Abstract
Edge computing migrates computing to the end user. It directly processes and makes decisions on the data locally. To a certain extent, similar to cloud computing, it avoids the long-distance transmission of data in the network and reduces the risk of privacy leakage. However, due to the users’ real-time data obtained by edge nodes, a large number of sensitive privacy data can be obtained by adversaries. The methodologies ensures the usage of the service without disclosing their sensitive location information have proposed higher requirements for privacy protection algorithms in edge computing.
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References
C. Mouradian, D. Naboulsi, S. Yangui, R.H. Glitho, M.J. Morrow, P.A. Polakos, A comprehensive survey on fog computing: state-of-the-art and research challenges. IEEE Commun. Surv. Tutor. 20(1), 416–464 (2018)
M. Yannuzzi, R. Milito, R. Serral-Graciá, D. Montero, M. Nemirovsky, Key ingredients in an iot recipe: fog computing, cloud computing, and more fog computing, in 2014 IEEE 19th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD) (2014), pp. 325–329
J. Ni, K. Zhang, X. Lin, X. Shen, Securing fog computing for internet of things applications: challenges and solutions. IEEE Commun. Surv. Tutor. 20(1), 601–628 (2018)
B. Gu, X. Wang, Y. Qu, J. Jin, Y. Xiang, L. Gao, Context-aware privacy preservation in a hierarchical fog computing system, in ICC 2019 - 2019 IEEE International Conference on Communications (ICC) (2019), pp. 1–6
J. Zhang, X. Feng, Z. Liu, A grid-based clustering algorithm via load analysis for industrial internet of things. IEEE Access PP, 1–1 (2018)
H. Kim, E.A. Lee, S. Dustdar, Creating a resilient IoT with edge computing. Computer 52(8), 43–53 (2019)
G. Potrino, F. De Rango, P. Fazio, A distributed mitigation strategy against DoS attacks in edge computing, in 2019 Wireless Telecommunications Symposium (WTS) (2019), pp. 1–7
Y. Xiao, Y. Jia, C. Liu, X. Cheng, J. Yu, W. Lv, Edge computing security: state of the art and challenges. Proc. IEEE 107(8), 1608–1631 (2019)
Deepali, K. Bhushan, DDoS attack defense framework for cloud using fog computing, in 2017 2nd IEEE International Conference on Recent Trends in Electronics, Information Communication Technology (RTEICT) (2017), pp. 534–538
B.J. Frey, D. Dueck, Clustering by passing messages between data points. Science 315(5814), 972–976 (2007)
VicFreeWiFi Access Point locations, Discover.data.vic.gov.au (2017). [Online]. Available: https://discover.data.vic.gov.au/dataset/2f2b954a-ee69-493e-8071-0754d01fd11f/ resource/1922597e-c989-4ebd-bec9-afcc284e5b2c/download/vicfreewifi20ap20map20data 2020170724.csv [Accessed: 20- Sept-2018]
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Gao, L., Luan, T.H., Gu, B., Qu, Y., Xiang, Y. (2021). Location-Aware Privacy Preserving in Edge Computing. In: Privacy-Preserving in Edge Computing. Wireless Networks. Springer, Singapore. https://doi.org/10.1007/978-981-16-2199-4_4
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DOI: https://doi.org/10.1007/978-981-16-2199-4_4
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