Low-Cost and Confidentiality-Preserving Data Acquisition for Internet of Multimedia Things

Zhang, Y; He, Q; Xiang, Y; Zhang, LY; Liu, B; Chen, J; Xie, Y

Low-Cost and Confidentiality-Preserving Data Acquisition for Internet of Multimedia Things

Zhang, Y He, Q Xiang, Y Zhang, LY Liu, B

Chen, J Xie, Y

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Internet of Things Journal, 2018, 5, (5), pp. 3442-3451
Issue Date:: 2018-10-01

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Field	Value	Language
dc.contributor.author	Zhang, Y
dc.contributor.author	He, Q
dc.contributor.author	Xiang, Y
dc.contributor.author	Zhang, LY
dc.contributor.author	Liu, B https://orcid.org/0000-0002-3603-6617
dc.contributor.author	Chen, J
dc.contributor.author	Xie, Y
dc.date.accessioned	2022-09-03T07:33:46Z
dc.date.available	2022-09-03T07:33:46Z
dc.date.issued	2018-10-01
dc.identifier.citation	IEEE Internet of Things Journal, 2018, 5, (5), pp. 3442-3451
dc.identifier.issn	2327-4662
dc.identifier.issn	2327-4662
dc.identifier.uri	http://hdl.handle.net/10453/161267
dc.description.abstract	Internet of Multimedia Things (IoMT) faces the challenge of how to realize low-cost data acquisition while still preserve data confidentiality. In this paper, we present a low-cost and confidentiality-preserving data acquisition framework for IoMT. First, we harness chaotic convolution and random subsampling to capture multiple image signals. The measurement matrix is under the control of chaos, ensuring the security of the sampling process. Next, we assemble these sampled images into a big master image, and then encrypt this master image based on Arnold transform and single value diffusion. The computation of these two transforms only requires some low-complexity operations. Finally, the encrypted image is delivered to cloud servers for storage and decryption service. Experimental results demonstrate the security and effectiveness of the proposed framework.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Internet of Things Journal
dc.relation.isbasedon	10.1109/JIOT.2017.2781737
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0805 Distributed Computing, 1005 Communications Technologies
dc.title	Low-Cost and Confidentiality-Preserving Data Acquisition for Internet of Multimedia Things
dc.type	Journal Article
utslib.citation.volume	5
utslib.for	0805 Distributed Computing
utslib.for	1005 Communications Technologies
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	2022-09-03T07:33:44Z
pubs.issue	5
pubs.publication-status	Published
pubs.volume	5
utslib.citation.issue	5

Abstract:

Internet of Multimedia Things (IoMT) faces the challenge of how to realize low-cost data acquisition while still preserve data confidentiality. In this paper, we present a low-cost and confidentiality-preserving data acquisition framework for IoMT. First, we harness chaotic convolution and random subsampling to capture multiple image signals. The measurement matrix is under the control of chaos, ensuring the security of the sampling process. Next, we assemble these sampled images into a big master image, and then encrypt this master image based on Arnold transform and single value diffusion. The computation of these two transforms only requires some low-complexity operations. Finally, the encrypted image is delivered to cloud servers for storage and decryption service. Experimental results demonstrate the security and effectiveness of the proposed framework.

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http://hdl.handle.net/10453/161267