Data sharing among different entities in the healthcare domain has become an increasingly common practice, where each entity would most likely want to prevent indirect data disclosure via inference channels. The Local Distortion Hiding (LDH) algorithm has been developed to protect sensitive decision tree (DT) rules, which are chosen not to be disclosed when DT construction techniques are applied to the data. This article presents eight experiments using a Java-based prototype that implements the LDH algorithm in a diabetes data set. Our experiments test the ability of the LDH algorithm in two ways, firstly in inference control and secondly in maintaining the structure and the performance metrics of the resulting DT. Our experiments on hiding eight terminal nodes in a diabetes data set using a Java-based prototype that implements the LDH algorithm, yield satisfactory results.
Keywords: Inference control; data security; machine learning; privacy-preserving.