Skip to main content
SpringerLink
Log in

FGDA: Fine-grained data analysis in privacy-preserving smart grid communications

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

Abstract

In a smart grid environment, smart meters periodically collect and report information such as electricity consumption of users to a control center for timely monitoring, billing and other analytical purposes. There is, however, a need to ensure the privacy of user data, particularly when the data is combined with data from other sources. In this paper, we propose a new fine-grained data analysis (hereafter referred to as FGDA) scheme for privacy preserving smart grid communications. FGDA is designed to compute multifunctional data analysis (such as average, variance, and skewness) based on users’ ciphertexts, as well as supporting fault tolerance feature. We remark that FGDA can still function when some smart meters fail. Compared to existing schemes providing both the properties of multifunction and fault tolerance, FGDA is more efficient in terms of computation overheads. This is because FDGA does not use bilinear map or Pollard’s lambda method during decryption. We also demonstrate that FGDA achieves a higher communication efficiency, as the gateway only needs to send the ciphertext to the control center once even for different statistical functions.

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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Heydt GT (2010) The next generation of power distribution systems. IEEE Trans Smart Grid 1(3):225–235

    Article  Google Scholar 

  2. Li B, Lu R, Wang W et al (2017) Distributed host-based collaborative detection for false data injection attacks in smart grid cyber-physical system. J Parallel Distrib Comput 103:32–41

    Article  Google Scholar 

  3. Liu J, Yu X, Xu Z et al (2017) A cloud-based taxi trace mining framework for smart city. Soft Practice and Exper 47(8):1081– 1094

    Google Scholar 

  4. Choo KKR (2014) A conceptual interdisciplinary plug-and-play cyber security framework. In: ICTs and the millennium development goals. Springer US, pp 81–99

  5. Yan Y, Qian Y, Sharif H et al (2012) A survey on cyber security for smart grid communications. IEEE Commun Surv Tutorials 14(4):998–1010

  6. Li X, Liang X, Lu R et al (2012) Securing smart grid: cyber attacks, countermeasures, and challenges. IEEE Commun Mag 8(50):38–45

  7. Pindoriya NM, Dasgupta D, Srinivasan D et al (2013) Infrastructure security for smart electric grids: a survey. In: Optimization and security challenges in smart power grids. Springer Berlin Heidelberg, pp 161–180

  8. Bao H, Lu R (2015) A new differentially private data aggregation with fault tolerance for smart grid communications. IEEE Internet Things J 2(3):248–258

    Article  Google Scholar 

  9. Chen L, Lu R, Cao Z (2015) PDAFT: A privacy-preserving data aggregation scheme with fault tolerance for smart grid communications. Peer-to-Peer Netw Appl 8(6):1122–1132

    Article  Google Scholar 

  10. Chen L, Lu R, Cao Z et al (2015) MuDA: Multifunctional data aggregation in privacy-preserving smart grid communications. Peer-to-peer Netw Appl 8(5):777–792

    Article  Google Scholar 

  11. Erkin Z (2015) Private data aggregation with groups for smart grids in a dynamic setting using CRT. In: 2015 IEEE international workshop on information forensics and security (WIFS), pp 1–6

  12. Erkin Z, Tsudik G (2012) Private computation of spatial and temporal power consumption with smart meters. In: ACNS, vol 12, pp 561–577

  13. Lu R, Alharbi K, Lin X et al (2015) A novel privacy-preserving set aggregation scheme for smart grid communications. In: 2015 IEEE global communications conference (GLOBECOM) , pp 1–6

  14. Lu R, Liang X, Li X et al (2012) Eppa: An efficient and privacy-preserving aggregation scheme for secure smart grid communications. IEEE Trans Parallel Distrib Syst 23(9):1621–1631

    Article  Google Scholar 

  15. Ni J, Zhang K, Lin X et al (2016) EDAT: Efficient data aggregation without TTP for privacy-assured smart metering. In: 2016 IEEE international conference on communications (ICC), pp 1–6

  16. Shi Z, Sun R, Lu R et al (2015) Diverse grouping-based aggregation protocol with error detection for smart grid communications. IEEE Trans Smart Grid 6(6):2856–2868

    Article  Google Scholar 

  17. Xie CR, Zhang RY (2015) Privacy-preserving power consumption data measuring protocol for smart grid. In: Proceedings of international conference on computer information systems and industrial applications, CISIA

  18. Paillier P (1999) Public-key cryptosystems based on composite degree residuosity classes. In: Eurocrypt, vol 99, pp 223–238

  19. Boneh D, Goh EJ, Nissim K (2005) Evaluating 2-DNF formulas on ciphertexts, vol 3378, pp 325–341

  20. Hu L, Evans D (2003) Secure aggregation for wireless networks. In: 2003 symposium on applications and the internet workshops, 2003. Proceedings, pp 384–391

  21. Mahimkar A, Rappaport TS (2004) SecureDAV: a secure data aggregation and verification protocol for sensor networks. In: Global telecommunications conference, 2004. GLOBECOM’04, vol 4. IEEE, pp 2175–2179

  22. Przydatek B, Song D, Perrig A (2003) SIA: Secure information aggregation in sensor networks. In: Proceedings of the 1st international conference on Embedded networked sensor systems, pp 255–265

  23. Sui Z, Niedermeier M, de Meer H (2015) RESA: a robust and efficient secure aggregation scheme in smart grids. In: International conference on critical information infrastructures security. Springer, Cham, pp 171–182

  24. Liang X, Li X, Lu R et al (2013) UDP: Usage-based dynamic pricing with privacy preservation for smart grid. IEEE Trans Smart Grid 4(1):141–150

    Article  Google Scholar 

  25. Lin HY, Tzeng WG, Shen ST et al (2012) A practical smart metering system supporting privacy preserving billing and load monitoring. In: Applied cryptography and network security. Springer Berlin/Heidelberg, pp 544–560

  26. Kursawe K, Danezis G, Kohlweiss M (2011) Privacy-friendly aggregation for the smart-grid. In: International symposium on privacy enhancing technologies symposium. Springer, Berlin, Heidelberg, pp 175–191

Download references

Acknowledgments

The work was supported in part by the NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization under Grant No. U1509219, the National Natural Science Foundation of China under Grant No. 61632012, the Shanghai Natural Science Foundation under Grant No. 17ZR1408400, and the Shanghai Sailing Program under Grant No. 17YF1404300. This work and the preparation of this publication were funded in part by monies provided by CPS Energy through an agreement with The University of Texas at San Antonio.

Author information

Authors and Affiliations

  1. Shanghai Key Laboratory of Trustworthy Computing, East China Normal University, Shanghai, China

    Shanshan Ge & Peng Zeng

  2. Faculty of Computer Science, University of New Brunswick, Fredericton, NB, Canada

    Rongxing Lu

  3. Department of Information Systems and Cyber Security and Department of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, TX, 78249, USA

    Kim-Kwang Raymond Choo

Authors
  1. Shanshan Ge
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Peng Zeng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rongxing Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kim-Kwang Raymond Choo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Peng Zeng.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ge, S., Zeng, P., Lu, R. et al. FGDA: Fine-grained data analysis in privacy-preserving smart grid communications. Peer-to-Peer Netw. Appl. 11, 966–978 (2018). https://doi.org/10.1007/s12083-017-0618-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12083-017-0618-9

Keywords

Search

Navigation