Privacy Preserving Framework for Big Data Management in Smart Buildings

Inibhunu, C; McGregor, C

Privacy Preserving Framework for Big Data Management in Smart Buildings

Inibhunu, C McGregor, C

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), 2021, 00, pp. 667-673
Issue Date:: 2021-05-25

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Field	Value	Language
dc.contributor.author	Inibhunu, C
dc.contributor.author	McGregor, C https://orcid.org/0000-0002-0491-4403
dc.date	2021-03-22
dc.date.accessioned	2021-09-17T18:56:25Z
dc.date.available	2021-09-17T18:56:25Z
dc.date.issued	2021-05-25
dc.identifier.citation	2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), 2021, 00, pp. 667-673
dc.identifier.isbn	9781665404242
dc.identifier.uri	http://hdl.handle.net/10453/150625
dc.description.abstract	There are many possibilities for smart buildings that have well provisioned and managed internet of things (IoT) frameworks where seamless data acquisition from sensors, processing to analytics can bring benefits to vast domains. Specifically, in building management, data captured from sensors, actuators and multiple devices within a building can be analyzed and utilized for resource planning as well as informed services provision while maintaining the security and privacy of IoT data sources and context. To facilitate such a process, a robust framework for acquisition, processing and analysis of data from vast IoT systems is necessary. This is an extremely complex procedure that requires augmentation of multiple computation layers for management of IoT data coupled with privacy preservation capabilities. In this paper, an overview of IoT and the data process flow from acquisition, collection, processing, integration and the technological enablers is presented. In particular this work examines methods proposed in the literature for management of IoT data from acquisition to analytical applications, highlights challenges and opportunities that are still open for research and proposes a privacy preserving computation framework that can be explored for robust processing of IoT data in a smart building ecosystem leading to effective provision of services while maintaining privacy of IoT data sources and context.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops)
dc.relation.ispartof	2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events
dc.relation.isbasedon	10.1109/percomworkshops51409.2021.9430994
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Privacy Preserving Framework for Big Data Management in Smart Buildings
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Kassel, Germany
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 Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2021-09-17T18:56:24Z
pubs.finish-date	2021-03-26
pubs.publication-status	Published
pubs.start-date	2021-03-22
pubs.volume	00

Abstract:

There are many possibilities for smart buildings that have well provisioned and managed internet of things (IoT) frameworks where seamless data acquisition from sensors, processing to analytics can bring benefits to vast domains. Specifically, in building management, data captured from sensors, actuators and multiple devices within a building can be analyzed and utilized for resource planning as well as informed services provision while maintaining the security and privacy of IoT data sources and context. To facilitate such a process, a robust framework for acquisition, processing and analysis of data from vast IoT systems is necessary. This is an extremely complex procedure that requires augmentation of multiple computation layers for management of IoT data coupled with privacy preservation capabilities. In this paper, an overview of IoT and the data process flow from acquisition, collection, processing, integration and the technological enablers is presented. In particular this work examines methods proposed in the literature for management of IoT data from acquisition to analytical applications, highlights challenges and opportunities that are still open for research and proposes a privacy preserving computation framework that can be explored for robust processing of IoT data in a smart building ecosystem leading to effective provision of services while maintaining privacy of IoT data sources and context.

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http://hdl.handle.net/10453/150625