Abstract
With the rapid growth in the number of smart devices and explosive data generated every day by the mobile users, cloud computing comes to the bottleneck due to the far-off transmission and bandwidth limitation. Fog computing has been introduced as one of the promising solutions to meet the requirements under Internet of Things (IoT) scenarios such as location awareness and real-time services. The study of fog-based applications has become an attractive and important potential trend. The existing research about fog-based recommender systems focus on providing personalized and localized services to users while serving as a fog computing optimization tool in the system. However, there is little research about how to preserve user privacy in fog-based recommender systems. In this paper, we propose a novel privacy preserving aggregation scheme to handle the privacy issue for fog-based recommender systems.
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
Similar content being viewed by others
References
Alrawais, A., Alhothaily, A., Chunqiang, H., Cheng, X.: Fog computing for the internet of things: security and privacy issues. IEEE Internet Comput. 21(2), 34–42 (2017)
Bell, R.M., Koren, Y., Volinsky, C.: The bellkor solution to the netflix prize. KorBell Team’s Report to Netflix (2007)
Bonomi, F., Milito, R., Natarajan, P., Zhu, J.: Fog computing: a platform for internet of things and analytics. In: Big Data and Internet of Things: A Roadmap for Smart Environments, pp. 169–186 (2014)
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)
Chiang, M., Zhang, T.: Fog and IoT: an overview of research opportunities. IEEE Internet of Things J. 3(6), 854–864 (2016)
Dastjerdi, A.V., Gupta, H., Calheiros, R.N., Ghosh, S.K., Buyya, R.: Fog computing: principles, architectures, and applications. In: Internet of Things: Principles and Paradigms, pp. 61–75 (2016)
Dwork, C.: Differential privacy: a survey of results. In: Agrawal, M., Du, D., Duan, Z., Li, A. (eds.) TAMC 2008. LNCS, vol. 4978, pp. 1–19. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-79228-4_1
Dwork, C., Roth, A., et al.: The algorithmic foundations of differential privacy. Found. Trends Theor. Comput. Sci. 9(3–4), 211–407 (2014)
Global Mobile Data Traffic Forecast: Cisco visual networking index: global mobile data traffic forecast update, 2017–2022. Update 2017, 2022 (2019)
Gu, B.S., Gao, L., Wang, X., Qu, Y., Jin, J., Yu, S.: Privacy on the edge: customizable privacy-preserving context sharing in hierarchical edge computing. IEEE Trans. Netw. Sci. Eng. (2019)
Mouradian, C., Naboulsi, D., Yangui, S., Glitho, R.H., Morrow, M.J., Polakos, P.A.: A comprehensive survey on fog computing: state-of-the-art and research challenges. IEEE Commun. Surv. Tutor. 20(1), 416–464 (2018)
Mukherjee, M., et al.: Security and privacy in fog computing: challenges. IEEE Access 5, 19293–19304 (2017)
Naha, R.K., et al.: Fog computing: survey of trends, architectures, requirements, and research directions. IEEE Access 6, 47980–48009 (2018)
OpenFog Consortium Architecture Working Group. OpenFog Reference Architecture for Fog Computing. OpenFogConsortium, (February), 1–162 (2017)
Qu, Y., Nosouhi, M.R., Cui, L., Yu, S.: Privacy preservation in smart cities. In: Smart Cities Cybersecurity and Privacy, pp. 75–88 (2019)
Qu, Y., Pokhrel, S.R., Garg, S., Gao, L., Xiang, Y.: A blockchained federated learning framework for cognitive computing in industry 4.0 networks. IEEE Trans. Ind. Inf. (2020)
Qu, Y., Yu, S., Gao, L., Niu, J.: Big data set privacy preserving through sensitive attribute-based grouping. In: 2017 IEEE International Conference on Communications (ICC), pp. 1–6. IEEE (2017)
Qu, Y., Shui, Yu., Gao, L., Zhou, W., Peng, S.: A hybrid privacy protection scheme in cyber-physical social networks. IEEE Trans. Comput. Soc. Syst. 5(3), 773–784 (2018)
Qu, Y., Yu, S., Zhou, W., Tian, Y.: Gan-driven personalized spatial-temporal private data sharing in cyber-physical social systems. IEEE Trans. Netw. Sci. Eng. (2020)
The OpenFog Consortium. OpenFog Architecture Overview. OpenFogConsortium, (February), 1–35 (2016)
Wang, X., Gu, B., Qu, Y., Ren, Y., Xiang, Y., Gao, L.: Reliable customized privacy-preserving in fog computing. In: ICC 2020–2020 IEEE International Conference on Communications (ICC), pp. 1–6. IEEE (2020)
Wang, X., et al.: A fog-based recommender system. IEEE Internet of Things J. 7(2), 1048–1060 (2019)
Yousefpour, A., Ishigaki, G., Gour, R., Jue, J.P.: On reducing IoT service delay via fog offloading. IEEE Internet of Things J. 5(2), 998–1010 (2018)
Zeng, X., et al.: IOTSim: a simulator for analysing IoT applications. J. Syst. Architect. 72, 93–107 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Wang, X., Gu, B., Qu, Y., Ren, Y., Xiang, Y., Gao, L. (2020). A Privacy Preserving Aggregation Scheme for Fog-Based Recommender System. In: Kutyłowski, M., Zhang, J., Chen, C. (eds) Network and System Security. NSS 2020. Lecture Notes in Computer Science(), vol 12570. Springer, Cham. https://doi.org/10.1007/978-3-030-65745-1_24
Download citation
DOI: https://doi.org/10.1007/978-3-030-65745-1_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-65744-4
Online ISBN: 978-3-030-65745-1
eBook Packages: Computer ScienceComputer Science (R0)