Summary of the Anonymization Workflow

After completing this part of the tutorial, you will understand the anonymization workflow.

This chapter summarizes the full anonymization workflow. Use it as a reference or checklist when you work through your own data.

1
Implement Data Privacy Privacy by design before & during collection: minimize data, plan storage/access, write a DMP.
▼
2
Collect Data & Handle Identifiers Use informed consent and pseudonymize early. Remove direct identifiers from every copy, store the pseudonym key separately, and work from a secure identifier-free copy.
▼
3
Run Analysis Do your analysis, writing, and collaborator sharing on the identifier-free working copy.
▼
4
Analyze Attack Scenarios Consider adversaries and disclosure risks (identity, attribute, inference, membership); set a k-anonymity goal.
▼
5
Calculate Disclosure Risk & Utility Measure k-anonymity and risk metrics; establish a utility baseline (e.g. sdcMicro).
▼
▼
7
Apply Anonymization Techniques Apply chosen techniques step by step; track changes with sdcMicro.
▼
8
Recalculate Risk & Utility Re-measure after each step. If the balance is not satisfactory, ↻ return to Step 4 and iterate.
▼
9
Document the Process Internal (auditing) and external (data dictionary) documentation; review scripts.
▼
10
Publish Data Share anonymized data + documentation; pick repository & access level (fully open is best).
Back to top