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dc.contributor.authorWu, Yulei
dc.contributor.authorMa, Yuxiang
dc.contributor.authorDai, Hong-Ning
dc.contributor.authorWang, Hao
dc.description.abstract5G heterogeneous networks have become a promising platform to connect a growing number of Internet-of-Things (IoT) devices and accommodate a wide variety of vertical services. IoT has not been limited to traditional sensing systems since the introduction of 5G, but also includes a range of autonomous moving platforms, e.g., autonomous flying vehicles, autonomous underwater vehicles, autonomous surface vehicles as well as autonomous land vehicles. These platforms can be used as an effective means to connect air, space, ground, and sea mobile networks for providing a wider diversity of Internet services. Deep learning has been widely used to extract useful information from network big data for enhancing network quality-of-service and user quality-of-experience. Privacy preservation for user and network data is a burning concern in 5G heterogeneous networks due to various attacks in this environment. In this paper, we conduct an in-depth investigation on how deep learning can cope with privacy preservation issues in 5G heterogeneous networks, in terms of heterogeneous radio access networks (RANs), beyond-RAN networks, and end-to-end network slices, followed by a set of key research challenges and open issues that aim to guide future research.en_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internasjonal*
dc.titleDeep learning for privacy preservation in autonomous moving platforms enhanced 5G heterogeneous networksen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.source.journalComputer Networksen_US
dc.description.localcode© 2020. This is the authors’ accepted and refereed manuscript to the article. Locked until 15/12-2022 due to copyright restrictions. This manuscript version is made available under the CC-BY-NC-ND 4.0 license

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Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal