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

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Download HPAE_PaperITT.pdf608.14 KB
Author(s)
Ayala-Rivera, Vanessa 
Nowak, Dawid 
McDonagh, Patrick 
Uri
http://hdl.handle.net/10197/8764
Date Issued
10 May 2013
Date Available
18T11:39:15Z September 2017
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
Keywords
  • 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
Description
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/
Owning collection
Computer Science Research Collection
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