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International Conference on Cloud Computing and Services Science

CLOSER 2014: Cloud Computing and Services Sciences pp 117–133Cite as

A Data Location Control Model for Cloud Service Deployments

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 512))

Abstract

A data location control model for Cloud services is presented. The model is intended for use by Cloud SaaS providers that collect personal data that can potentially be stored and processed at multiple geographic locations. It incorporates users’ location preferences into authorization decisions by converting them into XACML policies that are consulted before data transfer operations. The model also ensures that the users have visibility into the location of their data and are informed when the location of their data changes. A prototype of the model has been implemented and was used to perform validation tests in various Cloud setups. These scenarios serve to demonstrate how location control can be integrated on top of existing public and private Cloud platforms. A sketch is also provided of an architecture that embeds location control functionality directly into the OpenStack Cloud platform. We further propose an enhancement to the model that alters its behaviour from being restrictive to prescriptive so that Cloud providers can copy data to a non-preferred locations in case of emergency. Under this approach, the number of authorized vs unauthorized transfers can be made publicly available by the provider as an assurance measure for consumers.

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Notes

  1. 1.

    http://143.239.71.90:8013/data_location/User_registration.html.

  2. 2.

    http://sec.cs.kent.ac.uk/permis/downloads/Level3/standalone.shtml.

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Acknowledgements

The research work described in this paper was supported by the Irish Centre for Cloud Computing and Commerce, an Irish national Technology Centre funded by Enterprise Ireland and the Irish Industrial Development Authority.

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Authors and Affiliations

  1. Irish Centre for Cloud Computing and Commerce, University College Cork, Cork, Ireland

    Kaniz Fatema, Philip D. Healy, Vincent C. Emeakaroha & John P. Morrison

  2. Irish Centre for Cloud Computing and Commerce, Dublin City University, Dublin, Ireland

    Theo Lynn

Authors
  1. Kaniz Fatema
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  2. Philip D. Healy
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  3. Vincent C. Emeakaroha
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  4. John P. Morrison
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  5. Theo Lynn
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Corresponding author

Correspondence to Kaniz Fatema .

Editor information

Editors and Affiliations

  1. School of Computing, Dublin City University, Dublin 9, Ireland

    Markus Helfert

  2. LIP / Inria Ecole normale supérieure de Lyon, Lyon, France

    Frédéric Desprez

  3. Dell, ROUND ROCK, USA

    Donald Ferguson

  4. University of Stuttgart, Stuttgart, Germany

    Frank Leymann

  5. Universitat Autònoma de Barcelona, Bellaterra, Spain

    Victor Méndez Munoz

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Fatema, K., Healy, P.D., Emeakaroha, V.C., Morrison, J.P., Lynn, T. (2015). A Data Location Control Model for Cloud Service Deployments. In: Helfert, M., Desprez, F., Ferguson, D., Leymann, F., Méndez Munoz, V. (eds) Cloud Computing and Services Sciences. CLOSER 2014. Communications in Computer and Information Science, vol 512. Springer, Cham. https://doi.org/10.1007/978-3-319-25414-2_8

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  • DOI: https://doi.org/10.1007/978-3-319-25414-2_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25413-5

  • Online ISBN: 978-3-319-25414-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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