Privacy-Preserving and Traceable Data Aggregation in Energy Internet

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 747)

Abstract

Energy Internet is considered as a promising approach to solve the problems of energy crisis and carbon emission. It needs to collect user’s real-time data for optimizing the energy utilization. Edge nodes like GWs (gateway) are used for data aggregation to improve the efficiency of the system. Due to a large number of GWs are widely distributed and difficult to be managed, which brings potential security threats for the Energy Internet. Existing data aggregation schemes fails in preventing the adversary from controlling or destroying GWs. In this paper, we propose an IBE-based Device Traceable Privacy-Preserving Aggregation Scheme, named IBE-DTPPA. Increasing the RA (Residential Area) users’ data aggregation integrity verification by BGN Cryptosystem; using IBE Cryptosystem to encrypt aggregation data, calculating ciphertext based on GW’s dynamic ID, realizing the target GW traceability; choosing CC (Control Center) dynamic identity information as public key to realize CC authentication, preventing adversary from using CC’s identity fraudulently. Through extensive analysis, we demonstrate that IBE-DTPPA resists various security threats, and can trace target GW efficiently.

Keywords

Device tracking Authentication Data aggregation Energy Internet 

Notes

Acknowledgement

This work is partially supported by Natural Science Foundation of China under grant 61402171, the Fundamental Research Funds for the Central Universities under grant 2016MS29.

