Towards scalable, fine-grained, intrusion-tolerant data protection models for healthcare cloud

Chen, L; Hoang, DB

Towards scalable, fine-grained, intrusion-tolerant data protection models for healthcare cloud

Chen, L Hoang, DB

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Publication Type:: Conference Proceeding
Citation:: Proc. 10th IEEE Int. Conf. on Trust, Security and Privacy in Computing and Communications, TrustCom 2011, 8th IEEE Int. Conf. on Embedded Software and Systems, ICESS 2011, 6th Int. Conf. on FCST 2011, 2011, pp. 126 - 133
Issue Date:: 2011-12-01

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Field	Value	Language
dc.contributor.author	Chen, L	en_US
dc.contributor.author	Hoang, DB https://orcid.org/0000-0003-1798-4926	en_US
dc.date.issued	2011-12-01	en_US
dc.identifier.citation	Proc. 10th IEEE Int. Conf. on Trust, Security and Privacy in Computing and Communications, TrustCom 2011, 8th IEEE Int. Conf. on Embedded Software and Systems, ICESS 2011, 6th Int. Conf. on FCST 2011, 2011, pp. 126 - 133	en_US
dc.identifier.isbn	9780769546001	en_US
dc.identifier.uri	http://hdl.handle.net/10453/19089
dc.description.abstract	Despite cloud computing has been widely adopted by most industries, the healthcare industry still reveals a slow development in cloud-based solution due to the raising of user fear that their confidential health data or privacy would leak out in the cloud. To allay users' concern of data control, data ownership, security and privacy, we propose a robust data protection framework which is surrounded by a chain of protection schemes from access control, monitoring, to active auditing. The framework includes three key components which are Cloud-based Privacy-aware Role Based Access Control (CPRBAC) model, Triggerable Data File Structure (TDFS), and Active Auditing Scheme (AAS) respectively. Our schemes address controllability, trace ability of data and authorize access to healthcare system resource. Data violation against access control policies can be proactively triggered to perform corresponding defense mechanisms. Our goal is to bring benefits of cloud computing to healthcare industries to assist them improve quality of service and reduce the cost of overall healthcare. © 2011 IEEE.	en_US
dc.relation.ispartof	Proc. 10th IEEE Int. Conf. on Trust, Security and Privacy in Computing and Communications, TrustCom 2011, 8th IEEE Int. Conf. on Embedded Software and Systems, ICESS 2011, 6th Int. Conf. on FCST 2011	en_US
dc.relation.isbasedon	10.1109/TrustCom.2011.19	en_US
dc.title	Towards scalable, fine-grained, intrusion-tolerant data protection models for healthcare cloud	en_US
dc.type	Conference Proceeding
utslib.for	0805 Distributed Computing	en_US
dc.location.activity	ChangSha, China	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
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Despite cloud computing has been widely adopted by most industries, the healthcare industry still reveals a slow development in cloud-based solution due to the raising of user fear that their confidential health data or privacy would leak out in the cloud. To allay users' concern of data control, data ownership, security and privacy, we propose a robust data protection framework which is surrounded by a chain of protection schemes from access control, monitoring, to active auditing. The framework includes three key components which are Cloud-based Privacy-aware Role Based Access Control (CPRBAC) model, Triggerable Data File Structure (TDFS), and Active Auditing Scheme (AAS) respectively. Our schemes address controllability, trace ability of data and authorize access to healthcare system resource. Data violation against access control policies can be proactively triggered to perform corresponding defense mechanisms. Our goal is to bring benefits of cloud computing to healthcare industries to assist them improve quality of service and reduce the cost of overall healthcare. © 2011 IEEE.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/19089