What it means
Effectiveness depends on the data, external information, attacker capability, context, and governance. Removing names alone rarely eliminates re-identification risk.
Why it matters
These techniques can reduce exposure and support analysis, but overstating their protection can create false confidence and inappropriate data sharing.
Practical focus
- Model realistic re-identification paths
- Separate and protect re-linking information
- Reduce quasi-identifiers and unnecessary detail
- Reassess as data sets and external sources change
Common mistakes
- Calling tokenized data anonymous
- Ignoring linkage across multiple data sets
- Using the same pseudonym everywhere
- Treating anonymization as irreversible without evidence
Certification relevance
This subject appears in or supports the following certification bodies of knowledge: