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
Identifying and anonymising privacy-sensitive information in information objects (in many cases documents).
Last change on 11th of May 2026, at 12:00 (CET) | Publication Standard 1.0
- Publication category
- Other algorithms
- Impact assessment
- DPIA
- Status
- In use
General information
Theme
Organisation and business operations
Begin date
05-2026
Contact information
algoritmeregister@arnhem.nl
Link to publication website
opendata.arnhem.nl
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.
Human intervention
The algorithm results only act as a tool, with human intervention always needed for final anonymisation.
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
Links to legal bases
- Open Government Act (WOO): https://wetten.overheid.nl/BWBR0045754/
- Electronic Publications Act (WEP): https://wetten.overheid.nl/BWBR0043961/
Elaboration on impact assessments
The municipality of Arnhem conducted a DPIA on e-Data anonymisation
Impact assessment
Data Protection Impact Assessment (DPIA)
Operations
Data
Spatial plans and internal documents.
Links to data sources
Spatial plans and internal documents: https://www.ruimtelijkeplannen.nl/home
Technical design
Deep learning models that determine in both visual and textual ways what information is considered privacy-sensitive.
External provider
eData B.V.
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