Anonymisation tool

Municipality of Laren

Identifying and anonymising (privacy) sensitive information in files (in many cases documents).

Last change on 26th of May 2025, at 14:46 (CET) | Publication Standard 1.0

• Publication category: Other algorithms
• Impact assessment: DPIA
• Status: In use

General information

Theme

Organisation and business operations

Begin date

2025-05

Contact information

[e-mail]

Responsible use

Goal and impact

The anonymisation tool is used to give substance to open government. In doing so, the municipality protects the data of individuals and organisations about whom documents are concerned.

Openness so that the organisation can share this information according to the law such as the Open Government Act (Woo). For the resident about whom there are data in documents to be made public, the use means that there is no breach of privacy. The municipality thus complies with the General Data Protection Regulation (AVG). This also applies to the protection of personal data of the municipality's employees.

The applicant of a Woo request is given the information she asked for. She receives this in a (partly) anonymised version. For the employees handling a Woo request and/or disclosing information, it means they are complying with the law. Use of the algorithm makes anonymisation faster and easier. This allows the municipality to offer the requested information within the legal deadlines.

The risk of the algorithm is low. In particular, the algorithm searches for (personal) data and anonymises it. A proposal is made to anonymise a text fragment to an employee. The algorithm does not make any (automatic) decisions itself. In addition, the algorithm has the option of anonymising information itself that cannot be disclosed for other reasons. With this, for example, a text fragment containing sensitive information of the municipality can be made anonymous to protect the organisation. The reason for anonymisation is mentioned in the box.

Considerations

It happens that text excerpts in documents made public cannot be shared with the public. The Woo gives reasons on the basis of which this is possible. And the General Data Protection Regulation (AVG) is another such reason for not disclosing (part of) documents.

Without the use of the algorithm, anonymising text fragments in documents would take much more time. Using the algorithm makes the process for active and passive disclosure of documents faster and easier. Anonymisation by the algorithm is also less error-prone than just handling by an employee. This reduces the risk of a data breach and better protects the data of the people involved.

Human intervention

A proposal is made by the algorithm to anonymise a text snippet to an employee. No automatic decisions are made. In particular, the algorithm searches for (personal) data to make it anonymous despite document content.

The collaborator handles the suggestions, indicating where they are good and improving where they should be. Also, within the algorithm, the employee's decisions can be checked by a second person.

For the resident, this means that the municipality can show that it is working to eliminate (the risk of) privacy breaches.

Risk management

The algorithm will be checked again and again to see if it is working properly. The algorithm will then be adjusted and/or modified. To avoid errors, a staff member will always make the decision on the proposed text fragments to be anonymised.

Legal basis

Open Government Act (Woo)

Electronic Publications Act (Wep)

General Data Protection Regulation (AVG)

General Administrative Law Act (AWB)

Links to legal bases

• Wet open overheid (WOO): https://wetten.overheid.nl/BWBR0045754/
• Wet elektronische publicaties (WEP): https://wetten.overheid.nl/BWBR0043961/
• Algemene verordening gegevensbescherming (AVG): https://eur-lex.europa.eu/eli/reg/2016/679
• Algemene Wet Bestuursrecht (AWB): https://wetten.overheid.nl/BWBR0005537/2022-11-05

Impact assessment

Data Protection Impact Assessment (DPIA)

Operations

Data

Spatial plans and internal documents.

Links to data sources

Ruimtelijke plannen en interne documenten: 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.