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 15th of July 2026, at 12:28 (CET) | Publication Standard 1.0
- Publication category
- Other algorithms
- Impact assessment
- Field not filled in.
- Status
- In use
General information
Theme
Organisation and business operations
Begin date
2026-01
Contact information
gemeente@hellendoorn.nl
Responsible use
Goal and impact
The aim of the algorithm is to anonymise privacy-sensitive information contained in data objects. This protects personal information and safeguards individuals’ privacy.
Considerations
The algorithm has been trained to recognise privacy-sensitive information, but is limited to classification and will therefore never reveal any substantive information. Furthermore, the technology helps to improve the quality of anonymisation.
Human intervention
The results produced by the algorithm serve merely as a tool; human intervention is always required for the final anonymisation.
Risk management
To mitigate the privacy risks posed by the algorithm, it undergoes constant evaluation and updates to address new threats and privacy challenges. Human oversight and intervention are built into the system to correct errors. Furthermore, there is an ongoing 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/
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, on the basis of both visual and textual data, which information is considered privacy-sensitive.
External provider
eData / eSpecialists
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