A hybrid approach for scalable sub-tree anonymization over big data using MapReduce on cloud

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

A hybrid approach for scalable sub-tree anonymization over big data using MapReduce on cloud

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

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Publication Type:: Journal Article
Citation:: Journal of Computer and System Sciences, 2014, 80 (5), pp. 1008 - 1020
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Zhang, X	en_US
dc.contributor.author	Liu, C	en_US
dc.contributor.author	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	2014-01-01	en_US
dc.identifier.citation	Journal of Computer and System Sciences, 2014, 80 (5), pp. 1008 - 1020	en_US
dc.identifier.issn	0022-0000	en_US
dc.identifier.uri	http://hdl.handle.net/10453/34943
dc.description.abstract	In big data applications, data privacy is one of the most concerned issues because processing large-scale privacy-sensitive data sets often requires computation resources provisioned by public cloud services. Sub-tree data anonymization is a widely adopted scheme to anonymize data sets for privacy preservation. Top-Down Specialization (TDS) and Bottom-Up Generalization (BUG) are two ways to fulfill sub-tree anonymization. However, existing approaches for sub-tree anonymization fall short of parallelization capability, thereby lacking scalability in handling big data in cloud. Still, either TDS or BUG individually suffers from poor performance for certain valuing of k-anonymity parameter. In this paper, we propose a hybrid approach that combines TDS and BUG together for efficient sub-tree anonymization over big data. Further, we design MapReduce algorithms for the two components (TDS and BUG) to gain high scalability. Experiment evaluation demonstrates that the hybrid approach significantly improves the scalability and efficiency of sub-tree anonymization scheme over existing approaches. © 2014 Elsevier Inc.	en_US
dc.relation.ispartof	Journal of Computer and System Sciences	en_US
dc.relation.isbasedon	10.1016/j.jcss.2014.02.007	en_US
dc.subject.classification	Computation Theory & Mathematics	en_US
dc.title	A hybrid approach for scalable sub-tree anonymization over big data using MapReduce on cloud	en_US
dc.type	Journal Article
utslib.citation.volume	5	en_US
utslib.citation.volume	80	en_US
utslib.for	0802 Computation Theory and Mathematics	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	0806 Information Systems	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	5	en_US
pubs.publication-status	Published	en_US
pubs.volume	80	en_US

Abstract:

In big data applications, data privacy is one of the most concerned issues because processing large-scale privacy-sensitive data sets often requires computation resources provisioned by public cloud services. Sub-tree data anonymization is a widely adopted scheme to anonymize data sets for privacy preservation. Top-Down Specialization (TDS) and Bottom-Up Generalization (BUG) are two ways to fulfill sub-tree anonymization. However, existing approaches for sub-tree anonymization fall short of parallelization capability, thereby lacking scalability in handling big data in cloud. Still, either TDS or BUG individually suffers from poor performance for certain valuing of k-anonymity parameter. In this paper, we propose a hybrid approach that combines TDS and BUG together for efficient sub-tree anonymization over big data. Further, we design MapReduce algorithms for the two components (TDS and BUG) to gain high scalability. Experiment evaluation demonstrates that the hybrid approach significantly improves the scalability and efficiency of sub-tree anonymization scheme over existing approaches. © 2014 Elsevier Inc.

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http://hdl.handle.net/10453/34943