A Selective Review

  • Fábio Borges de Oliveira


This chapter presents the areas in which Privacy-Preserving Protocols (PPPs) have been developed and aims to highlight the most relevant related work for PPPs. Naturally, there are privacy-enhancing technologies with restrictive results on cost, efficiency, or privacy. For example, the use of a home battery is the best solution as discussed in Sect. 3.1.1. However, it is too expensive. The areas with promising results are investigated in this book. The next two sections present the restrictive and promising results found.


Privacy-preserving protocols Privacy-enhancing technologies Survey Obfuscation Anonymization Homomorphic encryption DC-Net Commitment 


  1. 1.
    J.-M. Bohli, C. Sorge, O. Ugus, A privacy model for smart metering, in 2010 IEEE International Conference on Communications Workshops (ICC) (2010), pp. 1–5. doi:10.1109/ICCW.2010.5503916
  2. 2.
    F. Borges, L.A. Martucci, iKUP keeps users’ privacy in the Smart Grid, in 2014 IEEE Conference on Communications and Network Security (CNS) (2014), pp. 310–318. doi:10.1109/CNS.2014.6997499
  3. 3.
    F. Borges, M. Mühlhäuser, EPPP4SMS: efficient privacy-preserving protocol for smart metering systems and its simulation using real-world data. IEEE Trans. Smart Grid 5 (6), 2701–2708 (2014). doi:10.1109/TSG.2014.2336265.
  4. 4.
    F. Borges, L.A. Martucci, M. Mühlhäuser, Analysis of privacy-enhancing protocols based on anonymity networks, in 2012 IEEE Third International Conference on Smart Grid Communications (SmartGridComm) (2012), pp. 378–383. doi:10.1109/SmartGridComm.2012.6486013
  5. 5.
    F. Borges et al., A privacy-enhancing protocol that provides innetwork data aggregation and verifiable smart meter billing, in 2014 IEEE Symposium on Computers and Communication (ISCC) (2014), pp. 1–6. doi:10.1109/ISCC.2014.6912612
  6. 6.
    F. Borges, J. Buchmann, M. Mühlhäuser, Introducing asymmetric DC-Nets, in 2014 IEEE Conference on Communications and Network Security (CNS) (2014), pp. 508–509. doi:10.1109/CNS.2014.6997528
  7. 7.
    F. Borges, F. Volk, M. Mühlhäuser, Efficient, verifiable, secure, and privacy-friendly computations for the smart grid, in 2015 IEEE Power Energy Society Innovative Smart Grid Technologies Conference (ISGT) (2015), pp. 1–5. doi:10.1109/ISGT.2015.7131862
  8. 8.
    D. Chaum, The dining cryptographers problem: unconditional sender and recipient untraceability. J. Cryptol. 1 (1), 65–75 (1988). issn:0933-2790.
  9. 9.
    C. Dwork, Differential privacy: a survey of results. English, in Theory and Applications of Models of Computation, ed. by M. Agrawal et al., vol. 4978. Lecture Notes in Computer Science (Springer, Berlin, Heidelberg, 2008), pp. 1 –19. isbn:978-3-540-79227-7. doi: 10.1007/978-3-540-79228-4_1.
  10. 10.
    C. Efthymiou, G. Kalogridis, Smart grid privacy via anonymization of smart metering data, in 2010 First IEEE International Conference on Smart Grid Communications (SmartGridComm) (2010), pp. 238–243. doi:10.1109/SMARTGRID.2010.5622050
  11. 11.
    G. Eibl, D. Engel, Influence of data granularity on smart meter privacy. IEEE Trans. Smart Grid 6 (2), 930–939 (2015). issn:1949-3053. doi:10.1109/TSG.2014.2376613
  12. 12.
    Z. Erkin, G. Tsudik, Private computation of spatial and temporal power consumption with smart meters, in ACNS, ed. by F. Bao, P. Samarati, J. Zhou, vol. 7341. Lecture Notes in Computer Science (Springer, Berlin, 2012), pp. 561–577. isbn:978-3-642-31283-0Google Scholar
  13. 13.
    Z. Erkin et al., Privacy-preserving data aggregation in smart metering systems: an overview. IEEE Signal Process. Mag. 30.2 (2013), pp. 75–86. issn: 1053-5888. doi:10.1109/MSP.2012.2228343.
  14. 14.
    M. Jawurek, F. Kerschbaum, Fault-tolerant privacy- preserving statistics, in Privacy Enhancing Technologies, ed. by S. Fischer-Hübner, M. Wright, vol. 7384. Lecture Notes in Computer Science (Springer, Berlin, Heidelberg, 2012), pp. 221–238. isbn:978-3-642-31679-1. doi: 10.1007/978-3-642-31680-7_12.
  15. 15.
