Reidentifiability metric. This corresponds to a risk model similar to
what is called "journalist risk" in the literature, except the attack
dataset is statistically modeled instead of being perfectly known. This
can be done using publicly available data (like the US Census), or using
a custom statistical model (indicated as one or several BigQuery
tables), or by extrapolating from the distribution of values in the
input dataset. A column with a semantic tag attached.
ISO 3166-1 alpha-2 region code to use in the statistical
modeling. Required if no column is tagged with a region-
specific InfoType (like US_ZIP_5) or a region code.
An auxiliary table contains statistical information on the relative
frequency of different quasi-identifiers values. It has one or several
quasi-identifiers columns, and one column that indicates the relative
frequency of each quasi-identifier tuple. If a tuple is present in the
data but not in the auxiliary table, the corresponding relative
frequency is assumed to be zero (and thus, the tuple is highly
reidentifiable).
Quasi-identifier columns. [required]
TaggedField
API documentation for dlp_v2.types.PrivacyMetric.KMapEstimationConfig.TaggedField class.