Differences

This shows you the differences between two versions of the page.

--- dcc:pdpsol:de-identification [2026/05/11 14:28] – text editing based on feedback marlon
+++ dcc:pdpsol:de-identification [2026/05/13 13:23] (current) – alba
@@ Line 1: / Line 1: @@
 {{indexmenu_n>2}}
-===== De-identification, Anonymization and Pseudonymization =====
+===== De-identification, anonymization and pseudonymization =====
 ==== Introduction ====
 De-identification is the masking, manipulation or removal of personal data with the aim of making individuals in a dataset less easy to identify. It is especially important when you want to share, publish or archive your dataset, but it can also help protect your participants' privacy in case of a [[https://www.rug.nl/digital-competence-centre/privacy-and-data-protection/data-protection/data-leak|data leak]] during your research. During the different phases of your research, you should determine whether it is possible to de-identify your dataset while also keeping in mind its usability.
@@ Line 24: / Line 24: @@
 ==== General de-identification techniques ====
-Use the de-identification techniques outlined below to reduce the identifiability of your dataset. You can apply these techniques during different phases of your research:
+Use the de-identification techniques outlined below to reduce the identifiability of your dataset. Be aware that these techniques often affect its analytical value. Therefore, always make sure to document the way you transformed your data.
-  * After data collection to protect participants when analyzing their data
+You can apply these techniques during different phases of your research:
+  * After data collection, to protect participants when analyzing their data
   * Before sharing data with collaborators or other third parties
   * Before archiving data
   * Before publishing data (with access restrictions)
-Be aware that these techniques often affect its analytical value. Therefore, always make sure to document the way you transformed your data.