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Détection des Données à Caractère Personnel dans les Bases Multidimensionnelles
In EDA 2019, vol. RNTI-B-15, pp.31-44
Mapping personal data in the process for de-identification is always a prerequisite. Today
with the big data, we are obliged to automate the detection step in this process. That avoids
time-consuming, increases the accuracy of detection and allow to ensure confidentiality. For all
these reasons we have proposed a new approach to exploit the data. Our approach in this work
is adapted to multidimensional databases. Our techniques used in this approach are based on
two levels. We propose two detection solutions at the data level, and a solution at the metadata
level. After detecting personal data in a database using the identification scores, we use the
sensitivity scores to assess the total sensitivity of the multidimensional database before and
after anonymization.