Reconstruction-based Classification Rule Hiding through Controlled Data Modification

In this paper, we propose a reconstruction–based approach to classification rule hiding in categorical datasets. The proposed methodology modifies transactions supporting both sensitive and nonsensitive classification rules in the original dataset and then uses the supporting transactions of the nonsensitive rules to produce its sanitized counterpart. To further investigate some interesting properties of this methodology, we explore three variations of the main technique which differ in the way they select and sanitize transactions supporting sensitive rules. Finally, through extensive experimental evaluation, we demonstrate the effectiveness of the proposed algorithms towards effectively shielding the sensitive knowledge.