References

  1. 1.
    Wang, K., Yu, J., Yu, Y., et al.: A survey on Energy Internet: architecture, approach, and emerging technologies. IEEE Syst. J. PP(99), 1–14 (2017)CrossRefGoogle Scholar
  2. 2.
    Guan, Z., Li, J., Zhu, L., Zhang, Z., Du, X., Guizani, M.: Towards delay-tolerant flexible data access control for smart grid with renewable energy resources. IEEE Trans. Ind. Inform. 13(6), 3216–3225 (2017)CrossRefGoogle Scholar
  3. 3.
    Wang, K., Ouyang, Z., Krishnan, R., et al.: A game theory-based energy management system using price elasticity for smart grids. IEEE Trans. Ind. Inform. 11(6), 1607–1616 (2015)CrossRefGoogle Scholar
  4. 4.
    Guan, Z., Li, J., Wu, L., Zhang, Y., Wu, J., Du, X.: Achieving efficient and secure data acquisition for cloud-supported Internet of Things in smart grid. IEEE Internet Things J. 4(6), 1934–1944 (2017)CrossRefGoogle Scholar
  5. 5.
    Davies, S.: Internet of energy [smart grid security]. Eng. Technol. 5(1), 1–2 (2010)MathSciNetGoogle Scholar
  6. 6.
    Efthymiou, C., Kalogridis, G.: Smart grid privacy via anonymization of smart metering data In: First IEEE International Conference on Smart Grid Communications. IEEE, pp. 238–243 (2010)Google Scholar
  7. 7.
    Tan, X., Zheng, J., Zou, C., et al.: Pseudonym-based privacy-preserving scheme for data collection in smart grid. Int. J. Ad Hoc Ubiquitous Comput. 22(2), 120 (2016)CrossRefGoogle Scholar
  8. 8.
    Guan, Z., Si, G., Wu, J., et al.: Utility-privacy tradeoff based on random data obfuscation in internet of energy. IEEE Access 5, 3250–3262 (2017)CrossRefGoogle Scholar
  9. 9.
    Beussink, A., Akkaya, K., Senturk, I.F., Mahmoud, M.M.E.A.: Preserving consumer privacy on IEEE 802.11s-based smart grid AMI networks using data obfuscation. In: Proceedings of the IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pp. 658–663, April 2014Google Scholar
  10. 10.
    Shi, E., Chan, T.-H.H., Rieffel, E.G., Chow, R., Song, D.: Privacy-preserving aggregation of time-series data. In: NDSS, vol. 2, p. 4 (2011)Google Scholar
  11. 11.
    Kim, Y.S., Heo, J.: Device authentication protocol for smart grid systems using homomorphic hash. J. Commun. Netw. 14(6), 606–613 (2012)CrossRefGoogle Scholar
  12. 12.
    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
  13. 13.
    Chen, L., Lu, R., Cao, Z.: PDAFT: a privacy-preserving data aggregation scheme with fault tolerance for smart grid communications. PeerPeer Netw. Appl. 8(6), 1122–1132 (2015)CrossRefGoogle Scholar
  14. 14.
    Shi, Z., Sun, R., Lu, R., Chen, L., Chen, J., Shen, X.S.: Diverse grouping-based aggregation protocol with error detection for smart grid communications. IEEE Trans. Smart Grid 6(6), 2856–2868 (2015)CrossRefGoogle Scholar
  15. 15.
    Han, S., Zhao, S., Li, Q., Ju, C.-H., Zhou, W.: PPM-HDA: privacy-preserving and multifunctional health data aggregation with fault tolerance. IEEE Trans. Inf. Forensics Secur. 11(9), 1940–1955 (2015)CrossRefGoogle Scholar
  16. 16.
    Hua, J., Tang, A., Fang, Y., Shen, Z., Zhong, S.: Privacy-preserving utility verification of the data published by non-interactive differentially private mechanisms. IEEE Trans. Inf. Forensics Secur. 11(10), 2298–2311 (2016)CrossRefGoogle Scholar
  17. 17.
    Wang, H., Qin, B., Wu, Q., et al.: TPP: traceable privacy-preserving communication and precise reward for vehicle-to-grid networks in smart grids. IEEE Trans. Inf. Forensics Secur. 10(11), 2340–2351 (2015)CrossRefGoogle Scholar
  18. 18.
    Xiao, Y., Du, X., Zhang, J., Guizani, S.: Internet Protocol Television (IPTV): the killer application for the next generation internet. IEEE Commun. Mag. 45(11), 126–134 (2007)CrossRefGoogle Scholar
  19. 19.
    Du, X., Chen, H.H.: Security in wireless sensor networks. IEEE Wirel. Commun. Mag. 15(4), 60–66 (2008)CrossRefGoogle Scholar
  20. 20.
    Xiao, Y., Rayi, V., Sun, B., Du, X., Hu, F., Galloway, M.: A survey of key management schemes in wireless sensor networks. J. Comput. Commun. 30(11–12), 2314–2341 (2007)CrossRefGoogle Scholar
  21. 21.
    Du, X., Xiao, Y., Guizani, M., Chen, H.H.: An effective key management scheme for heterogeneous sensor networks. Ad Hoc Netw. 5(1), 24–34 (2007)CrossRefGoogle Scholar
  22. 22.
    Du, X., Guizani, M., Xiao, Y., Chen, H.H.: A routing-driven elliptic curve cryptography based key management scheme for heterogeneous sensor networks. IEEE Trans. Wirel. Commun. 8(3), 1223–1229 (2009)CrossRefGoogle Scholar
  23. 23.
    Du, X., Guizani, M., Xiao, Y., Chen, H.H.: Secure and efficient time synchronization in heterogeneous sensor networks. IEEE Trans. Veh. Technol. 57(4), 2387–2394 (2008)CrossRefGoogle Scholar
  24. 24.
    Du, X., Xiao, Y., Chen, H.H., Wu, Q.: Secure cell relay routing protocol for sensor networks. Wirel. Commun. Mob. Comput. 6(3), 375–391 (2006)CrossRefGoogle Scholar
  25. 25.
    Shamir, A.: Identity-based cryptosystems and signature schemes. In: Blakley, G.R., Chaum, D. (eds.) CRYPTO 1984. LNCS, vol. 196, pp. 47–53. Springer, Heidelberg (1985).  https://doi.org/10.1007/3-540-39568-7_5CrossRefGoogle Scholar
  26. 26.
    Gallant, R., Lambert, R., Vanstone, S.: Improving the parallelized pollard lambda search on anomalous binary curves. Math. Comput. Am. Math. Soc. 69(232), 1699–1705 (2000)MathSciNetCrossRefMATHGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.North China Electric Power UniversityBeijingChina

Personalised recommendations