Data integrity verification of the outsourced big data in the cloud environment: A survey

Zhou, L; Fu, A; Yu, S; Su, M; Kuang, B

Data integrity verification of the outsourced big data in the cloud environment: A survey

Zhou, L Fu, A Yu, S

Su, M Kuang, B

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Publication Type:: Journal Article
Citation:: Journal of Network and Computer Applications, 2018, 122 pp. 1 - 15
Issue Date:: 2018-11-15

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Field	Value	Language
dc.contributor.author	Zhou, L	en_US
dc.contributor.author	Fu, A	en_US
dc.contributor.author	Yu, S https://orcid.org/0000-0003-4485-6743	en_US
dc.contributor.author	Su, M	en_US
dc.contributor.author	Kuang, B	en_US
dc.date.issued	2018-11-15	en_US
dc.identifier.citation	Journal of Network and Computer Applications, 2018, 122 pp. 1 - 15	en_US
dc.identifier.issn	1084-8045	en_US
dc.identifier.uri	http://hdl.handle.net/10453/130084
dc.description.abstract	© 2018 Elsevier Ltd With the explosive growth of data and the rapid development of science technology, big data analysis has attracted increasing attention. Due to the restrictive performance of traditional devices, cloud computing emerges as a convenient storage and computing platform for big data analysis. Driven by benefits, cloud servers may intentionally delete or modify outsourced big data. Therefore, users need to make sure that the servers correctly store the outsourced big data prior to deploying the cloud computing applications in practice. To resolve the issue, many researchers have concentrated on enabling users to check the completeness of data with data integrity verification (DIV) technique. We have therefore collated a summary of the existing literature, aiming to present a solid and stimulating review of current academic achievements for interested readers. Firstly, we present a fundamental introduction by defining seven major topics in order to offer a summary of the existing research domain for DIV study. Secondly, we classify the state-of-the-art DIV solutions into four categories, and then we parse each category based on dynamics, providing a clear and hierarchical classification of forthcoming DIV efforts. Thirdly, we discuss the principal topics and technical means utilized to equip DIV schemes with different requirements. Finally, we discuss the issues and challenges anticipated in future work, thus suggesting possible directions for follow-up research.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP180102828
dc.relation.ispartof	Journal of Network and Computer Applications	en_US
dc.relation.isbasedon	10.1016/j.jnca.2018.08.003	en_US
dc.subject.classification	Networking & Telecommunications	en_US
dc.title	Data integrity verification of the outsourced big data in the cloud environment: A survey	en_US
dc.type	Journal Article
utslib.citation.volume	122	en_US
utslib.for	0899 Other Information and Computing Sciences	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	1005 Communications Technologies	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	122	en_US

Abstract:

© 2018 Elsevier Ltd With the explosive growth of data and the rapid development of science technology, big data analysis has attracted increasing attention. Due to the restrictive performance of traditional devices, cloud computing emerges as a convenient storage and computing platform for big data analysis. Driven by benefits, cloud servers may intentionally delete or modify outsourced big data. Therefore, users need to make sure that the servers correctly store the outsourced big data prior to deploying the cloud computing applications in practice. To resolve the issue, many researchers have concentrated on enabling users to check the completeness of data with data integrity verification (DIV) technique. We have therefore collated a summary of the existing literature, aiming to present a solid and stimulating review of current academic achievements for interested readers. Firstly, we present a fundamental introduction by defining seven major topics in order to offer a summary of the existing research domain for DIV study. Secondly, we classify the state-of-the-art DIV solutions into four categories, and then we parse each category based on dynamics, providing a clear and hierarchical classification of forthcoming DIV efforts. Thirdly, we discuss the principal topics and technical means utilized to equip DIV schemes with different requirements. Finally, we discuss the issues and challenges anticipated in future work, thus suggesting possible directions for follow-up research.

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http://hdl.handle.net/10453/130084