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

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Title: Protecting organizational data confidentiality in the cloud using a high-performance anonymization engine
Authors: Ayala-Rivera, VanessaNowak, DawidMcDonagh, Patrick
Permanent link: http://hdl.handle.net/10197/8764
Date: 10-May-2013
Online since: 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.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Keywords: Cloud computingSaasData confidentialityData anonymizationPerformance
Other versions: http://hdl.handle.net/10344/3225
http://ittconference.ie/index.php?page=12th-annual-conference---ait
Language: en
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/
Appears in Collections:Computer Science Research Collection

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