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Training on Anonymization of Tabular Health Research Data: Foundations, Concepts and Practical Application

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This interactive online training introduces key principles of health data anonymization and demonstrates how to apply them using the ARX Data Anonymization Tool. Participants will learn how to identify potentially identifying information, understand how identification risks can vary across data-sharing scenarios, and select suitable privacy models and data transformations. Practical exercises will guide participants through the data anonymization process.

The image shows a human hand holding a drawn light bulb and the title of the webinar in large white letters.

After the training, participants will be able to:

  • distinguish between direct identifiers, indirect identifiers, and sensitive attributes,
  • recognize different data-sharing scenarios and understand their implications for identification risks,
  • prepare and configure a tabular dataset for anonymization in ARX,
  • apply privacy models, such as k-anonymity, and use techniques such as generalization and suppression to transform data,
  • assess identification risks and information loss to make informed anonymization decisions.

Target group: Doctoral researchers, early-career researchers, clinical researchers, data analysts, and data stewards
Requirements: No prior experience with ARX is required
Training format: Interactive online training with practical exercises
Speakers: Thierry Meurers, Ivana Nanevski
Date: 26 November 2026
Time: 09:00-12:00 CEST
Language: English

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Registration: [here] 

 

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