Advertisement

Privacy-Aware Data Collection and Aggregation in IoT Enabled Fog Computing

  • Yinghui Zhang
  • Jiangfan Zhao
  • Dong Zheng
  • Kaixin Deng
  • Fangyuan Ren
  • Xiaokun Zheng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11337)

Abstract

With the rapid development of the Internet of Things (IoT), a large number of IoT device data has flooded into cloud computing service centers, which has greatly increased the data processing task of cloud computing. To alleviate this situation, IoT enabled fog computing comes into being and it is necessary to aggregate the collected data of multiple IoT devices at the fog node. In this paper, we consider a privacy-aware data collection and aggregation scheme for fog computing. Although the fog node and the cloud control center are honest-but-curious, the proposed scheme also ensures that the data privacy will not be leaked. Our security and performance analysis indicates that the proposed scheme is secure and efficient in terms of computation and communication cost.

Keywords

Fog computing Data security Internet of Things Privacy Data aggregation 

References

  1. 1.
    Armbrust, M., et al.: A view of cloud computing. Commun. ACM 53(4), 50–58 (2010)CrossRefGoogle Scholar
  2. 2.
    Bonomi, F., Milito, R., Zhu, J., Addepalli, S.: 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. ACM (2012)Google Scholar
  3. 3.
    Han, Q., Zhang, Y., Chen, X., Li, H., Quan, J.: Efficient and robust identity-based handoff authentication in wireless networks. In: Xu, L., Bertino, E., Mu, Y. (eds.) NSS 2012. LNCS, vol. 7645, pp. 180–191. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-34601-9_14CrossRefGoogle Scholar
  4. 4.
    He, D., Kumar, N., Zeadally, S., Vinel, A., Yang, L.T.: Efficient and privacy-preserving data aggregation scheme for smart grid against internal adversaries. IEEE Trans. Smart Grid 8(5), 2411–2419 (2017)CrossRefGoogle Scholar
  5. 5.
    Hosseinian-Far, A., Ramachandran, M., Slack, C.L.: Emerging trends in cloud computing, big data, fog computing, IoT and smart living. In: Dastbaz, M., Arabnia, H., Akhgar, B. (eds.) Technology for Smart Futures, pp. 29–40. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-60137-3_2CrossRefGoogle Scholar
  6. 6.
    Huang, J.Y., Hong, W.C., Tsai, P.S., Liao, I.E.: A model for aggregation and filtering on encrypted XML streams in fog computing. Int. J. Distrib. Sens. Netw. 13(5), 1–14 (2017)CrossRefGoogle Scholar
  7. 7.
    Huang, Q., Yang, Y., Wang, L.: Secure data access control with ciphertext update and computation outsourcing in fog computing for internet of things. IEEE Access 5, 12941–12950 (2017)CrossRefGoogle Scholar
  8. 8.
    Jia, W., Zhu, H., Cao, Z., Dong, X., Xiao, C.: Human-factor-aware privacy-preserving aggregation in smart grid. IEEE Syst. J. 8(2), 598–607 (2014)CrossRefGoogle Scholar
  9. 9.
    Liu, Y., Zhang, Y., Ling, J., Liu, Z.: Secure and fine-grained access control on e-healthcare records in mobile cloud computing. Future Gener. Comput. Syst. 78, 1020–1026 (2018)CrossRefGoogle Scholar
  10. 10.
    Lu, R., Heung, K., Lashkari, A.H., Ghorbani, A.A.: A lightweight privacy-preserving data aggregation scheme for fog computing-enhanced IoT. IEEE Access 5, 3302–3312 (2017)CrossRefGoogle Scholar
  11. 11.
    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
  12. 12.
    Mahmoud, M., Saputro, N., Akula, P., Akkaya, K.: Privacy-preserving power injection over a hybrid AMI/LTE smart grid network. IEEE IoT J. 4(4), 870–880 (2016)Google Scholar
  13. 13.
    Mell, P.M., Grance, T.: SP 800–145. The NIST definition of cloud computing (2011)Google Scholar
  14. 14.
