Discovering Suitable Anonymization Techniques: A Privacy Toolbox for Data Experts

Identifying the appropriate anonymization technique is a critical yet challenging task for developers, data scientists, and security practitioners. Our interactive toolbox addresses this challenge by providing a comprehensive overview of available anonymization techniques to assist privacy-conscious developers in selecting the right one for their specific use cases. The toolbox offers a hierarchical and classified overview of techniques, each detailed with meta-model information. It employs a modular approach, allowing techniques to be implemented and deployed independently. Additionally, it enables developers to evaluate these techniques on test datasets. Our toolbox allows for the easy addition of new categories and modules. This paper demonstrates the anonymization toolbox’s capabilities, simplifying the decision-making process in the Anonymization by Design cycle by ensuring overview, modularity, and flexibility.