A MapReduce based approach of scalable multidimensional anonymization for big data privacy preservation on cloud

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

A MapReduce based approach of scalable multidimensional anonymization for big data privacy preservation on cloud

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

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Publication Type:: Conference Proceeding
Citation:: Proceedings - 2013 IEEE 3rd International Conference on Cloud and Green Computing, CGC 2013 and 2013 IEEE 3rd International Conference on Social Computing and Its Applications, SCA 2013, 2013, pp. 105 - 112
Issue Date:: 2013-12-01

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Field	Value	Language
dc.contributor.author	Zhang, X	en_US
dc.contributor.author	Yang, C	en_US
dc.contributor.author	Nepal, S	en_US
dc.contributor.author	Liu, C	en_US
dc.contributor.author	Dou, W	en_US
dc.contributor.author	Chen, J	en_US
dc.date.issued	2013-12-01	en_US
dc.identifier.citation	Proceedings - 2013 IEEE 3rd International Conference on Cloud and Green Computing, CGC 2013 and 2013 IEEE 3rd International Conference on Social Computing and Its Applications, SCA 2013, 2013, pp. 105 - 112	en_US
dc.identifier.isbn	9780769551142	en_US
dc.identifier.uri	http://hdl.handle.net/10453/28364
dc.description.abstract	The massive increase in computing power and data storage capacity provisioned by cloud computing as well as advances in big data mining and analytics have expanded the scope of information available to businesses, government, and individuals by orders of magnitude. Meanwhile, privacy protection is one of most concerned issues in big data and cloud applications, thereby requiring strong preservation of customer privacy and attracting considerable attention from both IT industry and academia. Data anonymization provides an effective way for data privacy preservation, and multidimensional anonymization scheme is a widely-adopted one among existing anonymization schemes. However, existing multidimensional anonymization approaches suffer from severe scalability or IT cost issues when handling big data due to their incapability of fully leveraging cloud resources or being cost-effectively adapted to cloud environments. As such, we propose a scalable multidimensional anonymization approach for big data privacy preservation using MapReduce on cloud. In the approach, a highly scalable median-finding algorithm combining the idea of the median of medians and histogram technique is proposed and the recursion granularity is controlled to achieve cost-effectiveness. Corresponding MapReduce jobs are dedicatedly designed, and the experiment evaluations demonstrate that with our approach, the scalability and cost-effectiveness of multidimensional scheme can be improved significantly over existing approaches. © 2013 IEEE.	en_US
dc.relation.ispartof	Proceedings - 2013 IEEE 3rd International Conference on Cloud and Green Computing, CGC 2013 and 2013 IEEE 3rd International Conference on Social Computing and Its Applications, SCA 2013	en_US
dc.relation.isbasedon	10.1109/CGC.2013.24	en_US
dc.title	A MapReduce based approach of scalable multidimensional anonymization for big data privacy preservation on cloud	en_US
dc.type	Conference Proceeding
utslib.for	0805 Distributed Computing	en_US
dc.location.activity	Karlsruhe, Germany	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/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
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.publication-status	Published	en_US

Abstract:

The massive increase in computing power and data storage capacity provisioned by cloud computing as well as advances in big data mining and analytics have expanded the scope of information available to businesses, government, and individuals by orders of magnitude. Meanwhile, privacy protection is one of most concerned issues in big data and cloud applications, thereby requiring strong preservation of customer privacy and attracting considerable attention from both IT industry and academia. Data anonymization provides an effective way for data privacy preservation, and multidimensional anonymization scheme is a widely-adopted one among existing anonymization schemes. However, existing multidimensional anonymization approaches suffer from severe scalability or IT cost issues when handling big data due to their incapability of fully leveraging cloud resources or being cost-effectively adapted to cloud environments. As such, we propose a scalable multidimensional anonymization approach for big data privacy preservation using MapReduce on cloud. In the approach, a highly scalable median-finding algorithm combining the idea of the median of medians and histogram technique is proposed and the recursion granularity is controlled to achieve cost-effectiveness. Corresponding MapReduce jobs are dedicatedly designed, and the experiment evaluations demonstrate that with our approach, the scalability and cost-effectiveness of multidimensional scheme can be improved significantly over existing approaches. © 2013 IEEE.

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http://hdl.handle.net/10453/28364