Toggle Main Menu Toggle Search

Open Access padlockePrints

A Secure Big Data Stream Analytics Framework for Disaster Management on the Cloud

Lookup NU author(s): Dr Deepak PuthalORCiD, Professor Raj Ranjan


Full text for this publication is not currently held within this repository. Alternative links are provided below where available.


© 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.

Publication metadata

Author(s): Puthal D, Nepal S, Ranjan R, Chen J

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: 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

Year of Conference: 2017

Pages: 1218-1225

Online publication date: 26/01/2017

Acceptance date: 02/04/2016

Publisher: Institute of Electrical and Electronics Engineers Inc.


DOI: 10.1109/HPCC-SmartCity-DSS.2016.0170

Library holdings: Search Newcastle University Library for this item

ISBN: 9781509042968