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--- dcc:pdpsol:de-identification [2026/06/18 11:06] – marlon
+++ dcc:pdpsol:de-identification [2026/07/14 10:12] (current) – marlon
@@ Line 93: / Line 93: @@
 ==== 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.
+++++ (Click) Face and body masking | You can use video editing software, such as [[https://www.adobe.com/nl/products/photoshop.html| Adobe Premiere]], or a simple command-line tool, such as [[https://pypi.org/project/deface/|Deface]], 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).
-++++
 ++++ (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.