Anonymisation removes all means reasonably likely to be used to identify a natural person from a dataset; if successful the data is outside GDPR scope. The report surveys the main anonymisation models including k-anonymity, l-diversity and t-closeness and warns that anonymisation must be assessed against motivated-intruder and re-identification attacks.
This control maps to 1 controls across 1 other frameworks. If you already hold one of them, the evidence you collected for it is the starting point here rather than new work.
Every mapping shown was judged rather than inferred from wording similarity, and the ones that failed review are published too. See the coverage reports and what was rejected.
The graph holds this control, the 1 it maps to, and the evidence behind each claim, over MCP and REST.