A Three Layered Model to Implement Data Privacy Policies

Gerardo Canfora; Corrado Aaron Visaggio

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Paper

A Three Layered Model to Implement Data Privacy Policies

In Proceedings of the 5th International Workshop on Security in Information Systems - Volume 0ICEIS, 155-165, 2007 , Funchal, Madeira, Portugal

Authors: Gerardo Canfora and Corrado Aaron Visaggio

Affiliation: Research Centre on Software Technology, University of Sannio, Italy

Abstract: Many business services for private companies and citizens are increasingly accomplished trough the web and mobile devices. As such a scenario is characterized by high dynamism and untrustworthiness, existing technologies could be unsuccessful. This paper proposes an approach, inspired to the front-end trust filter paradigm, to manage data privacy in a very flexible way. Our approach has the potential to reduce the change impact due to the dynamism and to foster the reuse of strategies, and their implementations, across organizations.

CC BY-NC-ND 4.0

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Paper citation in several formats:

Canfora, G. and Aaron Visaggio, C. (2007). A Three Layered Model to Implement Data Privacy Policies. In Proceedings of the 5th International Workshop on Security in Information Systems (ICEIS 2007) - WOSIS; ISBN 978-972-8865-96-2, SciTePress, pages 155-165. DOI: 10.5220/0002418301550165

@conference{wosis07,
author={Gerardo Canfora. and Corrado {Aaron Visaggio}.},
title={A Three Layered Model to Implement Data Privacy Policies},
booktitle={Proceedings of the 5th International Workshop on Security in Information Systems (ICEIS 2007) - WOSIS},
year={2007},
pages={155-165},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002418301550165},
isbn={978-972-8865-96-2},
}

TY - CONF

JO - Proceedings of the 5th International Workshop on Security in Information Systems (ICEIS 2007) - WOSIS
TI - A Three Layered Model to Implement Data Privacy Policies
SN - 978-972-8865-96-2
AU - Canfora, G.
AU - Aaron Visaggio, C.
PY - 2007
SP - 155
EP - 165
DO - 10.5220/0002418301550165
PB - SciTePress