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dcc:pdpsol:de-identification [2026/06/18 11:05] – marlondcc:pdpsol:de-identification [2026/06/25 11:05] (current) – marlon
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 **Table 1:** De-identification matrix adapted from [[https://lcrdm.nl/wp-content/uploads/2023/03/LCRDM-Risk-management-for-research-data-about-people.pdf|LCRDM (2019)]]. This matrix is an example of what de-identification and anonymization could look like in research. The identifiability of your data largely depends on the context of your research and only partly on the variables you collected. For example, the variable judge could be more identifiable for a person living in Leeuwarden than for a person living in Amsterdam, because more judges live in Amsterdam.  **Table 1:** De-identification matrix adapted from [[https://lcrdm.nl/wp-content/uploads/2023/03/LCRDM-Risk-management-for-research-data-about-people.pdf|LCRDM (2019)]]. This matrix is an example of what de-identification and anonymization could look like in research. The identifiability of your data largely depends on the context of your research and only partly on the variables you collected. For example, the variable judge could be more identifiable for a person living in Leeuwarden than for a person living in Amsterdam, because more judges live in Amsterdam. 
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 ==== Research specific de-identification techniques ====  ==== Research specific de-identification techniques ==== 
-=== Video data === +=== Videos or images === 
-Researchers use video to record real-world behaviour, interactions, or experiments in detail, for example, tracking how people move, communicate, or perform tasks over time. It is important to de-identify this type of data, because videos can easily reveal faces, voices, or surroundings, and leaving those visible can reveal participants’ identities.+Researchers use videos or images to record real-world behaviour, interactions, or experiments in detail, for example, tracking how people move, communicate, or perform tasks over time. It is important to de-identify this type of data, because they can easily reveal faces, voices, or surroundings, and leaving those visible can reveal participants’ identities.  
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 +++++ (Click) Face and body masking | You can use video editing software, such as [[https://www.adobe.com/nl/products/photoshop.html| Adobe Premiere]], to distort or obscure identifiable information in videos. For images, tools like Paint or [[https://www.adobe.com/nl/products/photoshop.html|Adobe Photoshop]] can be used to blur or pixelate personal identifiable information. ++++ 
  
-++++ (Click) Face and body masking |[[https://github.com/MaskAnyone/MaskAnyone|MaskAnyone]] is a de-identification toolbox for videos that allows you to remove personal identifiable information from videos, while at the same time preserving utility. It provides a variety of algorithms that allow you to de-identify or even anonymize videos (video & audio).  
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 ++++ (Click) Metadata de-identification | ++++ (Click) Metadata de-identification |
 Even after de-identifying video data so it's unrecognizable to people or machines, metadata, such as timestamps or location tags, can still indirectly reveal participants’ identities. Even after de-identifying video data so it's unrecognizable to people or machines, metadata, such as timestamps or location tags, can still indirectly reveal participants’ identities.