Hiding sensitive association rules using the optimal electromagnetic optimization method and a dynamic bit vector data structure

Abstract

Hiding the association rules is one of the methods used to protect sensitive information in data-mining processes. Its goal is to transform the original dataset so that the support for, or the reliability of, sensitive rules is reduced below the minimum threshold. Then these sensitive rules cannot be exploited, while the rules that are non-sensitive can still be exploited normally. Many methods have been proposed for hiding the association rules. However, most of these methods are very slow and consume a large amount of storage space. Consequently, they are not suitable when mining large datasets. Recently, the electromagnetic field optimization (EFO4ARH) method was proposed, and it was found to hide the sensitive association rules better than the other methods. To increase mining efficiency further, this paper proposes a new workaround called EFODBV4ARH. This technique applies a dynamic bit vector data structure in combination with the electromagnetic field optimization method. Experimental results indicate that EFODBV4ARH is significantly more efficient than EFO4ARH.