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. Anonymisation is irreversibly changing personal data so that it cannot be traced back to an individual. Anonymisation protects personal information and safeguards the privacy of individuals.
Considerations
The algorithm helps keep the process of anonymising documents digital and partially automated. Previously, documents had to be printed out first, then personal data deleted. Then the document was scanned and registered again. The algorithm is trained to recognise privacy-sensitive information, but is limited to classifying the information. It will never reveal the content information.
The technology also helps improve the quality of anonymisation.
Human intervention
The results of the algorithm are a tool. An employee checks the results of the algorithm. He can manually modify and approve them. Thus, human intervention is always needed for final anonymisation.
Risk management
To safeguard the algorithm's privacy risks, the algorithm undergoes constant review and updates from the vendor to address new threats and privacy challenges. City staff also monitor the operation of the algorithm and can make adjustments to correct errors.
The supplier engages in constant dialogue with stakeholders of the software.
Legal basis
Links to legal bases
- Wet open overheid (WOO): https://wetten.overheid.nl/BWBR0045754/
- Wet elektronische publicaties (WEP): https://wetten.overheid.nl/BWBR0043961/
Elaboration on impact assessments
A Data Protection Impact Assessment (DPIA) was carried out prior to the implementation of the software.
Operations
Data
For example, internal documents to be disclosed for the purposes of the Open Government Act (WOO) based on a WOO request. Or documents to be published based on the Electronic Publications Act (WEP), such as spatial plans.
Links to data sources
Technical design
Deep learning models that determine which information is considered privacy-sensitive based on images and text in documents. To further develop the algorithm, the vendor uses 'supervised machine learning'. This means that the algorithm is capable of learning and is continuously improved by the vendor.
External provider
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