Please note: The algorithm descriptions in English have been automatically translated. Errors may have been introduced in this process. For the original descriptions, go to the Dutch version of the Algorithm Register.
Anonymise
- Publication category
- Other algorithms
- Impact assessment
- Field not filled in.
- Status
- In use
General information
Theme
Begin date
Contact information
Link to publication website
Responsible use
Goal and impact
The purpose of the algorithm is to anonymise privacy-sensitive information in information objects. This protects personal information and preserves the privacy of individuals.
Considerations
The algorithm is trained to recognise privacy-sensitive information, but is limited to classification and will therefore never reveal substantive information. Moreover, the technology helps improve the quality of anonymisation and is many times more efficient than manual anonymisation.
Human intervention
The algorithm results only act as a tool, with human intervention always needed for final anonymisation. Samples are usually taken for this purpose.
Risk management
To safeguard the privacy risks of the algorithm, it undergoes constant evaluation and updates to address new threats and privacy challenges. Human oversight and intervention are embedded to correct errors. Moreover, there is continuous dialogue with stakeholders.
Legal basis
Open Government Act
Electronic Publications Act
AVG
Environment Act
Wmebv
Links to legal bases
- Wet open overheid (WOO): https://wetten.overheid.nl/BWBR0045754/
- Wet elektronische publicaties (WEP): https://wetten.overheid.nl/BWBR0043961/
- AVG: https://www.autoriteitpersoonsgegevens.nl/themas/basis-avg/avg-algemeen/de-avg-in-het-kort
- Omgevingswet: https://wetten.overheid.nl/BWBR0037885/2024-01-01
- Wembv: https://www.digitaleoverheid.nl/overzicht-van-alle-onderwerpen/wetgeving/wet-modernisering-elektronisch-bestuurlijk-verkeer/wat-doet-de-wet-modernisering-elektronisch-bestuurlijk-verkeer/
Operations
Data
Spatial plans and internal documents.
Links to data sources
Technical design
Deep learning models that determine in both visual and textual ways what information is considered privacy-sensitive.
External provider
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