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A Privacy-Friendly Framework for Vehicle-to-Grid Interactions

  • Cristina RottondiEmail author
  • Simone Fontana
  • Giacomo VerticaleEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8448)

Abstract

In the next decades, Electric Vehicles (EVs) are expected to gain increasing popularity and huge penetration in the automotive market, thanks to their potentialities for close interaction with the Smart Grid ecosystem. Firstly, recharging EV’s batteries with energy produced by renewables will allow for a consistent reduction of pollution due to the carbon emissions of traditional gasoline combustion; secondly, batteries could be exploited to store/inject energy from/to the grid in order to compensate the unpredictable fluctuations caused by Renewable Energy Sources (RES). To this aim, a load aggregator is envisioned as a scheduling entity to plan the EVs’ battery recharge/discharge according to the user’s needs and the current power generation of the grid. The main drawback of the introduction of such load aggregator is a potential harm of users’ privacy: gathering information about the EVs’ recharge requests and plug/unplug events could make the scheduler able to infer the private travelling habits of the customers, thus exposing them to the risk of tracking attacks and to other privacy threats. To address this issue, this paper proposes a security infrastructure for privacy-friendly Vehicle-to-Grid (V2G) interactions, which enables the load aggregator to schedule the EV’s battery charge/discharge without learning the current battery level, nor the amount of charged/discharged energy, nor the time periods in which the EVs are available for recharge. Our proposed scheduling protocol is based on the Shamir Secret Sharing scheme. We provide a security analysis of the privacy guarantees provided by our framework and compare its performance to the optimal schedule that would be obtained if the aggregator had full knowledge of the charging-related information.

Keywords

Integer Linear Program Battery Recharge Attack Scenario Time Epoch Integer Linear Program Model 
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.

