Skip to main content
SpringerLink
Log in

Privacy-preserving aggregate signcryption scheme with allowing dynamic updating of pseudonyms for fog-based smart grids

  • Published:
Peer-to-Peer Networking and Applications Aims and scope Submit manuscript

Abstract

Fog-based smart grids have been a hit research field in recent years, providing low latency, location-sensitive and latency-aware local data management by connecting the cloud and edge devices. Traditional data aggregation schemes adopt encryption-then-signature paradigm in smart grids, which requires encryption and signature algorithms to be performed separately or in sequence. Additionally, the privacy of users is still at risk of disclosure and the proliferation of pseudonyms is prominent. In order to solve the above problems, in this paper we propose a novel privacy-preserving aggregate signcryption and user query scheme with allowing dynamic updating of pseudonyms for fog-based smart grids. Firstly, a privacy-preserving data aggregation method based on certificateless signcryption is proposed. In order to solve the problem of complex pseudonym update and potential risk caused by not updating for too long, we specially design a dynamic update rule according to the use characteristics of pseudonym. Secondly, when the user’s pseudonym is dynamically updated, the user query function is designed according to the time series data of each user. This method has been applied to the fog-based smart grids scenario creatively and introduces dynamic updating pseudonym into fog-based smart grids. Finally, theoretical analysis and extensive simulation experiments are designed to evaluate the performance of the scheme. Experimental results show that our scheme has better performance than existing schemes.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Bonomi F, Milito R, Zhu J, Addepalli S (2012) Fog computing and its role in the internet of things, In Proceedings of the first edition of the MCC workshop on Mobile cloud computing, pp. 13-16

  2. Stojmenovic I, Wen S (2014) The fog computing paradigm: Scenarios and security issues, In 2014 federated conference on computer science and information systems, pp. 1-8

  3. Dastjerdi AV, Buyya R (2016) Fog computing: Helping the Internet of Things realize its potential. Computer 49(8):112–116

    Article  Google Scholar 

  4. Chiang M, Ha S, Risso F, Zhang T, Chih-Lin I (2017) Clarifying fog computing and networking: 10 questions and answers. IEEE Commun Mag 55(4):18–20

    Article  Google Scholar 

  5. Tang B, Chen Z, Hefferman G, Pei S, Wei T, He H, Yang Q (2017) Incorporating intelligence in fog computing for big data analysis in smart cities. IEEE Trans Ind Inf 13(5):2140–2150

    Article  Google Scholar 

  6. Okay FY, Ozdemir S (2016) A fog computing based smart grid model, In 2016 international symposium on networks. Comput Commun (ISNCC), pp. 1-6. IEEE

  7. Ni J, Zhang K, Lin X, Shen XS (2017) Balancing security and efficiency for smart metering against misbehaving collectors. IEEE Trans Smart Grid 10(2):1225–1236

    Article  Google Scholar 

  8. Zahoor S, Javaid S, Javaid N, Ashraf M, Ishmanov F, Afzal MK (2018) Cloud-fog-based smart grid model for efficient resource management. Sustainability 10(6):2079–2079

    Article  Google Scholar 

  9. Zahoor S, Javaid N, Khan A, Ruqia B, Muhammad FJ, Zahid M (2018) A cloud-fog-based smart grid model for efficient resource utilization. In 2018 14th International Wireless Communications & Mobile Computing Conference (IWCMC), pp. 1154-1160. IEEE

  10. Bukhsh R, Javaid N, Ali Khan Z, Ishmanov F, Afzal MK, Wadud Z (2018) Towards fast response, reduced processing and balanced load in fog-based data-driven smart grid. Energies 11(12):3345–3345

    Article  Google Scholar 

  11. Li G, Wu J, Li J, Wang K, Ye T (2018) Service popularity-based smart resources partitioning for fog computing-enabled industrial Internet of Things. IEEE Trans Ind Inf 14(10):4702–4711

    Article  Google Scholar 

  12. Wen M, Chen S, Lu R, Li B, Chen S (2019) Security and efficiency enhanced revocable access control for fog-based smart grid system. IEEE Access 7:137968–137981

    Article  Google Scholar 

  13. Lu R, Heung K, Lashkari AH, Ghorbani AA (2017) A lightweight privacy-preserving data aggregation scheme for fog computing-enhanced IoT. IEEE Access 5:3302–3312

    Article  Google Scholar 

  14. Zhu L, Li M, Zhang Z, Xu C, Zhang R, Du X, Guizani N (2019) Privacy-preserving authentication and data aggregation for fog-based smart grid. IEEE Commun Mag 57(6):80–85

    Article  Google Scholar 

  15. Liu JN, Weng J, Yang A, Chen Y, Lin X (2019) Enabling efficient and privacy-preserving aggregation communication and function query for fog computing-based smart grid. IEEE Trans Smart Grid 11(1):247–257

    Article  Google Scholar 

  16. Li K, Yang Y, Wang S, Shi R, Li J (2021) A lightweight privacy-preserving and sharing scheme with dual-blockchain for intelligent pricing system of smart grid. Comput Secur 103:102189

    Article  Google Scholar 

  17. Saleem A, Khan A, Malik SUR, Pervaiz H, Malik H, Alam M, Jindal A (2019) FESDA: Fog-enabled secure data aggregation in smart grid IoT network. IEEE Internet Things J 7(7):6132–6142