    Mukherjee, M., et al.: Security and privacy in fog computing: challenges. IEEE Access 5, 19293–19304 (2017)CrossRefGoogle Scholar
  15. 15.
    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).  https://doi.org/10.1007/3-540-48910-X_16CrossRefGoogle Scholar
  16. 16.
    Stergiou, C., Psannis, K.E., Kim, B.G., Gupta, B.: Secure integration of IoT and cloud computing. Future Gener. Comput. Syst. 78, 964–975 (2018)CrossRefGoogle Scholar
  17. 17.
    Stojmenovic, I.: Fog computing: a cloud to the ground support for smart things and machine-to-machine networks. In: 2014 Australasian Telecommunication Networks and Applications Conference (ATNAC), pp. 117–122 (2015)Google Scholar
  18. 18.
    Wang, H., Wang, Z., Domingo-Ferrer, J.: Anonymous and secure aggregation scheme in fog-based public cloud computing. Future Gener. Comput. Syst. 78, 712–719 (2018)CrossRefGoogle Scholar
  19. 19.
    Xhafa, F., Feng, J., Zhang, Y., Chen, X., Li, J.: Privacy-aware attribute-based PHR sharing with user accountability in cloud computing. J. Supercomput. 71(5), 1607–1619 (2015)CrossRefGoogle Scholar
  20. 20.
    Yannuzzi, M., Milito, R., Serral-Gracia, R., Montero, D., Nemirovsky, M.: Key ingredients in an IoT recipe: fog computing, cloud computing, and more fog computing. In: IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, pp. 325–329 (2015)Google Scholar
  21. 21.
    Zhang, Y., Zheng, D., Deng, R.H.: Security and privacy in smart health: efficient policy-hiding attribute-based access control. IEEE IoT J. 5(3), 2130–2145 (2018)Google Scholar
  22. 22.
    Zhang, Y.H., Chen, X.F., Li, H., Cao, J.: Identity-based construction for secure and efficient handoff authentication schemes in wireless networks. Secur. Commun. Netw. 5(10), 1121–1130 (2012)CrossRefGoogle Scholar
  23. 23.
    Zhang, Y., Chen, X., Li, J., Li, H.: Generic construction for secure and efficient handoff authentication schemes in EAP-based wireless networks. Comput. Netw. 75, 192–211 (2014)CrossRefGoogle Scholar
  24. 24.
    Zhang, Y., Chen, X., Li, J., Li, H., Li, F.: FDR-ABE: attribute-based encryption with flexible and direct revocation. In: 2013 5th International Conference on Intelligent Networking and Collaborative Systems (INCoS), pp. 38–45. IEEE (2013)Google Scholar
  25. 25.
    Zhang, Y., Chen, X., Li, J., Wong, D.S., Li, H.: Anonymous attribute-based encryption supporting efficient decryption test. In: Proceedings of the 8th ACM SIGSAC Symposium on Information, Computer and Communications Security ASIA CCS 2013, pp. 511–516. ACM, New York (2013)Google Scholar
  26. 26.
    Zhang, Y., Li, J., Chen, X., Li, H.: Anonymous attribute-based proxy re-encryption for access control in cloud computing. Secur. Commun. Netw. 9(14), 2397–2411 (2016)CrossRefGoogle Scholar
  27. 27.
    Zhang, Y., Zhao, J., Zheng, D.: Efficient and privacy-aware power injection over AMI and smart grid slice in future 5G networks. Mobile Inf. Syst. 2017, 1–11 (2017)Google Scholar
  28. 28.
    Zhang, Y., Zheng, D., Chen, X., Li, J., Li, H.: Efficient attribute-based data sharing in mobile clouds. Pervasive Mobile Comput. 28, 135–149 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Yinghui Zhang
    • 1
    • 2
  • Jiangfan Zhao
    • 1
  • Dong Zheng
    • 1
    • 2
  • Kaixin Deng
    • 1
  • Fangyuan Ren
    • 1
  • Xiaokun Zheng
    • 3
  1. 1.National Engineering Laboratory for Wireless SecurityXi’an University of Posts and TelecommunicationsXi’anPeople’s Republic of China
  2. 2.Westone Cryptologic Research CenterBeijingChina
  3. 3.School of Computer Science and TechnologyXi’an University of Posts and TelecommunicationsXi’anPeople’s Republic of China

Personalised recommendations