An upper-bound control approach for cost-effective privacy protection of intermediate dataset storage in cloud

Zhang, X; Liu, C; Chen, J; Dou, W

An upper-bound control approach for cost-effective privacy protection of intermediate dataset storage in cloud

Zhang, X Liu, C Chen, J Dou, W

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Publication Type:: Conference Proceeding
Citation:: Proceedings - IEEE 9th International Conference on Dependable, Autonomic and Secure Computing, DASC 2011, 2011, pp. 518 - 525
Issue Date:: 2011-12-01

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Field	Value	Language
dc.contributor.author	Zhang, X	en_US
dc.contributor.author	Liu, C	en_US
dc.contributor.author	Chen, J	en_US
dc.contributor.author	Dou, W	en_US
dc.date.issued	2011-12-01	en_US
dc.identifier.citation	Proceedings - IEEE 9th International Conference on Dependable, Autonomic and Secure Computing, DASC 2011, 2011, pp. 518 - 525	en_US
dc.identifier.isbn	9780769546124	en_US
dc.identifier.uri	http://hdl.handle.net/10453/26315
dc.description.abstract	Along with more and more data intensive applications have been migrated into cloud environments, storing some valuable intermediate datasets has been accommodated in order to avoid the high cost of re-computing them. However, this poses a risk on data privacy protection because malicious parties may deduce the private information of the parent dataset or original dataset by analyzing some of those stored intermediate datasets. The traditional way for addressing this issue is to encrypt all of those stored datasets so that they can be hidden. We argue that this is neither efficient nor cost-effective because it is not necessary to encrypt ALL of those datasets and encryption of all large amounts of datasets can be very costly. In this paper, we propose a new approach to identify which stored datasets need to be encrypted and which not. Through intensive analysis of information theory, our approach designs an upper bound on privacy measure. As long as the overall mixed information amount of some stored datasets is no more than that upper bound, those datasets do not need to be encrypted while privacy can still be protected. A tree model is leveraged to analyze privacy disclosure of datasets, and privacy requirements are decomposed and satisfied layer by layer. With a heuristic implementation of this approach, evaluation results demonstrate that the cost for encrypting intermediate datasets decreases significantly compared with the traditional approach while the privacy protection of parent or original dataset is guaranteed. © 2011 IEEE.	en_US
dc.relation.ispartof	Proceedings - IEEE 9th International Conference on Dependable, Autonomic and Secure Computing, DASC 2011	en_US
dc.relation.isbasedon	10.1109/DASC.2011.98	en_US
dc.title	An upper-bound control approach for cost-effective privacy protection of intermediate dataset storage in cloud	en_US
dc.type	Conference Proceeding
utslib.for	0805 Distributed Computing	en_US
dc.location.activity	Sydney, Australia	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
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Along with more and more data intensive applications have been migrated into cloud environments, storing some valuable intermediate datasets has been accommodated in order to avoid the high cost of re-computing them. However, this poses a risk on data privacy protection because malicious parties may deduce the private information of the parent dataset or original dataset by analyzing some of those stored intermediate datasets. The traditional way for addressing this issue is to encrypt all of those stored datasets so that they can be hidden. We argue that this is neither efficient nor cost-effective because it is not necessary to encrypt ALL of those datasets and encryption of all large amounts of datasets can be very costly. In this paper, we propose a new approach to identify which stored datasets need to be encrypted and which not. Through intensive analysis of information theory, our approach designs an upper bound on privacy measure. As long as the overall mixed information amount of some stored datasets is no more than that upper bound, those datasets do not need to be encrypted while privacy can still be protected. A tree model is leveraged to analyze privacy disclosure of datasets, and privacy requirements are decomposed and satisfied layer by layer. With a heuristic implementation of this approach, evaluation results demonstrate that the cost for encrypting intermediate datasets decreases significantly compared with the traditional approach while the privacy protection of parent or original dataset is guaranteed. © 2011 IEEE.

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http://hdl.handle.net/10453/26315