GAN-Based Differential Private Image Privacy Protection Framework for the Internet of Multimedia Things

Yu, J; Xue, H; Liu, B; Wang, Y; Zhu, S; Ding, M

GAN-Based Differential Private Image Privacy Protection Framework for the Internet of Multimedia Things

Yu, J Xue, H Liu, B

Wang, Y Zhu, S Ding, M

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Publisher:: MDPI AG
Publication Type:: Journal Article
Citation:: Sensors, 21, (1), pp. 58-58

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Field	Value	Language
dc.contributor.author	Yu, J
dc.contributor.author	Xue, H
dc.contributor.author	Liu, B https://orcid.org/0000-0002-3603-6617
dc.contributor.author	Wang, Y
dc.contributor.author	Zhu, S
dc.contributor.author	Ding, M
dc.date.accessioned	2021-01-29T10:13:09Z
dc.date.available	2021-01-29T10:13:09Z
dc.identifier.citation	Sensors, 21, (1), pp. 58-58
dc.identifier.issn	1424-8220
dc.identifier.uri	http://hdl.handle.net/10453/145667
dc.description.abstract	<jats:p>With the development of the Internet of Multimedia Things (IoMT), an increasing amount of image data is collected by various multimedia devices, such as smartphones, cameras, and drones. This massive number of images are widely used in each field of IoMT, which presents substantial challenges for privacy preservation. In this paper, we propose a new image privacy protection framework in an effort to protect the sensitive personal information contained in images collected by IoMT devices. We aim to use deep neural network techniques to identify the privacy-sensitive content in images, and then protect it with the synthetic content generated by generative adversarial networks (GANs) with differential privacy (DP). Our experiment results show that the proposed framework can effectively protect users’ privacy while maintaining image utility.</jats:p>
dc.language	en
dc.publisher	MDPI AG
dc.relation.ispartof	Sensors
dc.relation.isbasedon	10.3390/s21010058
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0301 Analytical Chemistry, 0805 Distributed Computing, 0906 Electrical and Electronic Engineering, 0502 Environmental Science and Management, 0602 Ecology
dc.subject.classification	Analytical Chemistry
dc.title	GAN-Based Differential Private Image Privacy Protection Framework for the Internet of Multimedia Things
dc.type	Journal Article
utslib.citation.volume	21
utslib.for	0301 Analytical Chemistry
utslib.for	0805 Distributed Computing
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	0502 Environmental Science and Management
utslib.for	0602 Ecology
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
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	open_access	*
dc.date.updated	2021-01-29T10:13:04Z
pubs.issue	1
pubs.publication-status	Published online
pubs.volume	21
utslib.citation.issue	1

Abstract:

With the development of the Internet of Multimedia Things (IoMT), an increasing amount of image data is collected by various multimedia devices, such as smartphones, cameras, and drones. This massive number of images are widely used in each field of IoMT, which presents substantial challenges for privacy preservation. In this paper, we propose a new image privacy protection framework in an effort to protect the sensitive personal information contained in images collected by IoMT devices. We aim to use deep neural network techniques to identify the privacy-sensitive content in images, and then protect it with the synthetic content generated by generative adversarial networks (GANs) with differential privacy (DP). Our experiment results show that the proposed framework can effectively protect users’ privacy while maintaining image utility.

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http://hdl.handle.net/10453/145667