<abstract xmlns="http://www.w3.org/1999/xhtml"><p> In the present age of Internet, data is accumulated at a dramatic pace. The accumulated huge data has no relevance, unless it provides certain useful information pertaining to the interest of the organization. But the real challenge lies in hiding sensitive information in order to provide privacy. Therefore, attribute reduction becomes an important aspect for handling such huge database by eliminating superfluous or redundant data to enable a sensitive rule hiding in an efficient manner before it is disclosed to the public. In this paper we propose a privacy preserving model to hide sensitive fuzzy association rules. In our model we use two processes, named a pre-process and post-process to mine fuzzified association rules and to hide sensitive rules. Experimental results demonstrate the viability of the proposed research.</p></abstract>

In the present age of Internet, data is accumulated at a dramatic pace. The accumulated huge data has no relevance, unless it provides certain useful information pertaining to the interest of the organization. But the real challenge lies in hiding sensitive information in order to provide privacy. Therefore, attribute reduction becomes an important aspect for handling such huge database by eliminating superfluous or redundant data to enable a sensitive rule hiding in an efficient manner before it is disclosed to the public. In this paper we propose a privacy preserving model to hide sensitive fuzzy association rules. In our model we use two processes, named a pre-process and post-process to mine fuzzified association rules and to hide sensitive rules. Experimental results demonstrate the viability of the proposed research.

Privacy preservation in fuzzy association rules using rough computing and DSR

School of Computing Science and Engineering, VIT University, Vellore-632014, Tamilnadu, India

Cybernetics and Information Technologies

This article is distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

{"article-title":"Privacy preservation in fuzzy association rules using rough computing and DSR"}

In the present age of Internet, data is accumulated at a dramatic pace. The accumulated huge data has no relevance, unless it provides certain useful...

Privacy preservation in fuzzy association rules using rough computing and DSR

Publicado en línea: 09 abr 2014

Páginas: 52 - 71

DOI: https://doi.org/10.2478/cait-2014-0005

Palabras clave
Almost indiscernibility, fuzzy proximity relation, fuzzy approximation space, ordering rules

© by A. Geetha Mary

This article is distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Privacy preservation in fuzzy association rules using rough computing and DSR

Publicado en línea: 09 abr 2014

Páginas: 52 - 71

DOI: https://doi.org/10.2478/cait-2014-0005

Palabras claveAlmost indiscernibility, fuzzy proximity relation, fuzzy approximation space, ordering rules

© by A. Geetha Mary

This article is distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Palabras clave
Almost indiscernibility, fuzzy proximity relation, fuzzy approximation space, ordering rules