Determining t in t-closeness using Multiple Sensitive Attributes
Debaditya Roy, Sanjay Kumar Jena · International Journal of Computer Applications · 2013
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 tcloseness 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.