Wireless Networks

, Volume 22, Issue 2, pp 491–502 | Cite as

Privacy-preserving data aggregation scheme against internal attackers in smart grids



With fast advancements of communication, systems and information technologies, a smart grid (SG) could bring much convenience to users because it could provide a reliable and efficient energy service. The data aggregation (DA) scheme for the SG plays an important role in evaluating information about current energy usage. To achieve the goal of preserving users’ privacy, many DA schemes for the SG have been proposed in last decade. However, how to withstand attacks of internal adversaries is not considered in those schemes. To enhance preservation of privacy, Fan et al. proposed a DA scheme for the SG against internal adversaries. In Fan et al.’s DA scheme, blinding factors are used in evaluating information about current energy usage and the aggregator cannot get the consumption information of any individual user. Fan et al. demonstrated that their scheme was secure against various attacks. However, we find that their scheme suffers from the key leakage problem, i.e., the adversary could extract the user’s private key through the public information. To overcome such serious weakness, this paper proposes an efficient and privacy-preserving DA scheme for the SG against internal attacks. Analysis shows that the proposed DA scheme not only overcome the key leakage problem in Fan et al.’s DA scheme, but also has better performance.


Smart grid Data aggregation Privacy Internal attacker 



The work of J.-H. Lee was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (NRF-2014R1A1A1006770).


