Combining top-down and bottom-up: Scalable sub-tree anonymization over big data using mapreduce on cloud

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

Combining top-down and bottom-up: 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:: Conference Proceeding
Citation:: Proceedings - 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2013, 2013, pp. 501 - 508
Issue Date:: 2013-12-01

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Field	Value	Language
dc.contributor.author	Zhang, X	en_US
dc.contributor.author	Liu, C	en_US
dc.contributor.author	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	2013-12-01	en_US
dc.identifier.citation	Proceedings - 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2013, 2013, pp. 501 - 508	en_US
dc.identifier.isbn	9780769550220	en_US
dc.identifier.uri	http://hdl.handle.net/10453/28367
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 power provided by public cloud services. Sub-tree data anonymization, achieving a good trade-off between data utility and distortion, 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 on cloud. Still, both TDS and BUG suffer from poor performance for certain value of k-anonymity parameter if they are utilized individually. 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 based algorithms for two components (TDS and BUG) to gain high scalability by exploiting powerful computation capability of cloud. Experiment evaluations demonstrate that the hybrid approach significantly improves the scalability and efficiency of sub-tree anonymization scheme over existing approaches. © 2013 IEEE.	en_US
dc.relation.ispartof	Proceedings - 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2013	en_US
dc.relation.isbasedon	10.1109/TrustCom.2013.235	en_US
dc.title	Combining top-down and bottom-up: Scalable sub-tree anonymization over big data using mapreduce on cloud	en_US
dc.type	Conference Proceeding
utslib.for	0805 Distributed Computing	en_US
dc.location.activity	Melbourne, 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/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:

In big data applications, data privacy is one of the most concerned issues because processing large-scale privacy-sensitive data sets often requires computation power provided by public cloud services. Sub-tree data anonymization, achieving a good trade-off between data utility and distortion, 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 on cloud. Still, both TDS and BUG suffer from poor performance for certain value of k-anonymity parameter if they are utilized individually. 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 based algorithms for two components (TDS and BUG) to gain high scalability by exploiting powerful computation capability of cloud. Experiment evaluations demonstrate that the hybrid approach significantly improves the scalability and efficiency of sub-tree anonymization scheme over existing approaches. © 2013 IEEE.

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http://hdl.handle.net/10453/28367