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A Semantic Approach for Semi-Automatic Detection of Sensitive Data

A Semantic Approach for Semi-Automatic Detection of Sensitive Data

Jacky Akoka, Isabelle Comyn-Wattiau, Cédric Du Mouza, Hammou Fadili, Nadira Lammari, Elisabeth Metais, Samira Si-Said Cherfi
Copyright: © 2014 |Volume: 27 |Issue: 4 |Pages: 22
ISSN: 1040-1628|EISSN: 1533-7979|EISBN13: 9781466657519|DOI: 10.4018/irmj.2014100102
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MLA

Akoka, Jacky, et al. "A Semantic Approach for Semi-Automatic Detection of Sensitive Data." IRMJ vol.27, no.4 2014: pp.23-44. http://doi.org/10.4018/irmj.2014100102

APA

Akoka, J., Comyn-Wattiau, I., Du Mouza, C., Fadili, H., Lammari, N., Metais, E., & Cherfi, S. S. (2014). A Semantic Approach for Semi-Automatic Detection of Sensitive Data. Information Resources Management Journal (IRMJ), 27(4), 23-44. http://doi.org/10.4018/irmj.2014100102

Chicago

Akoka, Jacky, et al. "A Semantic Approach for Semi-Automatic Detection of Sensitive Data," Information Resources Management Journal (IRMJ) 27, no.4: 23-44. http://doi.org/10.4018/irmj.2014100102

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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.

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