  1. 1.
    He, D., Chen, C., Bu, J., Chan, S., Zhang, Y., & Guizani, M. (2012). Secure service provision in smart grid communications. IEEE Communications Magazine, 50(8), 53–61.CrossRefGoogle Scholar
  2. 2.
    Fadlullah, Z., Kato, N., Lu, R., Shen, X., & Nozaki, Y. (2012). Toward secure targeted broadcast in smart grid. IEEE Communications Magazine, 50(5), 150–156.CrossRefGoogle Scholar
  3. 3.
    He, D., Chan, S., Chen, C., & Bu, J. (2014). An enhanced public key infrastructure to secure smart grid wireless communication networks. IEEE Network, 28(1), 10–16.CrossRefGoogle Scholar
  4. 4.
    Gao, J., Xiao, Y., Liu, J., Liang, W., & Chen, C. (2011). A survey of communication/networking in smart grids. Future Generation Computer Systems, 28(2), 391–404.CrossRefGoogle Scholar
  5. 5.
    Li, Q., & Cao, G. (2011). Multicast authentication in the smart grid with onetime signature. IEEE Transactions on Smart Grid, 2(4), 686–696.CrossRefGoogle Scholar
  6. 6.
    Fouda, M., Fadlullah, Z., Kato, N., Lu, R., & Shen, X. (2011). A lightweight message authentication scheme for smart grid communications. IEEE Transactions on Smart Grid, 2(4), 675–685.CrossRefGoogle Scholar
  7. 7.
    Li, H., Lu, R., Zhou, L., Yang, B., & Shen, X. (2014). An efficient merkle-tree-based authentication scheme for smart grid. IEEE Systems Journal, 8(2), 655–663.CrossRefGoogle Scholar
  8. 8.
    Wu, D., & Zhou, C. (2011). Fault-tolerant and scalable key management for smart grid. IEEE Transactions on Smart Grid, 2(2), 375–381.CrossRefGoogle Scholar
  9. 9.
    Tseng, H. (2014). Threshold-based privacy-preserving key management scheme for vehicle-to-grid networks. Applied Mechanics and Materials, 479(1), 978–982.Google Scholar
  10. 10.
    Wan, Z., Wang, G., Yang, Y., & Shi, S. (2014). SKM: Scalable key management for advanced metering infrastructure in smart grids. IEEE Transactions on Industrial Electronics, 61(12), 7055–7066.CrossRefGoogle Scholar
  11. 11.
    Li, F., Luo, B., & Liu, P. (2010) Secure information aggregation for smart grids using homomorphic encryption. In First IEEE international conference on smart grid communications (pp. 327–332).Google Scholar
  12. 12.
    Paillier, P. (1999) A public-key cryptosystem based on composite degree residuosity classes. In EUROCRYPT ‘99 (pp. 223–238).Google Scholar
  13. 13.
    Garcia, F. D., & Jacobs B. (2011). Privacy-friendly energy-metering via homomorphic encryption. In 6th international conference on security and trust management (pp. 226–238).Google Scholar
  14. 14.
    Lu, R., Liang, X., Li, X., Lin, X., & Shen, X. (2012). EPPA: An efficient and privacy-preserving aggregation scheme for secure smart grid communications. IEEE Transactions on Parallel and Distributed Systems, 23(9), 1621–1632.CrossRefGoogle Scholar
  15. 15.
    Zhang, J., Liu, L., Cui, Y., & Chen, Z. (2013). SP2DAS: Self-certified PKC-based privacy-preserving data aggregation scheme in smart grid. International Journal of Distributed Sensor Networks, 2013, 457325. doi: 10.1155/2013/457325.
  16. 16.
    Chen, L., Lu, R., & Cao, Z. (2014). PDAFT: A privacy-preserving data aggregation scheme with fault tolerance for smart grid communications. Peer-to-Peer Networking and Applications. doi: 10.1007/s12083-014-0255-5.
  17. 17.
    Fan, C., Huang, S., & Lai, Y. (2014). Privacy enhanced data aggregation scheme against internal attackers in smart grid. IEEE Transactions on Industrial Informatics, 10(1), 666–675.CrossRefGoogle Scholar
  18. 18.
    Farash, M., & Attari, M. (2014). A secure and efficient identity-based authenticated key exchange protocol for mobile client–server networks. The Journal of Supercomputing, 69(1), 395–411.CrossRefMathSciNetGoogle Scholar
  19. 19.
    Farash, M., & Attari, M. (2014). Cryptanalysis and improvement of a chaotic maps-based key agreement protocol using Chebyshev sequence membership testing. Nonlinear Dynamics, 76(2), 1203–1213.CrossRefMathSciNetMATHGoogle Scholar
  20. 20.
    Mishra, D., Jangirala, S., & Mukhopadhyay, S. (2014). A secure and efficient chaotic map-based authenticated key agreement scheme for telecare medicine information systems. Journal of Medical Systems, 38(10), 1–10.CrossRefGoogle Scholar
  21. 21.
    Xie, Q., Tan, X., Wong, D., Wang, G., Bao, M., & Dong, N. (2014). A practical anonymous authentication protocol for wireless roaming. Security and Communication Networks, 7(8), 1264–1273.CrossRefGoogle Scholar
  22. 22.
    He, D., & Zeadally, S. (2015). Authentication protocol for ambient assisted living system. IEEE Communications Magazine, 35(1), 71–77.CrossRefGoogle Scholar
  23. 23.
    He, D., Kumar, N., & Chilamkurti, N. (2015) A secure temporal-credential-based mutual authentication and key agreement scheme with pseudo identity for wireless sensor networks. Information Sciences. doi: 10.1016/j.ins.2015.02.010.
  24. 24.
    Jiang, Q., Ma, J., Li, G., & Li, X. (2015). Improvement of robust smart-card-based password authentication scheme. International Journal Communication Systems, 28(2), 383–393.CrossRefGoogle Scholar
  25. 25.
    David, P., & Jacque, S. (2000). Security arguments for digital signatures and blind signatures. Journal of Cryptology, 13(3), 361–396.CrossRefMATHGoogle Scholar
  26. 26.
    Wu, T., & Tseng, Y. (2010). An efficient user authentication and key exchange protocol for mobile client–server environment. Computer Networks, 54(9), 1520–1530.CrossRefMATHGoogle Scholar
  27. 27.
    He, D. (2012). An efficient remote user authentication and key agreement protocol for mobile client–server environment from pairings. Ad Hoc Networks, 10(6), 1009–1016.CrossRefGoogle Scholar
  28. 28.
    Kilinc, H. H., & Yanik, T. (2014). A survey of SIP authentication and key agreement schemes. IEEE Communications Surveys & Tutorials, 16(2), 1005–1023.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.State Key Laboratory of Software Engineering, School of ComputerWuhan UniversityWuhanChina
  2. 2.Department of Computer Science and EngineeringThapar UniversityPatialaIndia
  3. 3.Department of Computer Science and EngineeringSangmyung UniversityCheonanRepublic of Korea

Personalised recommendations