Relational Database Anonymization - A Model-driven Guiding Approach - Archive ouverte HAL Access content directly
Conference Papers Year : 2018

Relational Database Anonymization - A Model-driven Guiding Approach

Abstract

Personal data anonymization requires complex algorithms aiming at avoiding disclosure risk without compromising data utility. In this paper, we describe a model-driven approach guiding the data owner during the anonymization process. Depending on the step, the guidance is informative or suggestive. It helps in choosing the most relevant algorithm given the data characteristics and the future usage of anonymized data. It also helps in defining the best input values for the chosen algorithm. The contribution is twofold: a meta-model describing the anonymization process and components and an approach based on this meta-model. In this paper, we focus on microdata generalization algorithms. Both theoretical and experimental knowledge regarding anonymization is stored in an ontology. An experiment, conducted with sixteen participants allowing us to check the usability of the approach, is described.
Fichier principal
Vignette du fichier
ICISSP_2018_66.pdf (495.87 Ko) Télécharger le fichier
Origin : Publisher files allowed on an open archive
Loading...

Dates and versions

hal-02469154 , version 1 (01-04-2020)

Licence

Attribution - NonCommercial - NoDerivatives

Identifiers

Cite

Feten Ben Fredj, Nadira Lammari, Isabelle Comyn-Wattiau. Relational Database Anonymization - A Model-driven Guiding Approach. 4th International Conference on Information Systems Security and Privacy, Jan 2018, Funchal, France. pp.161-170, ⟨10.5220/0006659201610170⟩. ⟨hal-02469154⟩
127 View
69 Download

Altmetric

Share

Gmail Facebook X LinkedIn More