An Efficient and Secure Range Query Scheme for Encrypted Data in Smart Grid

  • Xiaoli Zeng
  • Min Hu
  • Nuo Yu
  • Xiaohua Jia
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 747)


In smart grid information systems, the electricity usage data should be audited by data users, such as the market analysts to finish their tasks. Besides that, electricity company always outsources the data to the cloud server (CS) to release its data management pressure. Since the CS is untrusted and the detailed electricity usage data contains users’ privacy, the privacy concern of the data and data users’ queries is raised. Although many schemes have been proposed to achieve the encrypted data query in smart grid, they are not applied well due to the numeric attributes in electricity usage data and privacy concern in smart grid application. In this paper, we provide an efficient privacy-preserving scheme for range query in smart grid. Our scheme achieves the range query without disclosing the privacy of the data and queries. And the performance shows that our scheme can reduce the computation cost for both the data owner and data users, and shorten the response time of every query, which is great significance for smart grid application.


Smart grid Privacy-preserving Range query 



This work was financially supported by National Natural Science Foundation of China with Grant No.61672195 and No. 61732022, National Key Research and Development Program of China with Grant No. 2016YFB0800804 and No. 2017YFB0803002, and Shenzhen Science and Technology Plan with Grant No. JCYJ20160318094336513 and No. JCYJ20160318094101317.


  1. 1.
    Wen, M., Lu, R., Zhang, K., Lei, J., Liang, X., Shen, X.: PaRQ: a privacy-preserving range query scheme over encrypted metering data for smart grid. IEEE Trans. Emerg. Top. Comput. 1(1), 178–191 (2013)CrossRefGoogle Scholar
  2. 2.
    Lu, R., Liang, X., Li, X., Lin, X., Shen, X.: 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
  3. 3.
    Liang, X., Li, X., Lu, R., Lin, X., Shen, X.: UDP: usage-based dynamic pricing with privacy preservation for smart grid. IEEE Trans. Smart Grid 4(1), 141–150 (2013)CrossRefGoogle Scholar
  4. 4.
    Boneh, D., Di Crescenzo, G., Ostrovsky, R., Persiano, G.: Public key encryption with keyword search. In: Cachin, C., Camenisch, J.L. (eds.) EUROCRYPT 2004. LNCS, vol. 3027, pp. 506–522. Springer, Heidelberg (2004). Scholar
  5. 5.
    Zhang, B., Zhang, F.: An efficient public key encryption with conjunctive-subset keywords search. J. Netw. Comput. Appl. 34(1), 262–267 (2011)CrossRefGoogle Scholar
  6. 6.
    Baek, J., Safavi-Naini, R., Susilo, W.: On the integration of public key data encryption and public key encryption with keyword search. In: Katsikas, S.K., López, J., Backes, M., Gritzalis, S., Preneel, B. (eds.) ISC 2006. LNCS, vol. 4176, pp. 217–232. Springer, Heidelberg (2006). Scholar
  7. 7.
    Liu, Q., Wang, G., Wu, J.: An efficient privacy preserving keyword search scheme in cloud computing. In: 2009 International Conference on Computational Science and Engineering, CSE 2009, vol. 2, pp. 715–720. IEEE (2009)Google Scholar
  8. 8.
    Wen, M., Lu, R., Lei, J., Li, H., Liang, X., Sherman Shen, X.: SESA: an efficient searchable encryption scheme for auction in emerging smart grid marketing. Secur. Commun. Netw. 7(1), 234–244 (2014)CrossRefGoogle Scholar
  9. 9.
    Agrawal, R., Kiernan, J., Srikant, R., Xu, Y.: Order preserving encryption for numeric data. In: Proceedings of the 2004 ACM SIGMOD International Conference on Management of Data, pp. 563–574. ACM (2004)Google Scholar
  10. 10.
    Boldyreva, A., Chenette, N., Lee, Y., O’Neill, A.: Order-preserving symmetric encryption. In: Joux, A. (ed.) EUROCRYPT 2009. LNCS, vol. 5479, pp. 224–241. Springer, Heidelberg (2009). Scholar
  11. 11.
    Boldyreva, A., Chenette, N., O’Neill, A.: Order-preserving encryption revisited: improved security analysis and alternative solutions. In: Rogaway, P. (ed.) CRYPTO 2011. LNCS, vol. 6841, pp. 578–595. Springer, Heidelberg (2011). Scholar
  12. 12.
    Boneh, D., Waters, B.: Conjunctive, Subset, and Range Queries on Encrypted Data. In: Vadhan, S.P. (ed.) TCC 2007. LNCS, vol. 4392, pp. 535–554. Springer, Heidelberg (2007). Scholar
  13. 13.
    Shi, E., Bethencourt, J., Chan, T.H.H., Song, D., Perrig, A.: Multi-dimensional range query over encrypted data. In: IEEE Symposium on Security and Privacy, 2007, SP 2007, pp. 350–364. IEEE (2007)Google Scholar
  14. 14.
    Wang, B., Hou, Y., Li, M., Wang, H., Li, H.: Maple: scalable multi-dimensional range search over encrypted cloud data with tree-based index. In: Proceedings of the 9th ACM Symposium on Information, Computer and Communications Security, pp. 111–122. ACM (2014)Google Scholar
  15. 15.
    Lu, Y.: Privacy-preserving logarithmic-time search on encrypted data in cloud. In: NDSS (2012)Google Scholar
  16. 16.
    Wong, W.K., Cheung, D.W., Kao, B., Mamoulis, N.: Secure knn computation on encrypted databases. In: Proceedings of the 2009 ACM SIGMOD International Conference on Management of Data, pp. 139–152. ACM (2009)Google Scholar
  17. 17.
    Wang, P., Ravishankar, C.V.: Secure and efficient range queries on outsourced databases using Rp-trees. In: 2013 IEEE 29th International Conference on Data Engineering (ICDE), pp. 314–325. IEEE (2013)Google Scholar
  18. 18.
    Chi, J., Hong, C., Zhang, M., Zhang, Z.: Privacy-enhancing range query processing over encrypted cloud databases. In: Wang, J., Cellary, W., Wang, D., Wang, H., Chen, S.-C., Li, T., Zhang, Y. (eds.) WISE 2015. LNCS, vol. 9419, pp. 63–77. Springer, Cham (2015). Scholar
  19. 19.
    Hacigümüş, H., Iyer, B., Li, C., Mehrotra, S.: Executing SQL over encrypted data in the database-service-provider model. In: Proceedings of the 2002 ACM SIGMOD International Conference on Management of Data, pp. 216–227. ACM (2002)Google Scholar
  20. 20.
    Hore, B., Mehrotra, S., Tsudik, G.: A privacy-preserving index for range queries. In: Thirtieth International Conference on Very Large Data Bases, pp. 720–731 (2004)Google Scholar
  21. 21.
    Hore, B., Mehrotra, S., Canim, M., Kantarcioglu, M.: Secure multidimensional range queries over outsourced data. VLDB J. 21(3), 333–358 (2012)CrossRefGoogle Scholar
  22. 22.
    Paillier, P.: Public-key cryptosystems based on composite degree residuosity classes. In: Stern, J. (ed.) EUROCRYPT 1999. LNCS, vol. 1592, pp. 223–238. Springer, Heidelberg (1999). Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Harbin Institute of Technology Shenzhen Graduate SchoolShenzhenChina
  2. 2.School of Electrical EngineeringAnhui Polytechnic UniversityWuhuChina

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