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.
Inheritance
builtins.object > google.protobuf.pyext._message.CMessage > builtins.object > google.protobuf.message.Message > KMapEstimationConfigClasses
AuxiliaryTable
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.