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.
Supported by National Key R&D Program of China (No. 2017YFB0802000), National Natural Science Foundation of China (No. 61772418, 61472472, 61402366), Natural Science Basic Research Plan in Shaanxi Province of China (No. 2015JQ6236). Yinghui Zhang is supported by New Star Team of Xi’an University of Posts and Telecommunications (No. 2016-02).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Armbrust, M., et al.: A view of cloud computing. Commun. ACM 53(4), 50–58 (2010)
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)
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_14
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)
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_2
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)
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)
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)
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)
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)
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)
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)
Mell, P.M., Grance, T.: SP 800–145. The NIST definition of cloud computing (2011)
Mukherjee, M., et al.: Security and privacy in fog computing: challenges. IEEE Access 5, 19293–19304 (2017)
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_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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhang, Y., Zhao, J., Zheng, D., Deng, K., Ren, F., Zheng, X. (2018). Privacy-Aware Data Collection and Aggregation in IoT Enabled Fog Computing. In: Vaidya, J., Li, J. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2018. Lecture Notes in Computer Science(), vol 11337. Springer, Cham. https://doi.org/10.1007/978-3-030-05063-4_44
Download citation
DOI: https://doi.org/10.1007/978-3-030-05063-4_44
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-05062-7
Online ISBN: 978-3-030-05063-4
eBook Packages: Computer ScienceComputer Science (R0)