SaC-FRAPP: A scalable and cost-effective framework for privacy preservation over big data on cloud

Zhang, X; Liu, C; Nepal, S; Yang, C; Dou, W; Chen, J

SaC-FRAPP: A scalable and cost-effective framework for privacy preservation over big data on cloud

Zhang, X Liu, C Nepal, S Yang, C Dou, W Chen, J

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Publication Type:: Journal Article
Citation:: Concurrency Computation Practice and Experience, 2013, 25 (18), pp. 2561 - 2576
Issue Date:: 2013-12-25

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Field	Value	Language
dc.contributor.author	Zhang, X	en_US
dc.contributor.author	Liu, C	en_US
dc.contributor.author	Nepal, S	en_US
dc.contributor.author	Yang, C	en_US
dc.contributor.author	Dou, W	en_US
dc.contributor.author	Chen, J	en_US
dc.date.issued	2013-12-25	en_US
dc.identifier.citation	Concurrency Computation Practice and Experience, 2013, 25 (18), pp. 2561 - 2576	en_US
dc.identifier.issn	1532-0626	en_US
dc.identifier.uri	http://hdl.handle.net/10453/26572
dc.description.abstract	Big data and cloud computing are two disruptive trends nowadays, provisioning numerous opportunities to the current information technology industry and research communities while posing significant challenges on them as well. Cloud computing provides powerful and economical infrastructural resources for cloud users to handle ever increasing data sets in big data applications. However, processing or sharing privacy-sensitive data sets on cloud probably engenders severe privacy concerns because of multi-tenancy. Data encryption and anonymization are two widely-adopted ways to combat privacy breach. However, encryption is not suitable for data that are processed and shared frequently, and anonymizing big data and manage numerous anonymized data sets are still challenges for traditional anonymization approaches. As such, we propose a scalable and cost-effective framework for privacy preservation over big data on cloud in this paper. The key idea of the framework is that it leverages cloud-based MapReduce to conduct data anonymization and manage anonymous data sets, before releasing data to others. The framework provides a holistic conceptual foundation for privacy preservation over big data. Further, a corresponding proof-of-concept prototype system is implemented. Empirical evaluations demonstrate that scalable and cost-effective framework for privacy preservation can anonymize large-scale data sets and mange anonymous data sets in a highly flexible, scalable, efficient, and cost-effective fashion. Copyright © 2013 John Wiley & Sons, Ltd.	en_US
dc.relation.ispartof	Concurrency Computation Practice and Experience	en_US
dc.relation.isbasedon	10.1002/cpe.3083	en_US
dc.subject.classification	Distributed Computing	en_US
dc.title	SaC-FRAPP: A scalable and cost-effective framework for privacy preservation over big data on cloud	en_US
dc.type	Journal Article
utslib.citation.volume	18	en_US
utslib.citation.volume	25	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0803 Computer Software	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
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access
pubs.issue	18	en_US
pubs.publication-status	Published	en_US
pubs.volume	25	en_US

Abstract:

Big data and cloud computing are two disruptive trends nowadays, provisioning numerous opportunities to the current information technology industry and research communities while posing significant challenges on them as well. Cloud computing provides powerful and economical infrastructural resources for cloud users to handle ever increasing data sets in big data applications. However, processing or sharing privacy-sensitive data sets on cloud probably engenders severe privacy concerns because of multi-tenancy. Data encryption and anonymization are two widely-adopted ways to combat privacy breach. However, encryption is not suitable for data that are processed and shared frequently, and anonymizing big data and manage numerous anonymized data sets are still challenges for traditional anonymization approaches. As such, we propose a scalable and cost-effective framework for privacy preservation over big data on cloud in this paper. The key idea of the framework is that it leverages cloud-based MapReduce to conduct data anonymization and manage anonymous data sets, before releasing data to others. The framework provides a holistic conceptual foundation for privacy preservation over big data. Further, a corresponding proof-of-concept prototype system is implemented. Empirical evaluations demonstrate that scalable and cost-effective framework for privacy preservation can anonymize large-scale data sets and mange anonymous data sets in a highly flexible, scalable, efficient, and cost-effective fashion. Copyright © 2013 John Wiley & Sons, Ltd.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/26572