EFFECT: an efficient flexible privacy-preserving data aggregation scheme with authentication in smart grid

  • Zhitao Guan
  • Yue Zhang
  • Liehuang ZhuEmail author
  • Longfei Wu
  • Shui Yu
Research Paper


Smart grid is considered as a promising approach to solve the problems of carbon emission and energy crisis. In smart grid, the power consumption data are collected to optimize the energy utilization. However, security issues in communications still present practical concerns. To cope with these challenges, we propose EFFECT, an efficient flexible privacy-preserving aggregation scheme with authentication in smart grid. Specifically, in the proposed scheme, we achieve both data source authentication and data aggregation in high efficiency. Besides, in order to adapt to the dynamic smart grid system, the threshold for aggregation is adjusted according to the energy consumption information of each particular residential area and the time period, which can support fault-tolerance while ensuring individual data privacy during aggregation. Detailed security analysis shows that our scheme can satisfy the desired security requirements of smart grid. In addition, we compare our scheme with existing schemes to demonstrate the effectiveness of our proposed scheme in terms of low computational complexity and communication overhead.


privacy-preserving authentication batch verification smart grid 



This work was partially supported by Beijing Natural Science Foundation (Grant No. 4182060), and Fundamental Research Funds for the Central Universities (Grant No. 2018ZD06).


