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dcc:pdpsol:de-identification [2026/02/17 09:42] – add text marlondcc:pdpsol:de-identification [2026/02/17 10:09] (current) – marlon
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 De-identification is the masking, manipulation or removal of personal data with the aim to make individuals in a dataset less easy to identify. It is especially important when you want to share, publish or archive your dataset. Before sharing, publishing or archiving your data, you should determine whether it is possible to de-identify your dataset, while also keeping in mind its usability.  De-identification is the masking, manipulation or removal of personal data with the aim to make individuals in a dataset less easy to identify. It is especially important when you want to share, publish or archive your dataset. Before sharing, publishing or archiving your data, you should determine whether it is possible to de-identify your dataset, while also keeping in mind its usability. 
  
-==== Anonymization and pseudonymization ====+==== Anonymization versus pseudonymization ==== 
 === Pseudonymization === === Pseudonymization ===
-Pseudonymization is a de-identification procedure during which personally identifiable information is replaced by an unique alias or code (pseudonym). In general, the names and/or contact details of data subjects are stored with this pseudonym in a so-called keyfile. The keyfile enables the re-identification of individuals in the dataset. Keyfiles are stored separately from the rest of the data and access should be restricted. In contrast to an anonymized dataset, a pseudonymized dataset in principle still allows for the re-identification of data subjects. +Pseudonymization is a de-identification procedure which is often implemented during data collection. During pseudonymization personally identifiable information is replaced by an unique alias or code (pseudonym). In general, the names and/or contact details of data subjects are stored with this pseudonym in a so-called keyfile. The keyfile enables the re-identification of individuals in the dataset. Keyfiles are stored separately from the rest of the data and access should be restricted. In contrast to an anonymized dataset, a pseudonymized dataset in principle still allows for the re-identification of data subjects. 
  
-Refer to our page on pseudonymization for practical advise on its implementation. +[[pseudonymization|→ Refer to our page on pseudonymization for practical advise on its implementation.]] 
  
 === Anonymization === === Anonymization ===
 Anonymization is a de-identification procedure during which “personal data is altered in such a way that a data subject can no longer be identified directly or indirectly, either by the data controller alone or in collaboration with any other party." ([[https://www.iso.org/standard/63553.html|ISO 25237:2017 Health informatics -- Pseudonymization]]. ISO. 2017. p. 7.). In contrast to a pseudonymized dataset, an anonymized dataset does not allow for the re-identification of data subjects and is therefore no longer considered personal data.   Anonymization is a de-identification procedure during which “personal data is altered in such a way that a data subject can no longer be identified directly or indirectly, either by the data controller alone or in collaboration with any other party." ([[https://www.iso.org/standard/63553.html|ISO 25237:2017 Health informatics -- Pseudonymization]]. ISO. 2017. p. 7.). In contrast to a pseudonymized dataset, an anonymized dataset does not allow for the re-identification of data subjects and is therefore no longer considered personal data.  
 +
 +**Warning:** de-identification does not equal anonymization. Although all direct identifiers and your pseudonymization key have been replaced or removed, it might still be possible to re-identify some data subjects in your data because, in combination, certain attributes (e.g., combination of height, job occupation and location of data collection) may single out an individual.
  
 ==== De-identification techniques ==== ==== De-identification techniques ====
 There are several techniques that can make your dataset less identifiable. Check out possible techniques to de-identify your data below, but be aware that these techniques often affect its analytical value.  There are several techniques that can make your dataset less identifiable. Check out possible techniques to de-identify your data below, but be aware that these techniques often affect its analytical value. 
  
-=== Remove or suppress ===+=== Removing or suppressing ===
 Consider whether you can remove or suppress sensitive elements. Consider whether you can remove or suppress sensitive elements.
   * Remove variables that reveal rare personal attributes.   * Remove variables that reveal rare personal attributes.
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   * Use restricted access to your data and only provide those variables to researchers that are necessary to answer their research question.   * Use restricted access to your data and only provide those variables to researchers that are necessary to answer their research question.
  
-=== Replace or mask === +=== Replacing or masking === 
 A practice in which you replace sensitive personal data with values or codes that are not sensitive: A practice in which you replace sensitive personal data with values or codes that are not sensitive:
   * Replace direct identifiers (‘name’) with a pseudonym (‘X’).   * Replace direct identifiers (‘name’) with a pseudonym (‘X’).
   * Make numerical values less precise.   * Make numerical values less precise.
   * Replace identifiable text with ‘[redacted]’.   * Replace identifiable text with ‘[redacted]’.
-Masking is typically partial, i.e. applied only to some characters in the attribute. For example, in the case of a postal code: change 9746DC into 97****. +Masking is typically partial, i.e. applied only to some characters in the attribute. For example, in the case of a postal code: change 9746DC into 97∗∗∗∗. 
  
 === Aggregation & generalization === === Aggregation & generalization ===