De-Associative Techniques

Bucketization works by first grouping records into “buckets” based on their indirect identifiers (like age, gender, postal code). Each bucket must contain at least k records to satisfy k-anonymity. Within each bucket, the sensitive values (like income or political opinions) are then randomly shuffled, so the link between a specific person’s indirect identifiers and their sensitive attributes is broken. An attacker might narrow someone down to a bucket, but they cannot tell which sensitive value belongs to whom within that bucket.

Example Technique: Bucketization

  • idea: create QI groups with at least k records

  • stems from the created buckets or partitions

  • 1. step: generalization to create buckets (e..g., countries to continents)

  • 2. step: de-generalize the QI in the created buckets (i.e., continents back to countries)

  • 3. step: permutate sensitive values within each bucket

potentially swap for another technique that is not so confusing with regard to usual perturbative techniques (anatomization)

Learning Objective

  • After completing this part of the tutorial, you will be able to apply selected de-associative techniques in R.

Exercises

  • apply one selected technique to the dataset?
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