    M. Jawurek, M. Johns, F. Kerschbaum, Plug-in privacy for smart metering billing, in Proceedings of Privacy Enhancing Technologies: 11th International Symposium, PETS 2011, Waterloo, ON, July 27–29, 2011. ed. by S. Fischer-Hübner, N. Hopper (Springer, Berlin, Heidelberg, 2011), pp. 192–210. isbn:978-3-642-22263-4. doi: 10.1007/978-3-642-22263-4_11.
  16. 16.
    K. Kursawe, G. Danezis, M. Kohlweiss, Privacy-friendly aggregation for the smart-grid, in Proceedings of Privacy Enhancing Technologies: 11th International Symposium, PETS 2011, Waterloo, ON, July 27–29, 2011, ed. by S. Fischer-Hübner, N. Hopper (Springer, Berlin, Heidelberg, 2011), pp. 175–191. isbn:978-3-642-22263-4. doi: 10.1007/978-3-642-22263-4_10.
  17. 17.
    F. Li, B. Luo, P. Liu, Secure information aggregation for smart grids using homomorphic encryption, in 2010 First IEEE International Conference on Smart Grid Communications (SmartGridComm) (2010), pp. 327–332. doi:10.1109/SMARTGRID.2010.5622064
  18. 18.
    N. Lu, An Evaluation of the HVAC load potential for providing load balancing service. IEEE Trans. Smart Grid 3 (3), 1263–1270 (2012). issn:1949-3053. doi:10.1109/TSG.2012.2183649
  19. 19.
    T. Masuta, A. Yokoyama, Supplementary load frequency control by use of a number of both electric vehicles and heat pump water heaters. IEEE Trans. Smart Grid 3 (3), 1253–1262 (2012). issn:1949-3053. doi:10.1109/TSG.2012.2194746
  20. 20.
    S. McLaughlin, P. McDaniel, W. Aiello, Protecting consumer privacy from electric load monitoring, in Proceedings of the 18th ACM Conference on Computer and Communications Security. CCS ’11. Chicago, IL (ACM, New York, 2011), pp. 87–98. isbn:978-1-4503-0948-6. doi: 10.1145/2046707.2046720.
  21. 21.
    D. Novosel, V. Rabl, J. Nelson, A report to the U.S. DOE: IEEE shares its insights on priority issues [leader’s corner]. IEEE Power Energ. Mag. 13 (2), 6–12 (2015). issn:1540-7977. doi:10.1109/MPE.2014.2374971
  22. 22.
    P. Paillier, Public-key cryptosystems based on composite degree residuosity classes, in Advances in Cryptology - EUROCRYPT 1999, vol. 1592. Lecture Notes in Computer Science (Springer, Berlin, 1999), pp. 223–238. isbn:978-3-540-65889-4Google Scholar
  23. 23.
    T.P. Pedersen, Non-interactive and information-theoretic secure verifiable secret sharing, in Proceedings of the 11th Annual International Cryptology Conference on Advances in Cryptology. CRYPTO ’91 (Springer, London, 1992), pp. 129–140. isbn:3-540-55188-3.
  24. 24.
    C. Rottondi, G. Verticale, C. Krauss, Distributed privacy-preserving aggregation of metering data in smart grids. IEEE J. Sel. Areas Commun. 31 (7), 1342–1354 (2013). issn:0733-8716. doi:10.1109/JSAC.2013.130716
  25. 25.
    C. Rottondi, S. Fontana, G. Verticale, A privacy-friendly framework for vehicle-to-grid interactions. English, in Smart Grid Security, ed. by J. Cuellar. Lecture Notes in Computer Science (Springer International Publishing, Cham, 2014), pp. 125–138. isbn:978-3-319-10328-0. doi: 10.1007/978-3-319-10329-7_8.
  26. 26.
    S. Ruj, A. Nayak, A decentralized security framework for data aggregation and access control in smart grids. IEEE Trans. Smart Grid 4 (1), 196–205 (2013). issn:1949-3053. doi:10.1109/TSG.2012.2224389
  27. 27.
    A. Shamir, How to share a secret. Commun. ACM 22 (11), 612–613 (1979). issn:0001-0782. doi:10.1145/359168.359176.
  28. 28.
    D. Varodayan, A. Khisti, Smart meter privacy using a rechargeable battery: minimizing the rate of information leakage, in 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2011), pp. 1932–1935. doi:10.1109/ICASSP.2011.5946886
  29. 29.
    S. Wang et al., A randomized response model for privacy preserving smart metering. IEEE Trans. Smart Grid 3 (3), 1317–1324 (2012). issn:1949-3053. doi:10.1109/TSG.2012.2192487
  30. 30.
    W. Yang et al., Minimizing private data disclosures in the smart grid, in Proceedings of the 2012 ACM Conference on Computer and Communications Security. CCS ’12. (ACM, Raleigh, NC, 2012), pp. 415–427. isbn:978-1-4503-1651-4. doi:10.1145/2382196.2382242.

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Fábio Borges de Oliveira
    • 1
  1. 1.Laboratório Nacional de Computação Científica (LNCC) - PetrópolisRio de JaneiroBrazil

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