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Active data-centric framework for data protection in cloud environment

Chen, Lingfeng and Hoang, Doan B. 2012, Active data-centric framework for data protection in cloud environment, in ACIS 2012 : Location, location, location : Proceedings of the 23rd Australasian Conference on Information Systems 2012, ACIS, [Geelong, Vic.], pp. 1-11.

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Title Active data-centric framework for data protection in cloud environment
Author(s) Chen, Lingfeng
Hoang, Doan B.
Conference name Australasian Conference on Information Systems (23rd : 2012 : Geelong, Victoria)
Conference location Geelong, Victoria
Conference dates 3-5 Dec. 2012
Title of proceedings ACIS 2012 : Location, location, location : Proceedings of the 23rd Australasian Conference on Information Systems 2012
Editor(s) Lamp, JohnORCID iD for Lamp, John orcid.org/0000-0003-1891-0400
Publication date 2012
Conference series Australasian Conference on Information Systems
Start page 1
End page 11
Total pages 11
Publisher ACIS
Place of publication [Geelong, Vic.]
Keyword(s) cloud Computing
active data-centric
zero-knowledge proof
transformation
tamper-proof
Summary Cloud computing is an emerging evolutionary computing model that provides highly scalable services over highspeed Internet on a pay-as-usage model. However, cloud-based solutions still have not been widely deployed in some sensitive areas, such as banking and healthcare. The lack of widespread development is related to users’ concern that their confidential data or privacy would leak out in the cloud’s outsourced environment. To address this problem, we propose a novel active data-centric framework to ultimately improve the transparency and accountability of actual usage of the users’ data in cloud. Our data-centric framework emphasizes “active” feature which packages the raw data with active properties that enforce data usage with active defending and protection capability. To achieve the active scheme, we devise the Triggerable Data File Structure (TDFS). Moreover, we employ the zero-knowledge proof scheme to verify the request’s identification without revealing any vital information. Our experimental outcomes demonstrate the efficiency, dependability, and scalability of our framework.
Notes Reproduced with the kind permission of the copyright owner.
Language eng
Field of Research 089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
Related work DU:30049020
Copyright notice ©2012, The Authors/ACIS
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30049130

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