    Article  Google Scholar 

  18. Zhao S, Li F, Li H, Lu R, Ren S, Bao H, Han S (2020) Smart and practical privacy-preserving data aggregation for fog-based smart grids. IEEE Trans Inf Forensics Secur 16:521–536

    Article  Google Scholar 

  19. Shen X, Zhu L, Xu C, Sharif K, Lu R (2020) A privacy-preserving data aggregation scheme for dynamic groups in fog computing. Inf Sci 514:118–130

    Article  Google Scholar 

  20. Barbosa M, Farshim P (2008) Certificateless signcryption, In Proceedings of the 2008 ACM symposium on Information. Computer and Communications Security, pp. 369-372

  21. Weng J, Yao G, Deng RH, Chen MR, Li X (2011) Cryptanalysis of a certificateless signcryption scheme in the standard model. Inf Sci 181(3):661–667

    Article  MathSciNet  Google Scholar 

  22. Miao S, Zhang F, Li S, Mu Y (2013) On security of a certificateless signcryption scheme. Inf Sci 232:475–481

    Article  MathSciNet  Google Scholar 

  23. Eslami Z, Pakniat N (2014) Certificateless aggregate signcryption: Security model and a concrete construction secure in the random oracle model. J Comput Inf Sci 26(3):276–286

    Google Scholar 

  24. Cui M, Han D, Wang J (2019) An efficient and safe road condition monitoring authentication scheme based on fog computing. IEEE Internet Things J 6(5):9076–9084

    Article  Google Scholar 

  25. Mandal S, Bera B, Sutrala AK, Das AK, Choo KKR, Park Y (2020) Certificateless-Signcryption-Based three-factor user access control scheme for IoT environment. IEEE Internet Things J 7(4):3184–3197

    Article  Google Scholar 

  26. Xiong H, Zhao Y, Hou Y, Huang X, Jin C, Wang L, Kumari S (2020) Heterogeneous signcryption with equality test for IIoT environment. IEEE Internet of Things Journal, pp. 1–1

  27. Gao HH, Liu C, Yin YY, Xu YS, Li Y (2021) A hybrid approach to trust node assessment and management for VANETs cooperative data communication: Historical interaction perspective. IEEE Intelligent Transportation Systems Transactions. https://doi.org/10.1109/TITS.2021.3129458

  28. Gao HH, Zhang YD, Miao HK, Barroso RJD, Yang XX (2021) SDTIOA: Modeling the timed privacy requirements of IoT service composition: a user interaction perspective for automatic transformation from BPEL to timed automata. ACM/Springer Mobile Networks and Applications (MONET). https://doi.org/10.1007/s11036-021-01846-x

  29. Gao HH, Qin X, Barroso RJD, Hussain W, Xu YS, Yin YY (2022) Collaborative learning-based industrial IoT API recommendation for software-defined devices: the implicit knowledge discovery perspective. IEEE Transactions on Emerging Topics in Computational Intelligence(TETCI) 6(1):66–76. https://doi.org/10.1109/TETCI.2020.3023155

  30. Huang YZ, Xu HH, Gao HH, Ma XJ, Hussain W (2021) SSUR: An approach to optimizing virtual machine allocation strategy based on user requirements for cloud data center. IEEE Transactions on Green Communications and Networking 5(2):670-681. https://doi.org/10.1109/TGCN.2021.3067374

  31. Ma XJ, Xu HH, Gao HH, Bian MJ (2021) Real-time multiple-workflow scheduling in cloud environments. IEEE Transactions on Network and Service Management(TNSM) 18(4):4002-4018. https://doi.org/10.1109/TNSM.2021.3125395.5

  32. Mahmud R, Buyya R (2019) Modelling and simulation of fog and edge computing environments using iFogSim toolkit. Principles and paradigms. Fog and edge computing, pp 1–35

    Google Scholar 

  33. Ahene E, Qin Z, Adusei AK, Li F (2019) Efficient signcryption with proxy re-encryption and its application in smart grid. IEEE Internet Things J 6(6):9722–9737

    Article  Google Scholar 

  34. Pointcheval D, Stern J (2000) Security arguments for digital signatures and blind signatures. J Cryptol 13(3):361–396

    Article  Google Scholar 

  35. Liu Y, Guo W, Fan CI, Chang L, Cheng C (2018) A practical privacy-preserving data aggregation (3PDA) scheme for smart grid. IEEE Trans Ind Inf 15(3):1767–1774

    Article  Google Scholar 

Download references

Acknowledgements

The authors wish to thank anonymous reviewers.

Author information

Authors and Affiliations

  1. School of control and computer engineering, North China Electric Power University, Beijing, 102206, China

    Kunchang Li, Yifan Yang & Shuhao Wang

Authors
  1. Kunchang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yifan Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shuhao Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kunchang Li.

Ethics declarations

Conflicts of interest

The authors declared that they have no conflicts of interest to this work.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, K., Yang, Y. & Wang, S. Privacy-preserving aggregate signcryption scheme with allowing dynamic updating of pseudonyms for fog-based smart grids. Peer-to-Peer Netw. Appl. 15, 2101–2115 (2022). https://doi.org/10.1007/s12083-022-01343-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12083-022-01343-2

Keywords

2021 MSC

Search

Navigation