An Enhanced Anonymous Identification Scheme for Smart Grids

  • Shanshan Ge
  • Peng ZengEmail author
  • Kim-Kwang Raymond Choo
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 580)


In smart grid communications, preserving the privacy of consumers’ electricity usage data is a topic of interest for power providers and consumers, as well as regulators. Sui (IEEE Trans. Smart Grid, 2016) proposed a new threshold-based anonymous identification (TAI) scheme for smart grid communications and claimed that TAI scheme achieves unlinkability, strong anonymity, non-frameability, identification, and integrity. In this paper, however, we demonstrate that due to a flawed Decisional Diffie–Hellman assumption in a bilinear group, TAI scheme is unlikely to achieve unlinkability, in violation of their security claims. Specifically, an adversary \( {\mathcal{A}} \) can easily link different consumption reports from the same consumer during the anonymous consumption reporting part and link a disavowal proof of a compliant smart meter to its previous signature. We then propose an enhanced anonymous identification scheme to eliminate the security vulnerability in the scheme, in the sense that no one can determine whether two different consumption reports are from the same consumer.


Smart grid Privacy-preserving Unlinkability Demand-response Identification 



P. Zeng is the corresponding author. The work is supported in part by the NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization under Grant No. U1509219, the Shanghai Natural Science Foundation under Grant No. 17ZR1408400, the National Natural Science Foundation of China under Grant No. 61632012, and the Shanghai Sailing Program under Grant No. 17YF1404300.


  1. 1.
    Heydt, G.T.: The next generation of power distribution systems. IEEE Trans. Smart Grid 1(3), 225–235 (2010)CrossRefGoogle Scholar
  2. 2.
    Qdr, Q.: Benefits of demand response in electricity markets and recommendations for achieving them. US department of energy (2006)Google Scholar
  3. 3.
    Sui, Z., Niedermeier, M., de Meer, H.: TAI: a threshold-based anonymous identification scheme for demand-response in smart grids. IEEE Trans. Smart Grid (2016)Google Scholar
  4. 4.
    Lu, R., Liang, X., Li, X., et al.: Eppa: an efficient and privacy-preserving aggregation scheme for secure smart grid communications. IEEE Trans. Parallel Distrib. Syst. 23(9), 1621–1631 (2012)CrossRefGoogle Scholar
  5. 5.
    Sui, Z., Niedermeier, M., de Meer, H.: RESA: a robust and efficient secure aggregation scheme in smart grids. In: International conference on critical information infrastructures security. Springer International Publishing, pp. 171–182 (2015)Google Scholar
  6. 6.
    Chen, L., Lu, R., Cao, Z., et al.: MuDA: multifunctional data aggregation in privacy-preserving smart grid communications. Peer-to-Peer Netw. Appl. 8(5), 777–792 (2015)CrossRefGoogle Scholar
  7. 7.
    Liu, Z., Choo, R., Zhao, M.: Practical-oriented protocols for privacy-preserving outsourced big data analysis: challenges and future research directions. Comput. Secur. (2016)Google Scholar
  8. 8.
    Li, B., Lu, R., Wang, W., et al.: Distributed host-based collaborative detection for false data injection attacks in smart grid cyber-physical system. J. Parallel Distrib. Comput. 103, 32–41 (2017)CrossRefGoogle Scholar
  9. 9.
    Li, B., Lu, R., Wang, W., et al.: DDOA: a dirichlet-based detection scheme for opportunistic attacks in smart grid cyber-physical system. IEEE Trans. Inf. Forensics Secur. 11(11), 2415–2425 (2016)CrossRefGoogle Scholar
  10. 10.
    Jiang, R., Lu, R., Choo, K.K.R.: Achieving high performance and privacy-preserving query over encrypted multidimensional big metering data. Future Gener. Comput. Syst. (2016)Google Scholar
  11. 11.
    Quick, D., Choo, K.K.R.: Big forensic data management in heterogeneous distributed systems: quick analysis of multimedia forensic data. Softw.: Pract. Exp. (2016)Google Scholar
  12. 12.
    Chim, T.W., Yiu, S.M., Hui, L.C.K. et al.: PASS: privacy-preserving authentication scheme for smart grid network. In: Smart Grid Communications (SmartGridComm), 2011 IEEE International Conference on. IEEE, pp. 196–201 (2011)Google Scholar
  13. 13.
    He, D., Chen, C., Bu, J., et al.: Secure service provision in smart grid communications. IEEE Commun. Mag. 50(8), (2012)Google Scholar
  14. 14.
    Gong, Y., Cai, Y., Guo, Y., et al.: A privacy-preserving scheme for incentive-based demand response in the smart grid. IEEE Trans. Smart Grid 7(3), 1304–1313 (2016)CrossRefGoogle Scholar
  15. 15.
    Huang, X., Liu, J.K., Tang, S., et al.: Cost-effective authentic and anonymous data sharing with forward security. IEEE Trans. Comput. 64(4), 971–983 (2015)CrossRefzbMATHMathSciNetGoogle Scholar
  16. 16.
    Liu, X., Zhang, Y., Wang, B., et al.: An anonymous data aggregation scheme for smart grid systems. Secur. Commun. Netw. 7(3), 602–610 (2014)CrossRefGoogle Scholar
  17. 17.
    Joux, A., Nguyen, K.: Separating decision Diffie–Hellman from computational Diffie–Hellman in cryptographic groups. J. Cryptol. 16(4), 239–247 (2003)CrossRefzbMATHMathSciNetGoogle Scholar
  18. 18.
    Boneh, D.: A brief look at pairings based cryptography. In: Foundations of Computer Science, 2007. FOCS’07. 48th Annual IEEE Symposium on. IEEE, 2007, pp. 19–26Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Shanshan Ge
    • 1
  • Peng Zeng
    • 1
    Email author
  • Kim-Kwang Raymond Choo
    • 2
  1. 1.The Shanghai Key Laboratory of Trustworthy ComputingEast China Normal UniversityShanghaiChina
  2. 2.The Department of Information Systems and Cyber SecurityUniversity of Texas at San AntonioSan AntonioUSA

Personalised recommendations