What is it?
The removal of an entire part of data (also referred to as “column” in databases and spreadsheets) in a dataset. This is the strongest type of anonymisation technique, because there is no way of recovering any information from such an attribute.
When to use it?
When an attribute is not required in the anonymised dataset.
The dataset consists of student's name, tutor's name, and student's test score. The researcher only needs to anlayse student's test score with respect to their various tutors, but without analysis on the students themselves. Hence, the "Student" attribute was removed.
Before anonymisation:
| Student | Tutor | Test Score |
|---|---|---|
| John | Teddy | 87 |
| Stella | Teddy | 56 |
| Ming | Teddy | 92 |
| Poh | Song | 83 |
| Jake | Song | 67 |
| Yong | Song | 45 |
After anonymisation:
| Tutor | Test Score |
|---|---|
| Teddy | 87 |
| Teddy | 56 |
| Teddy | 92 |
| Song | 83 |
| Song | 67 |
| Song | 45 |
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