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dc.contributor.authorWu, Y
dc.contributor.authorMa, Y
dc.contributor.authorDai, H-N
dc.contributor.authorWang, H
dc.date.accessioned2021-01-14T09:47:42Z
dc.date.issued2020-12-15
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_GB
dc.description.sponsorshipEngineering and Physical Sciences Research Council (EPSRC)en_GB
dc.identifier.citationVol. 185, article 107743en_GB
dc.identifier.doi10.1016/j.comnet.2020.107743
dc.identifier.grantnumberEP/R030863/1en_GB
dc.identifier.urihttp://hdl.handle.net/10871/124391
dc.language.isoenen_GB
dc.publisherElsevieren_GB
dc.rights.embargoreasonUnder embargo until 15 December 2021 in compliance with publisher policyen_GB
dc.rights© 2020. This version is made available under the CC-BY-NC-ND 4.0 license: https://creativecommons.org/licenses/by-nc-nd/4.0/  en_GB
dc.subjectDeep learningen_GB
dc.subject5Gen_GB
dc.subjectHeterogeneous networksen_GB
dc.subjectPrivacy preservationen_GB
dc.subjectNetwork slicingen_GB
dc.titleDeep learning for privacy preservation in autonomous moving platforms enhanced 5G heterogeneous networksen_GB
dc.typeArticleen_GB
dc.date.available2021-01-14T09:47:42Z
dc.identifier.issn1389-1286
exeter.article-number107743en_GB
dc.descriptionThis is the author accepted manuscript. The final version is available from Elsevier via the DOI in this recorden_GB
dc.identifier.journalComputer Networksen_GB
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/  en_GB
dcterms.dateAccepted2020-12-12
exeter.funder::Engineering and Physical Sciences Research Council (EPSRC)en_GB
rioxxterms.versionAMen_GB
rioxxterms.licenseref.startdate2020-12-12
rioxxterms.typeJournal Article/Reviewen_GB
refterms.dateFCD2021-01-14T09:45:23Z
refterms.versionFCDAM
refterms.panelBen_GB


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© 2020. This version is made available under the CC-BY-NC-ND 4.0 license: https://creativecommons.org/licenses/by-nc-nd/4.0/  
Except where otherwise noted, this item's licence is described as © 2020. This version is made available under the CC-BY-NC-ND 4.0 license: https://creativecommons.org/licenses/by-nc-nd/4.0/