The week's pick
Reseach Article
Determining t in t-closeness using Multiple Sensitive Attributes
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 70 - Number 19 |
| Year of Publication: 2013 |
| Authors: Debaditya Roy, Sanjay Kumar Jena |
10.5120/12179-8291
|
Debaditya Roy, Sanjay Kumar Jena . Determining t in t-closeness using Multiple Sensitive Attributes. International Journal of Computer Applications. 70, 19 ( May 2013), 47-51. DOI=10.5120/12179-8291
Abstract
Over the years, t-closeness has been dealt with in great detail in Privacy Preserving Data Publishing and Mining. Other methods like k-anonymity fail in terms of attribute disclosure and background knowledge attack as demonstrated by many papers in this field. l-diversity also fails in case of skewness attack. t-closenesstakes care of all these shortcomings and is the most robust privacy model known till date. However, till now t-closeness was only applied upon a single sensitive attribute. Here, a novel way in determining t and applying t-closeness for multiple sensitive attributes is presented. The only information required beforehand is the partitioning classes of Sensitive Attribute(s). Since, t-closeness is generally applied on anonymized datasets, it is imperative to know the t values beforehand so as to unnecessarily anonymize data beyond requirement. The rationale of using the measure of determining t is discussed with conclusive proof and speedup achieved is also shown.
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