Skip to Main content Skip to Navigation
Journal articles

A semantic approach for semi-automatic detection of sensitive data

Abstract : 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.
Document type :
Journal articles
Complete list of metadatas
Contributor : Laboratoire Cedric <>
Submitted on : Friday, March 6, 2015 - 12:00:58 PM
Last modification on : Monday, November 30, 2020 - 3:00:06 PM

Links full text



Jacky Akoka, Isabelle Comyn-Wattiau, Cedric Du Mouza, Hammou Fadili, Nadira Lammari, et al.. A semantic approach for semi-automatic detection of sensitive data. Information Resources Management Journal, IGI Global, 2014, 27 (4), pp.23-44. ⟨10.4018/irmj.2014100102⟩. ⟨hal-01126551⟩



Record views