Some of our research focuses on masking methods for databases. These methods are applied when data need to be protected prior to its use for data analysis. Masking methods modify databases to avoid disclosure and trying to keep data utility. A good masking method is one that achieves a good trade-off between disclosure risk and data utility. Other research focuses on methods to avoid disclosure from analysis from a database (e.g., disclosure from a data-driven machine learning model).
