PrivacyMetric(mapping=None, *, ignore_unknown_fields=False, **kwargs)Privacy metric to compute for reidentification risk analysis.
This message has oneof_ fields (mutually exclusive fields).
For each oneof, at most one member field can be set at the same time.
Setting any member of the oneof automatically clears all other
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.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields
Attributes | |
|---|---|
| Name | Description |
numerical_stats_config |
google.cloud.dlp_v2.types.PrivacyMetric.NumericalStatsConfig
Numerical stats This field is a member of oneof_ type.
|
categorical_stats_config |
google.cloud.dlp_v2.types.PrivacyMetric.CategoricalStatsConfig
Categorical stats This field is a member of oneof_ type.
|
k_anonymity_config |
google.cloud.dlp_v2.types.PrivacyMetric.KAnonymityConfig
K-anonymity This field is a member of oneof_ type.
|
l_diversity_config |
google.cloud.dlp_v2.types.PrivacyMetric.LDiversityConfig
l-diversity This field is a member of oneof_ type.
|
k_map_estimation_config |
google.cloud.dlp_v2.types.PrivacyMetric.KMapEstimationConfig
k-map This field is a member of oneof_ type.
|
delta_presence_estimation_config |
google.cloud.dlp_v2.types.PrivacyMetric.DeltaPresenceEstimationConfig
delta-presence This field is a member of oneof_ type.
|
Classes
CategoricalStatsConfig
CategoricalStatsConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)Compute numerical stats over an individual column, including number of distinct values and value count distribution.
DeltaPresenceEstimationConfig
DeltaPresenceEstimationConfig(
mapping=None, *, ignore_unknown_fields=False, **kwargs
)δ-presence metric, used to estimate how likely it is for an attacker to figure out that one given individual appears in a de-identified dataset. Similarly to the k-map metric, we cannot compute δ-presence exactly without knowing the attack dataset, so we use a statistical model instead.
KAnonymityConfig
KAnonymityConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)k-anonymity metric, used for analysis of reidentification risk.
KMapEstimationConfig
KMapEstimationConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)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.
LDiversityConfig
LDiversityConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)l-diversity metric, used for analysis of reidentification risk.
NumericalStatsConfig
NumericalStatsConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)Compute numerical stats over an individual column, including min, max, and quantiles.