These tutorials show how to use the already implemented libraries for the anonymization and antidiscrimination of data sets: - Anonymization library: https://test.pypi.org/project/anonymization-crisesurv/ (also available in https://github.com/CrisesUrv/SoBigDataTraining/tree/master/anonymization) - Antidiscrimination library: https://test.pypi.org/project/antidiscrimination-crisesurv/ (also available in https://github.com/CrisesUrv/SoBigData_antidiscrimination) Two jupyter notebooks have been developed (one for the anonymization library and one for the antidiscrimination library). They show step by step how to properly anonymize a data set, how to indicate the required privacy parameters and how to evaluate the resulting data. Both notebooks are included in this catalog item. In addition, to facilitate the training process, both notebooks have also been published as Google Colaboratory notebooks which allow them to be executed in the cloud or downloaded to be executed locally.