A Semantic Approach for Semi-Automatic Detection of Sensitive Data

We propose an innovative approach and its implementation as an expert system to achieve the semiautomatic detection of candidate attributes for scrambling sensitive data. Our 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, we also address the challenging problem of the propagation of the scrambling process through the entire database. One main contribution of our 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 our approach using four different databases is provided.