An Effective Approach for Hiding Sensitive Knowledge in Data Publishing

Abstract

Recent efforts have been made to address the problem of privacy preservation in data publishing. However, they mainly focus on preserving data privacy. In this paper, we address another aspect of privacy preservation in data publishing, where some of the knowledge implied by a dataset are regarded as private or sensitive information. In particular, we consider that the data are stored in a transaction database, and the knowledge is represented in the form of patterns. We present a data sanitization algorithm, called SanDB, for effectively protecting a set of sensitive patterns, meanwhile attempting to minimize the impact of data sanitization on the non-sensitive patterns. The experimental results show that SanDB can achieve significant improvement over the best approach presented in the literature.