Statistical disclosure control and quality evaluation of synthetic data generated by non-parametric methods

This PhD aims to continue the work in statistical disclosure control by improving the quality of synthetic data generated through non-parametric methods (such as GAN or CART). To achieve this, it will be necessary to extend existing exact inferential procedures for data generated by single imputation to cases of multiple imputation, perform the computational refinement of the degrees of freedom defined in asymptotic inferential procedures for vectors used in their adaptation to matrix inference, and adapt GAN methods for generating partially or completely synthetic data for a sequence of sensitive variables describing a profile. Finally, simulations will be carried out, along with the application to a real database.