Hiding sensitive XML Association Rules via Bayesian network

Privacy Preserving Data Mining (PPDM) is receiving a lot of attention recently by researchers from multiple domains, especially in Association Rule Mining. The outputs of Association Rule Mining often involve values of attributes that can be used to characterize the identities of the users. The relations between antecedents and consequents are also explicitly displayed. The purpose of preserving association rules is to minimize the risk of disclosing sensitive information to external parties. In this paper, we proposed a PPDM model for XML Association Rules (XARs). The proposed model identifies the most probable items called `sensitive items', and to modify their original data sources, so that the resultant XARs can have higher accuracy and stronger reliability. Such reliability is not addressed before in the literature in any kind of methodology used in PPDM domain and especially in XML association rules mining. Thus, the significance of the suggested model sets to open a new research dimension to the academia in order to control the sensitive information in a more unyielding line of attack.