Overview Anonymization Techniques
Explain the goal of anonymization further
Data anonymization is the process of transforming sensitive data to protect individuals’ privacy(El Emam and Arbuckle 2013). Its primary goal is to remove the association between identifying data and the data subject, making it impossible or very difficult to trace the data back to an individual (El Emam and Arbuckle 2013)
In the next sections of the tutorial, I will present you with several groups of techniques for anonymization, following the taxonomy by Carvalho et al. (2023).
Non-perturbative techniques are those that do not change data values but rather mask them.
Perturbative techniques involve modifying data values to anonymize the data.
De-associative techniques separate specific parts of the data from other parts.
Synthesizing data means creating new data with the same statistical properties as the original data. I will not discuss this method in detail in this tutorial.
Insert graphics
Add info on which risks these techniques mitigate
For each anonymization technique, the variable’s scale level needs to be taken into account. Add relevant scale levels for all anonymization techniques
Explain the structure of the following parts (exercises based on the same data)
Mention to what data this applies
Learning Objective
- After completing this part of the tutorial, you will understand the fundamental principles of anonymization techniques and how they relate to one another.
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Resources, Links, Examples
Carvalho et al. (2023) for detailed information
To Do List
- Create graphics to illustrate each kind of technique