| Both sides previous revision Previous revision Next revision | Previous revision |
| dcc:pdpsol:de-identification [2026/03/17 16:01] – marlon | dcc:pdpsol:de-identification [2026/03/23 14:09] (current) – add go back to P&DP home page marlon |
|---|
| ==== Research specific de-identification techniques ==== | ==== Research specific de-identification techniques ==== |
| === Video data === | === Video data === |
| | Researchers use video to record real-world behavior, 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. |
| |
| ++++ 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 allows you to de-identify or even anonymize videos (video & audio). | ++++ 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 allows you to de-identify or even anonymize videos (video & audio). |
| 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. |
| To protect participant privacy, always remove or mask the following metadata: | To protect participant privacy, always remove or mask the following metadata: |
| * location data (e.g. GPS coordinates) | * Location data (e.g. GPS coordinates) |
| * Network identifiers (e.g. IP addresses) | * Network identifiers (e.g. IP addresses) |
| * Device or user IDs (e.g. serial numbers, or account IDs) | * Device or user IDs (e.g. serial numbers, or account IDs) |
| |
| === Audio data === | === Audio data === |
| | Audio recordings are typically collected to capture exactly what participants say during interviews or focus groups, or to study voice patterns. Audio data itself can contain identifying information: Participants may be recognizable from their voice by other people, and modern speech recognition technologies can also be used to identify participants. For this reason, audio data should be de-identified before further use or sharing. |
| |
| ++++ Transcription | | ++++ Transcription | |
| Audio recordings in research are typically collected to capture exactly what participants say during interviews or focus groups. However, audio data itself can contain identifying information. Participants may be recognizable from their voice by other people, and modern speech recognition technologies can also be used to identify participants. For this reason, audio data should be de-identified before further use or sharing. A common approach is to convert the recordings into written transcripts and then work only with the text data. Transcription removes the direct voice signal that could reveal the speaker’s identity. | A common step in research to make audio data suitable for analysis is to convert the recordings into written transcripts and then work only with the text data. Transcription removes the direct voice signal that could reveal the speaker’s identity. |
| |
| ---- | ---- |
| ++++ | ++++ |
| ++++ Acoustic de-identification | | ++++ Acoustic de-identification | |
| If the research extends beyond the textual content and transcript analysis alone is insufficient, additional de-identification measures may be required. In such cases, parts of the audio data can be modified to protect identity of your participants. For audio recordings, editing software such as Audacity can be used to alter or distort voices or to mask personal information (e.g., by muting or inserting bleeps). Be aware that applying these techniques can be time consuming and can also heavily impact the usability of the data. | If the research extends beyond the textual content and transcript analysis alone is insufficient, additional de-identification measures may be considered. In such cases, parts of the audio data can be modified to protect the identity of your participants. For audio recordings, editing software such as [[https://nl.wikipedia.org/wiki/Audacity|Audacity]] can be used to alter or distort voices or to mask personal information (e.g., by muting or inserting bleeps). Be aware that applying these techniques can be time consuming and can also heavily impact the usability of the data. |
| ++++ | ++++ |
| |
| * Network identifiers (e.g. IP addresses) | * Network identifiers (e.g. IP addresses) |
| * Device or user IDs (e.g. serial numbers, or account IDs) | * Device or user IDs (e.g. serial numbers, or account IDs) |
| |
| ++++ | ++++ |
| |
| | ---- |
| | [[dcc:pdpsol:start | → Go back to the Privacy & Data protection home page]] |