt. Stefan Schiffner is currently employed at the European Union Agency for Network and Information Security (ENISA). The content of this article does not reflect the official opinion of ENISA. Responsibility for the information and views expressed in therein lies entirely with the authors.


  1. 1.
    Ács, G., Castelluccia, C.: I have a DREAM! (DiffeRentially privatE smArt Metering). In: Filler, T., Pevný, T., Craver, S., Ker, A. (eds.) IH 2011. LNCS, vol. 6958, pp. 118–132. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  2. 2.
    Backes, M., Meiser, S.: Differentially private smart metering with battery recharging. In: Garcia-Alfaro, J., Lioudakis, G., Cuppens-Boulahia, N., Foley, S., Fitzgerald, W.M. (eds.) DPM 2013 and SETOP 2013. LNCS, vol. 8247, pp. 194–212. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  3. 3.
    Baignères, T., Sepehrdad, P., Vaudenay, S.: Distinguishing distributions using chernoff information. In: Heng, S.-H., Kurosawa, K. (eds.) ProvSec 2010. LNCS, vol. 6402, pp. 144–165. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  4. 4.
    Bohli, J.-M., Sorge, C., Ugus, O.: A privacy model for smart metering. In: 2010 IEEE International Conference on Communications Workshops, pp. 1–5. IEEE, May 2010Google Scholar
  5. 5.
    Clark, S.S., Mustafa, H., Ransford, B., Sorber, J., Fu, K., Xu, W.: Current events: identifying webpages by tapping the electrical outlet. In: Jajodia, S., Mayes, K., Crampton, J. (eds.) ESORICS 2013. LNCS, vol. 8134, pp. 700–717. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  6. 6.
    Csisz, I., et al.: Information-type measures of difference of probability distributions and indirect observations. Studia Sci. Math. Hungar. 2, 299–318 (1967)MathSciNetGoogle Scholar
  7. 7.
    Danezis, G., Kohlweiss, M., Rial, A.: Differentially private billing with rebates. In: Filler, T., Pevný, T., Craver, S., Ker, A. (eds.) IH 2011. LNCS, vol. 6958, pp. 148–162. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  8. 8.
    Dwork, C.: Differential privacy. In: Bugliesi, M., Preneel, B., Sassone, V., Wegener, I. (eds.) ICALP 2006. LNCS, vol. 4052, pp. 1–12. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  9. 9.
    Dwork, C., Naor, M., Pitassi, T., Rothblum, G.N.: Differential privacy under continual observation. In: Proceedings of the 42nd ACM Symposium on Theory of Computing (STOC), pp. 715–724 (2010)Google Scholar
  10. 10.
    Efthymiou, C., Kalogridis, G.: Smart grid privacy via anonymization of smart metering data. In: International Conference on Smart Grid Communications (SmartGridComm), pp. 238–243. IEEE (2010)Google Scholar
  11. 11.
    Garcia, F.D., Jacobs, B.: Privacy-friendly energy-metering via homomorphic encryption. In: Cuellar, J., Lopez, J., Barthe, G., Pretschner, A. (eds.) STM 2010. LNCS, vol. 6710, pp. 226–238. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  12. 12.
    Greveler, U., Justus, B., Loehr, D.: Multimedia content identification through smart meter power usage profiles. Computers, Privacy and Data Protection CPDP, Brussels, Belgium (2012)Google Scholar
  13. 13.
    Hart, G.W.: Residential energy monitoring and computerized surveillance via utility power flows. IEEE Technol. Soc. Mag. 8(2), 12–16 (1989)CrossRefGoogle Scholar
  14. 14.
    Jawurek, M., Kerschbaum, F., Danezis, G.: Privacy technologies for smart grids - a survey of options. Technical report, Microsoft Research - Tech Report - 2012 - 119 (2012)Google Scholar
  15. 15.
    Kalogridis, G., Efthymiou, C., Denic, S.Z., Lewis, T.A., Cepeda, R.: Privacy for smart meters: towards undetectable appliance load signatures. In: IEEE International Conference on Smart Grid Communications (SmartGridComm), pp. 232–237 (2010)Google Scholar
  16. 16.
    Kolter, J.Z., Johnson, M.J.: REDD: a public data set for energy disaggregation research. In: SustKDD Workshop on Data Mining Applications in Sustainability, San Diego, CA, pp. 1–6 (2011)Google Scholar
  17. 17.
    Kursawe, K., Danezis, G., Kohlweiss, M.: Privacy-friendly aggregation for the smart-grid. In: Fischer-Hübner, S., Hopper, N. (eds.) PETS 2011. LNCS, vol. 6794, pp. 175–191. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  18. 18.
    Lin, H.-Y., Tzeng, W.-G., Shen, S.-T., Lin, B.-S.P.: A practical smart metering system supporting privacy preserving billing and load monitoring. In: Bao, F., Samarati, P., Zhou, J. (eds.) ACNS 2012. LNCS, vol. 7341, pp. 544–560. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  19. 19.
    Makonin, S., Popowich, F., Bartram, L., Gill, B., Bajic, I.V.: AMPds: a public dataset for load disaggregation and eco-feedback research. In: IEEE Electrical Power and Energy Conference, pp. 1–6 (2013)Google Scholar
  20. 20.
    Molina-Markham, A., Shenoy, P., Fu, K., Cecchet, E., Irwin, D.: Private memoirs of a smart meter. In: Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building, pp. 61–66. ACM (2010)Google Scholar
  21. 21.
    Neyman, J., Pearson, E.S.: On the problem of the most efficient tests of statistical hypotheses. In: Kotz, S., Johnson, N. (eds.) Breakthroughs in Statistics. Springer Series in Statistics, pp. 73–108 (1992)Google Scholar
  22. 22.
    Pinsker, M.S.: Information and information stability of random variables and processes (1960)Google Scholar
  23. 23.
    Rial, A., Danezis, G.: Privacy-preserving smart metering. In: Proceedings of the 10th Annual ACM Workshop on Privacy in the Electronic Society, pp. 49–60. ACM (2011)Google Scholar
  24. 24.
    Varodayan, D., Khisti, A.: Smart meter privacy using a rechargeable battery: minimizing the rate of information leakage. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1932–1935 (2011)Google Scholar
  25. 25.
    Vaudenay, S.: On privacy models for RFID. In: Kurosawa, K. (ed.) ASIACRYPT 2007. LNCS, vol. 4833, pp. 68–87. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  26. 26.
    Wang, S., Cui, L., Que, J., Choi, D.-H., Jiang, X., Cheng, S., Xie, L.: A randomized response model for privacy preserving smart metering. IEEE Trans. Smart Grid 3(3), 1317–1324 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Niklas Büscher
    • 1
    Email author
  • Stefan Schiffner
    • 2
  • Mathias Fischer
    • 3
  1. 1.Technische Universität DarmstadtDarmstadtGermany
  2. 2.ENISAAthensGreece
  3. 3.Westfälische Wilhelms-Universität MünsterMünsterGermany

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