Abstract—The sharing of information has been proven to be beneficial for business partnerships in many application areas such as business planning or marketing. Today, association rule mining imposes threats to data sharing, since it may disclose patterns and various kinds of sensitive knowledge that are difficult to find. Such information must be protected against unauthorized access. The challenge is to protect actionable knowledge for strategic decisions, but at the same time not to lose the great benefit of association rule mining. To address this challenge, a sanitizing process transforms the source database into a released database in which the counterpart cannot extract sensitive rules from it. Unlike existing works that focused on hiding sensitive association rules at a single concept level, this paper emphasizes on building a sanitizing algorithm for hiding association rules at multiple concept levels. Employing multi-level association rule mining may lead to the discovery of more specific and concrete knowledge from datasets. The proposed system uses genetic algorithm as a biogeography-based optimization strategy for modifying multi-level items in database in order to minimize sanitization’s side effects such as non-sensitive rules falsely hidden and fake rules falsely generated. The new approach is empirically tested and compared with other sanitizing algorithms depicting considerable improvement in completely hiding any given multi-level rule that in turn can fully support security of database and keeping the utility and certainty of mined multi-level rules at highest level.

Index Terms—Database sanitization, genetic algorithm, privacy preserving data mining, multi-level association rule hiding.

All authors are with the Institute of Graduate Studies and Research, Alexandria University, 163Horreya Avenue, El-Shatby, 21526 P.O. Box 832, Alexandria, Egypt (e-mail: Saad.darwish@alex-igsr.edu.eg, m.madbouly@alex-igsr.edu.eg, m_abdelhakeem@ymail.com).

Cite:Saad M. Darwish, Magda M. Madbouly, and Mohamed A. El-Hakeem, "A Database Sanitizing Algorithm for Hiding Sensitive Multi-Level Association Rule Mining," International Journal of Computer and Communication Engineering vol. 3, no. 4, pp. 285-293, 2014.