  1. 1.
    Wang K, Du M, Maharjan S, et al. Strategic honeypot game model for distributed denial of service attacks in the smart grid. IEEE Trans Smart Grid, 2017, 8: 2474–2482CrossRefGoogle Scholar
  2. 2.
    Guan Z T, Si G L, Zhang X S, et al. Privacy-preserving and efficient aggregation based on blockchain for power grid communications in smart communities. IEEE Commun Mag, 2018, 56: 82–88CrossRefGoogle Scholar
  3. 3.
    Xue K P, Li S H, Hong J N, et al. Two-cloud secure database for numeric-related sql range queries with privacy preserving. IEEE Trans Inf Foren Sec, 2017, 12: 1596–1608CrossRefGoogle Scholar
  4. 4.
    Wu J, Dong M X, Ota K, et al. Securing distributed storage for social internet of things using regenerating code and blom key agreement. Peer-to-Peer Netw Appl, 2015, 8: 1133–1142CrossRefGoogle Scholar
  5. 5.
    Erkin Z, Troncoso-Pastoriza J, Lagendijk R, et al. Privacy-preserving data aggregation in smart metering systems: an overview. IEEE Signal Proc Mag, 2013, 30: 75–86CrossRefGoogle Scholar
  6. 6.
    Yan Y, Qian Y, Sharif H, et al. A survey on smart grid communication infrastructures: motivations, requirements and challenges. IEEE Commun Surv Tut, 2013, 15: 5–20CrossRefGoogle Scholar
  7. 7.
    Cho S, Li H, Choi B J. Palda: efficient privacy-preserving authentication for lossless data aggregation in smart grids. In: Proceedings of IEEE International Conference on Smart Grid Communications, 2014. 914–919Google Scholar
  8. 8.
    Guan Z T, Li J, Zhu L H, et al. Toward delay-tolerant flexible data access control for smart grid with renewable energy resources. IEEE Trans Ind Inform, 2017, 13: 3216–3225CrossRefGoogle Scholar
  9. 9.
    Zheng J M, Tan Y A, Zhang Q K, et al. Cross-cluster asymmetric group key agreement for wireless sensor networks. Sci China Inf Sci, 2018, 61: 048103MathSciNetCrossRefGoogle Scholar
  10. 10.
    Guan Z T, Li J, Wu L F, et al. Achieving efficient and secure data acquisition for cloud-supported internet of things in smart grid. IEEE Int Thing J, 2017, 4: 1934–1944CrossRefGoogle Scholar
  11. 11.
    Zhang Z J, Qin Z, Zhu L H, et al. Cost-friendly differential privacy for smart meters: exploiting the dual roles of the noise. IEEE Trans Smart Grid, 2016, 8: 619–626Google Scholar
  12. 12.
    Li S H, Xue K P, Yang Q Y, et al. PPMA: privacy-preserving multisubset data aggregation in smart grid. IEEE Trans Ind Inf, 2018, 14: 462–471CrossRefGoogle Scholar
  13. 13.
    Li S H, Zhang X, Xue K P, et al. Privacy-preserving prepayment based power request and trading in smart grid. China Commun, 2018, 15: 14–27CrossRefGoogle Scholar
  14. 14.
    Xiao Y, Tan Y A, Sun Z Z, et al. A fault-tolerant and energy-efficient continuous data protection system. J Amb Intel Hum Comp, 2018. doi: 10.1007/s12652-018-0726-2Google Scholar
  15. 15.
    Przydatek B, Song D, Perrig A. Sia: secure information aggregation in sensor networks. In: Proceedings of the 1st International Conference on Embedded Networked Sensor Systems, 2003. 255–265CrossRefGoogle Scholar
  16. 16.
    Shi E, Chan T H, Rieffel E, et al. Privacy-preserving aggregation of time-series data. In: Proceedings of the 18th Annual Network and Distributed System Security Conference, 2011Google Scholar
  17. 17.
    Kim Y S, Heo J. Device authentication protocol for smart grid systems using homomorphic Hash. J Commun Netw, 2012, 14: 606–613CrossRefGoogle Scholar
  18. 18.
    Lu R X, Liang X H, Li X, et al. EPPA: an efficient and privacy-preserving aggregation scheme for secure smart grid communications. IEEE Trans Paral Distrib Syst, 2012, 23: 1621–163CrossRefGoogle Scholar
  19. 19.
    Chen L, Lu R X, Cao Z F. Pdaft: a privacy-preserving data aggregation scheme with fault tolerance for smart grid communications. Peer Peer Netw Appl, 2015, 8: 1122–1132CrossRefGoogle Scholar
  20. 20.
    Shi Z G, Sun R X, Lu R X, et al. Diverse grouping-based aggregation protocol with error detection for smart grid communications. IEEE Trans Smart Grid, 2015, 6: 2856–2868CrossRefGoogle Scholar
  21. 21.
    Wu J, Dong M X, Ota K, et al. Big data analysis-based secure cluster management for optimized control plane in software-defined networks. IEEE Trans Netw Serv Manage, 2018, 15: 27–38CrossRefGoogle Scholar
  22. 22.
    Zhang X S, Tan Y A, Xue Y, et al. Cryptographic key protection against FROST for mobile devices. Cluster Comput, 2017, 20: 2393–2402CrossRefGoogle Scholar
  23. 23.
    Gao S, Ma X D, Zhu J M, et al. APRS: a privacy-preserving location-aware recommender system based on differentially private histogram. Sci China Inf Sci, 2017, 60: 119103CrossRefGoogle Scholar
  24. 24.
    Mustafa M A, Zhang N, Kalogridis G, et al. Dep2sa: a decentralized efficient privacy-preserving and selective aggregation scheme in advanced metering infrastructure. IEEE Access, 2016, 3: 2828–2846CrossRefGoogle Scholar
  25. 25.
    Wang T, Zeng J D, Bhuiyan M Z A, et al. Trajectory privacy preservation based on a fog structure for cloud location services. IEEE Access, 2017, 5: 7692–7701CrossRefGoogle Scholar
  26. 26.
    Shen H, Zhang M W, Shen J. Efficient privacy-preserving cube-data aggregation scheme for smart grids. IEEE Trans Inf Foren Secur, 2017, 12: 1369–1381CrossRefGoogle Scholar
  27. 27.
    Fouda M M, Fadlullah Z M, Kato N, et al. A lightweight message authentication scheme for smart grid communications. IEEE Trans Smart Grid, 2011, 2: 675–685CrossRefGoogle Scholar
  28. 28.
    Paillier P. Public-key cryptosystems based on composite degree residuosity classes. In: Proceedings of International Conference on Theory and Application of Cryptographic Techniques, 1999. 223–238Google Scholar
  29. 29.
    Blakley G R. Safeguarding cryptographic keys. In: Proceeding of International Workshop on Managing Requirements Knowledge, 1979. 313–317Google Scholar
  30. 30.
    Yu Y, Xue L, Au M H, et al. Cloud data integrity checking with an identity-based auditing mechanism from RSA. Future Gener Comput Syst, 2016, 62: 85–91CrossRefGoogle Scholar
  31. 31.
    Bellare M, Garay J A, Rabin T. Fast batch verification for modular exponentiation and digital signatures. In: Proceeding of International Conference on the Theory and Applications of Cryptographic Techniques, 1998. 236–250Google Scholar
  32. 32.
    Li H W, Lin X D, Yang H M, et al. EPPDR: an efficient privacy-preserving demand response scheme with adaptive key evolution in smart grid. IEEE Trans Paral Distrib Syst, 2014, 25: 2053–2064CrossRefGoogle Scholar
  33. 33.
    Dan B, Lynn B, Shacham H. Short signatures from the weil pairing. In: Proceeding of International Conference on the Theory and Application of Cryptology and Information Security, 2001. 514–532Google Scholar
  34. 34.
    Failla P. Privacy preserving processing of biometric templates by homomorphic encryption. Dissertation for Ph.D. Degree. Siena: University of Siena, 2011Google Scholar
  35. 35.
    Lynn B. PBC: the pairing-based cryptography library. Version 0.5.14, 2013. Scholar

Copyright information

© Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Zhitao Guan
    • 1
  • Yue Zhang
    • 1
  • Liehuang Zhu
    • 2
    Email author
  • Longfei Wu
    • 3
  • Shui Yu
    • 4
  1. 1.School of Control and Computer EngineeringNorth China Electric Power UniversityBeijingChina
  2. 2.School of ComputerBeijing Institute of TechnologyBeijingChina
  3. 3.Department of Mathematics and Computer ScienceFayetteville State UniversityFayettevilleUSA
  4. 4.School of Information TechnologyDeakin UniversityBurwoodAustralia

Personalised recommendations