Today’s session looked at de-identificacion as a way of changing data sets so that it is not that easy anymore to identify individuals in the dataset. The first steps of de-identification is pseudonymization. Pseudonymization means removing direct identifiers and exchanging them for random data. However, as you have seen this is not enough to de-identify a dataset, as other data such as number of children or gender could allow to identify someone or attribute specific assumptions to a person. Even though de-identification is not a silver bullet, it is still an important instrument to protect personal data. Remember, when thinking through your process of de-identification it is important to keep in mind the context of the individuals in your dataset, and other data that is already in the ‘wild’ about them in account.