A secure big data stream analytics framework for disaster management on the cloud

Puthal, D; Nepal, S; Ranjan, R; Chen, J

A secure big data stream analytics framework for disaster management on the cloud

Puthal, D

Nepal, S Ranjan, R Chen, J

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Publication Type:: Conference Proceeding
Citation:: Proceedings - 18th IEEE International Conference on High Performance Computing and Communications, 14th IEEE International Conference on Smart City and 2nd IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2016, 2017, pp. 1218 - 1225
Issue Date:: 2017-01-20

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Field	Value	Language
dc.contributor.author	Puthal, D https://orcid.org/0000-0002-8332-278X	en_US
dc.contributor.author	Nepal, S	en_US
dc.contributor.author	Ranjan, R	en_US
dc.contributor.author	Chen, J	en_US
dc.date.issued	2017-01-20	en_US
dc.identifier.citation	Proceedings - 18th IEEE International Conference on High Performance Computing and Communications, 14th IEEE International Conference on Smart City and 2nd IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2016, 2017, pp. 1218 - 1225	en_US
dc.identifier.isbn	9781509042968	en_US
dc.identifier.uri	http://hdl.handle.net/10453/114661
dc.description.abstract	© 2016 IEEE. Cloud computing and big data analysis are gaining lots of interest across a range of applications including disaster management. These two technologies together provide the capability of real-time data analysis not only to detect emergencies in disaster areas, but also to rescue the affected people. This paper presents a framework that supports emergency event detection and alert generation by analyzing the data stream, which includes efficient data collection, data aggregation and alert dissemination. One of the goals for such a framework is to support an end-to-end security architecture to protect the data stream from unauthorized manipulation as well as leakage of sensitive information. The proposed system provides support for both data security punctuation and query security punctuation. This paper presents the proposed architecture with a specific focus on data stream security. It also briefly describes the implementation of security aspects of the architecture.	en_US
dc.relation.ispartof	Proceedings - 18th IEEE International Conference on High Performance Computing and Communications, 14th IEEE International Conference on Smart City and 2nd IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2016	en_US
dc.relation.isbasedon	10.1109/HPCC-SmartCity-DSS.2016.0170	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	A secure big data stream analytics framework for disaster management on the cloud	en_US
dc.type	Conference Proceeding
utslib.for	0805 Distributed Computing	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 Engineering and Information Technology/School of Information, Systems and Modelling
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:

© 2016 IEEE. Cloud computing and big data analysis are gaining lots of interest across a range of applications including disaster management. These two technologies together provide the capability of real-time data analysis not only to detect emergencies in disaster areas, but also to rescue the affected people. This paper presents a framework that supports emergency event detection and alert generation by analyzing the data stream, which includes efficient data collection, data aggregation and alert dissemination. One of the goals for such a framework is to support an end-to-end security architecture to protect the data stream from unauthorized manipulation as well as leakage of sensitive information. The proposed system provides support for both data security punctuation and query security punctuation. This paper presents the proposed architecture with a specific focus on data stream security. It also briefly describes the implementation of security aspects of the architecture.

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http://hdl.handle.net/10453/114661