On Mining Sensitive Rules to Identify Privacy Threats

Data mining techniques represent a useful tool to cope with privacy problems. In this work an association rule mining algorithm adapted to the privacy context is developed. The algorithm produces association rules with a certain structure (the premise set is a subset of the public features of a released table while the consequent is the feature to protect). These rules are then used to reveal and explain relationships from data affected by some kind of anonymization process and thus, to detect threats.