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.
Anonymisation software
- Publication category
- Other algorithms
- Impact assessment
- Field not filled in.
- Status
- In use
General information
Theme
Begin date
Contact information
Responsible use
Goal and impact
Support in the review process where legal protection applies to information disclosed. Protection from the AVG (persons) and Woo legislation (especially business confidentiality), where exceptions are named in
Considerations
An advantage of using this algorithm is that (personal) data can be anonymised more efficiently and effectively. That is, anonymisation happens faster and more accurately compared to a completely manual process. As a result, a citizen gets a faster response to the submitted Woo request.
Human intervention
Within the software, a list is built and offered to the user to select in the automatic varnishing process. The choice to adopt an advised term as a person name and not to disclose it is up to the user.
Risk management
There is no risk of automated decision-making and the algorithm has no impact on fundamental rights because the algorithm does not make decisions with legal consequences. It only makes a proposal for anonymising personal data. The employee of the administrative body always makes the final check whether a document has been correctly anonymised.
Legal basis
Legislation around public access to government data (Woo)
Links to legal bases
Operations
Data
This depends on the document being anonymised. Examples include personal data such as e-mail addresses, phone numbers, bank account numbers, address details and signatures. And based on the Open Government Act (Woo), it can also involve data beyond personal data. These grounds for exception are listed in the Woo.
Technical design
Texts are recognised on the basis of Named Entity Recognition (NER) and a process within Insights extracts the names for further processing towards the management interface and the automatic lacquer rules.
Similar algorithm descriptions
- Among other things, the algorithm identifies and anonymises (personal) data and confidential (financial) information in documents before they are published, as required by the Open Government Act.Last change on 16th of May 2024, at 11:52 (CET) | Publication Standard 1.0
- Publication category
- Other algorithms
- Impact assessment
- Field not filled in.
- Status
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- Among other things, the algorithm recognises and anonymises (personal) data and confidential (financial) data in documents before they are published, e.g. on the basis of the Open Government Act.Last change on 5th of September 2024, at 14:30 (CET) | Publication Standard 1.0
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- Impact assessment
- DPIA
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- Among other things, the algorithm recognises and anonymises (personal) data and confidential (financial) data in documents before they are published, e.g. on the basis of the Open Government Act.Last change on 8th of April 2024, at 17:05 (CET) | Publication Standard 1.0
- Publication category
- Other algorithms
- Impact assessment
- Field not filled in.
- Status
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- Among other things, the algorithm recognises and anonymises (personal) data and confidential (financial) data in documents before they are published, e.g. on the basis of the Open Government Act.Last change on 4th of April 2024, at 9:22 (CET) | Publication Standard 1.0
- Publication category
- Other algorithms
- Impact assessment
- Field not filled in.
- Status
- In use
- Among other things, the algorithm recognises and anonymises (personal) data and confidential (financial) data in documents before they are published, e.g. on the basis of the Open Government Act.Last change on 8th of April 2024, at 17:15 (CET) | Publication Standard 1.0
- Publication category
- Other algorithms
- Impact assessment
- Field not filled in.
- Status
- In use