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  5. Protecting organizational data confidentiality in the cloud using a high-performance anonymization engine
 
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Protecting organizational data confidentiality in the cloud using a high-performance anonymization engine

Author(s)
Ayala-Rivera, Vanessa  
Nowak, Dawid  
McDonagh, Patrick  
Uri
http://hdl.handle.net/10197/8764
Date Issued
2013-05-10
Date Available
2017-09-18T11:39:15Z
Abstract
Data security remains a top concern for the adoption of cloud-based delivery models, especially in the case of the Software as a Service (SaaS). This concern is primarily caused due to the lack of transparency on how customer data is managed. Clients depend on the security measures implemented by the service providers to keep their information protected. However, not many practical solutions exist to protect data from malicious insiders working for the cloud providers, a factor that represents a high potential for data breaches. This paper presents the High-Performance Anonymization Engine (HPAE), an approach to allow companies to protect their sensitive information from SaaS providers in a public cloud. This approach uses data anonymization to prevent the exposure of sensitive data in its original form, thus reducing the risk for misuses of customer information. This work involved the implementation of a prototype and an experimental validation phase, which assessed the performance of the HPAE in the context of a cloud-based log management service. The results showed that the architecture of the HPAE is a practical solution and can efficiently handle large volumes of data.
Sponsorship
Science Foundation Ireland
Type of Material
Conference Publication
Subjects

Cloud computing

Saas

Data confidentiality

Data anonymization

Performance

Web versions
http://hdl.handle.net/10344/3225
http://ittconference.ie/index.php?page=12th-annual-conference---ait
Language
English
Status of Item
Peer reviewed
Conference Details
12th Information Technology &Telecommunications (IT&T) Conference, Athlone, Ireland, March, 2013
ISSN
1649‐1246
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
File(s)
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HPAE_PaperITT.pdf

Size

608.14 KB

Format

Adobe PDF

Checksum (MD5)

c706c6812f42d0ecfdb72c787bda8c89

Owning collection
Computer Science Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
All other content is subject to copyright.

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