Hiding frequent patterns under multiple sensitive thresholds

Abstract

Frequent pattern mining is a popular topic in data mining. With the advance of this technique, privacy issues attract more and more attention in recent years. In this field, previous works based hiding sensitive information on a uniform support threshold or a disclosure threshold. However, in practical applications, we probably need to apply different support thresholds to different itemsets for reflecting their significance. In this paper, we propose a new hiding strategy to protect sensitive frequent patterns with multiple sensitive thresholds. Based on different sensitive thresholds, the sanitized dataset is able to highly fulfill user requirements in real applications, while preserving more information of the original dataset. Empirical studies show that our approach can protect sensitive knowledge well not only under multiple thresholds, but also under a uniform threshold. Moreover, the quality of the sanitized dataset can be maintained. © 2008 Springer-Verlag Berlin Heidelberg.