This article proposes an innovative approach and its implementation as an expert system to achieve the semi-automatic detection of candidate attributes for scrambling sensitive data. Its approach is based on semantic rules that determine which concepts have to be scrambled, and on a linguistic component that retrieves the attributes that semantically correspond to these concepts. Because attributes cannot be considered independently from each other, it also addresses the challenging problem of the propagation of the scrambling process through the entire database. One main contribution of this article's approach is to provide a semi-automatic process for the detection of sensitive data. The underlying knowledge is made available through production rules operationalizing the detection of the sensitive data. A validation of its approach using four different databases is provided.