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.