A central tendency-based privacy preserving model for sensitive XML association rules using Bayesian networks

The rationale of XML design is to transfer and store data at different levels. A key feature of these levels in an XML document is to identify its components for additional processing. XML components can expose sensitive information after application of data mining techniques over a shared database. Therefore, privacy preservation of sensitive information must be ensured prior to signify the outcome especially in sensitive XML Association Rules. Privacy issues in XML domain are not exceptionally addressed to determine a solution by the academia in a reliable and precise manner. In this paper, we have proposed a model for identifying sensitive items (nodes) to declare sensitive XML association rules and then to hide them. Bayesian networks-based central tendency measures are applied in declaration of sensitive XML association rules. K2 algorithm is used to generate Bayesian networks to ensure reliability and accuracy in preserving privacy of XML Association Rules. The proposed model is tested and compared using several case studies and large UCI machine learning datasets. The experimental results show improved accuracy and reliability of proposed model without any side effects such as new rules and lost rules. The proposed model uses the same minimum support threshold to find XML Association Rules from the original and transformed data sources. The significance of the proposed model is to minimize an incredible disclosure risk involved in XML association rule mining from external parties in a competitive business environment.