Summary and Outlook

Qu, Y; Gao, L; Yu, S; Xiang, Y

Summary and Outlook

Qu, Y Gao, L Yu, S

Xiang, Y

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Publisher:: Springer Nature Singapore
Publication Type:: Chapter
Citation:: SpringerBriefs in Computer Science, 2022, pp. 117-119
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Qu, Y
dc.contributor.author	Gao, L
dc.contributor.author	Yu, S https://orcid.org/0000-0003-4485-6743
dc.contributor.author	Xiang, Y
dc.date.accessioned	2023-03-24T02:14:03Z
dc.date.available	2023-03-24T02:14:03Z
dc.date.issued	2022-01-01
dc.identifier.citation	SpringerBriefs in Computer Science, 2022, pp. 117-119
dc.identifier.isbn	9789811917967
dc.identifier.uri	http://hdl.handle.net/10453/168306
dc.description.abstract	In this monograph, we summarize the state-of-art research on machine learning driven privacy protection models in IoT scenarios. As far as we can see, machine learning driven privacy protection is still in its early stage. What have been present in this monograph, like models, theories, and potentially conceptual designs, could serve as a point of departure for follow-up readers, students, engineers, and researchers to investigate this emerging field. Our target is to provide a systematic summary of existing research and application outputs on machine learning driven privacy protection in IoTs. Based on this, we analyze the theoretical and practical applicability under big data settings. Subsequently, we offer the interested readers several future research directions, which we hope the following explorers find them insightful or inspiring from a certain angle.
dc.language	en
dc.publisher	Springer Nature Singapore
dc.relation.ispartof	SpringerBriefs in Computer Science
dc.relation.isbasedon	10.1007/978-981-19-1797-4_7
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Summary and Outlook
dc.type	Chapter
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2023-03-24T02:14:01Z
pubs.publication-status	Published

Abstract:

In this monograph, we summarize the state-of-art research on machine learning driven privacy protection models in IoT scenarios. As far as we can see, machine learning driven privacy protection is still in its early stage. What have been present in this monograph, like models, theories, and potentially conceptual designs, could serve as a point of departure for follow-up readers, students, engineers, and researchers to investigate this emerging field. Our target is to provide a systematic summary of existing research and application outputs on machine learning driven privacy protection in IoTs. Based on this, we analyze the theoretical and practical applicability under big data settings. Subsequently, we offer the interested readers several future research directions, which we hope the following explorers find them insightful or inspiring from a certain angle.

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http://hdl.handle.net/10453/168306