References

  1. 1.
    Chan, C., Bouscayrol, A., Chen, K.: Electric, hybrid, and fuel-cell vehicles: architectures and modeling. IEEE Trans. Veh. Technol. 59(2), 589–598 (2010)CrossRefGoogle Scholar
  2. 2.
    Offer, G., Howey, D., Contestabile, M., Clague, R., Brandon, N.: Comparative analysis of battery electric, hydrogen fuel cell and hybrid vehicles in a future sustainable road transport system. Energy Policy 38(1), 24–29 (2010)CrossRefGoogle Scholar
  3. 3.
    Parks, K., Denholm, P., Markel, A.J.: Costs and emissions associated with plug-in hybrid electric vehicle charging in the Xcel Energy Colorado service territory. National Renewable Energy Laboratory Golden, CO (2007)Google Scholar
  4. 4.
    Sioshansi, R., Denholm, P.: Emissions impacts and benefits of plug-in hybrid electric vehicles and vehicle-to-grid services. Environ. Sci. Technol. 43(4), 1199–1204 (2009)CrossRefGoogle Scholar
  5. 5.
    Kempton, W., Tomić, J.: Vehicle-to-grid power implementation: from stabilizing the grid to supporting large-scale renewable energy. J. Power Sources 144(1), 280–294 (2005)CrossRefGoogle Scholar
  6. 6.
    DeForest, N., Funk, J., Lorimer, A., Ur, B., Sidhu, I., Kaminsky, P., Tenderich, B.: Impact of widespread electric vehicle adoption on the electrical utility business-threats and opportunities. Center for Entrepreneurship and Technology (CET) (2009)Google Scholar
  7. 7.
    U.S. Department of Energy (DOE): Communication requirements for smart grid technologies (2010)Google Scholar
  8. 8.
    Kempton, W., Tomic, J., Letendre, S., Brooks, A., Lipman, T.: Vehicle-to-grid power: battery, hybrid, and fuel cell vehicles as resources for distributed electric power in california. In of Transportation Studies (UCD), U.D.I., ed.: Working Paper Series ECD-ITS-Rr-=1-03 (2001)Google Scholar
  9. 9.
    Brooks, A.: Integration of electric drive vehicles with the power grid-a new application for vehicle batteries. In: The Seventeenth Annual Battery Conference on Applications and Advances, p. 239 (2002)Google Scholar
  10. 10.
    Kempton, W., Marra, F., Andersen, P., Garcia-Valle, R.: Business models and control and management architectures for ev electrical grid integration. In: Garcia-Valle, R., Peas Lopes, J.A. (eds.) Electric Vehicle Integration into Modern Power Networks. Power Electronics and Power Systems, pp. 87–105. Springer, New York (2013)CrossRefGoogle Scholar
  11. 11.
    Brooks, A.: Vehicle-to-grid demonstration project: grid regulation ancillary service with a battery electric vehicle. In: Research Report to CARB, AC Propulsion (2002)Google Scholar
  12. 12.
    Hoh, B., Gruteser, M., Xiong, H., Alrabady, A.: Enhancing security and privacy in traffic-monitoring systems. IEEE Pervasive Comput. 5(4), 38–46 (2006)CrossRefGoogle Scholar
  13. 13.
    Liao, L., Patterson, D.J., Fox, D., Kautz, H.: Learning and inferring transportation routines. Artif. Intell. 171(5–6), 311–331 (2007)CrossRefzbMATHMathSciNetGoogle Scholar
  14. 14.
    Liu, R., Dow, L., Liu, E.: A survey of pev impacts on electric utilities. In: 2011 IEEE PES Innovative Smart Grid Technologies (ISGT), pp. 1–8 (2011)Google Scholar
  15. 15.
    Han, Y., Chen, Y., Han, F., Liu, K.: An optimal dynamic pricing and schedule approach in v2g. In: 2012 Asia-Pacific Signal Information Processing Association Annual Summit and Conference (APSIPA ASC), pp. 1–8 (2012)Google Scholar
  16. 16.
    Mets, K., Verschueren, T., Haerick, W., Develder, C., De Turck, F.: Optimizing smart energy control strategies for plug-in hybrid electric vehicle charging. In: 2010 IEEE/IFIP Network Operations and Management Symposium Workshops (NOMS Wksps), pp. 293–299, April 2010Google Scholar
  17. 17.
    Stegelmann, M., Kesdogan, D.: Design and evaluation of a privacy-preserving architecture for vehicle-to-grid interaction. In: Petkova-Nikova, S., Pashalidis, A., Pernul, G. (eds.) EuroPKI 2011. LNCS, vol. 7163, pp. 75–90. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  18. 18.
    Stegelmann, M., Kesdogan, D.: Location privacy for vehicle-to-grid interaction through battery management. In: 2012 Ninth International Conference on Information Technology: New Generations (ITNG), pp. 373–378 (2012)Google Scholar
  19. 19.
    Yang, Z., Yu, S., Lou, W., Liu, C.: \(p^{2}\): privacy-preserving communication and precise reward architecture for v2g networks in smart grid. IEEE Trans. Smart Grid 2(4), 697–706 (2011)CrossRefGoogle Scholar
  20. 20.
    Liu, J.K., Au, M.H., Susilo, W., Zhou, J.: Enhancing location privacy for electric vehicles (at the Right time). In: Foresti, S., Yung, M., Martinelli, F. (eds.) ESORICS 2012. LNCS, vol. 7459, pp. 397–414. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  21. 21.
    Nicanfar, H., Hosseininezhad, S., TalebiFard, P., Leung, V.C.M.: Robust privacy-preserving authentication scheme for communication between electric vehicle as power energy storage and power stations. In: INFOCOM, pp. 3429–3434. IEEE (2013)Google Scholar
  22. 22.
    Shamir, A.: How to share a secret. Commun. ACM 22, 612–613 (1979)CrossRefzbMATHMathSciNetGoogle Scholar
  23. 23.
    Bogdanov, D.: Foundations and properties of shamir’s secret sharing scheme (2007). Research Seminar in CryptographyGoogle Scholar
  24. 24.
    Nishide, T., Ohta, K.: Multiparty computation for interval, equality, and comparison without bit-decomposition protocol. In: Okamoto, T., Wang, X. (eds.) PKC 2007. LNCS, vol. 4450, pp. 343–360. Springer, Heidelberg (2007) CrossRefGoogle Scholar
  25. 25.
    Kerschbaum, F., Biswas, D., de Hoogh, S.: Performance comparison of secure comparison protocols. In: 20th International Workshop on Database and Expert Systems Application, pp. 133–136 (2009)Google Scholar
  26. 26.
    Bychkovsky, V., Hull, B., Miu, A., Balakrishnan, H., Madden, S.: A measurement study of vehicular internet access using in situ wi-fi networks. In: Proceedings of the 12th Annual International Conference on Mobile Computing and Networking, MobiCom ’06, pp. 50–61. ACM, New York (2006)Google Scholar
  27. 27.
    Rottondi, C., Verticale, G., Capone, A.: Privacy-preserving smart metering with multiple data consumers. Comput. Netw. 57(7), 1699–1713 (2013)CrossRefGoogle Scholar
  28. 28.
    Stinson, D.: Cryptography Theory and Practice, 2nd edn. CRC Press, Boca Raton (2005)Google Scholar
  29. 29.
    Rottondi, C., Mauri, G., Verticale, G.: A protocol for metering data pseudonymization in smart grids to appear on Transactions on Emerging Telecommunications Technologies. doi: 10.1002/ett.2760
  30. 30.
    Rottondi, C., Fontana, S., Verticale, G.: Enabling privacy in vehicle-to-grid interactions for battery recharging. Energies 7, 2780–2798 (2014)CrossRefGoogle Scholar
  31. 31.
    Barker, S., Mishra, A., Irwin, D., Cecchet, E., Shenoy, P., Albrecht, J.: Smart*: an open data set and tools for enabling research in sustainable homes. In: The 1st KDD Workshop on Data Mining Applications in Sustainability (SustKDD) (2011)Google Scholar
  32. 32.
    Global Energy Forecasting Competition 2012: Wind forecasting. http://www.kaggle.com/c/GEF2012-wind-forecasting/data
  33. 33.
    U.S. Department of Transportation, Federal Highway Administration: National household travel survey (2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Dipartimento di Elettronica, Informazione e BioingegneriaPolitecnico di MilanoMilanoItaly

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