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IoT-SDNPP: A Method for Privacy-Preserving in Smart City with Software Defined Networking

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11337))

Abstract

Internet of Things (IoT) era appeared to connect all the digital and non-digital devices around the globe through the Internet. Based on predictions, billions of devices will be connected with each other by 2050 with the aim of providing high-level and humanized services. One application of IoT is a smart city that means IT-enabled cities running by themselves without human interventions. These large number of devices, especially in a smart city environment, may sense sensitive and personal data which makes the system vulnerable. We have to protect private information so that unwanted parties would not be able to find original data, which is a part of privacy-preserving. Meanwhile, a new networking paradigm evolved called Software Defined Networking (SDN) that aimed to separate the Control Plane and the Data Plane of the network results in much more flexibility to manage the network. Most of the existing works are deficient in flexibility or very tedious. In this paper, we facilitated IoT-based smart city with SDN paradigm to leverage the benefits of SDN. Then, based on the environment, we propose IoT-SDN Privacy-Preserving, IoT-SDNPP, to keep private data safe. We have done extensive experiments, and the experimental results have demonstrated the effectiveness of our approach.

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Acknowledgment

This work is supported in part by the National Natural Science Foundation of China under Grants 61632009 and 61472451, in part by the Guangdong Provincial Natural Science Foundation under Grant 2017A030308006, and High-Level Talents Program of Higher Education in Guangdong Province under Grant 2016ZJ01.

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Correspondence to Guojun Wang .

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Gheisari, M., Wang, G., Chen, S., Ghorbani, H. (2018). IoT-SDNPP: A Method for Privacy-Preserving in Smart City with Software Defined Networking. 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_24

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  • DOI: https://doi.org/10.1007/978-3-030-05063-4_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05062-7

  • Online ISBN: 978-3-030-05063-4

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