Class: Google::Privacy::Dlp::V2::PrivacyMetric
- Inherits:
-
Object
- Object
- Google::Privacy::Dlp::V2::PrivacyMetric
- Defined in:
- lib/google/cloud/dlp/v2/doc/google/privacy/dlp/v2/dlp.rb
Overview
Privacy metric to compute for reidentification risk analysis.
Defined Under Namespace
Classes: CategoricalStatsConfig, DeltaPresenceEstimationConfig, KAnonymityConfig, KMapEstimationConfig, LDiversityConfig, NumericalStatsConfig
Instance Attribute Summary collapse
- #categorical_stats_config ⇒ Google::Privacy::Dlp::V2::PrivacyMetric::CategoricalStatsConfig
- #delta_presence_estimation_config ⇒ Google::Privacy::Dlp::V2::PrivacyMetric::DeltaPresenceEstimationConfig
- #k_anonymity_config ⇒ Google::Privacy::Dlp::V2::PrivacyMetric::KAnonymityConfig
- #k_map_estimation_config ⇒ Google::Privacy::Dlp::V2::PrivacyMetric::KMapEstimationConfig
- #l_diversity_config ⇒ Google::Privacy::Dlp::V2::PrivacyMetric::LDiversityConfig
- #numerical_stats_config ⇒ Google::Privacy::Dlp::V2::PrivacyMetric::NumericalStatsConfig
Instance Attribute Details
#categorical_stats_config ⇒ Google::Privacy::Dlp::V2::PrivacyMetric::CategoricalStatsConfig
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# File 'lib/google/cloud/dlp/v2/doc/google/privacy/dlp/v2/dlp.rb', line 658 class PrivacyMetric # Compute numerical stats over an individual column, including # min, max, and quantiles. # @!attribute [rw] field # @return [Google::Privacy::Dlp::V2::FieldId] # Field to compute numerical stats on. Supported types are # integer, float, date, datetime, timestamp, time. class NumericalStatsConfig; end # Compute numerical stats over an individual column, including # number of distinct values and value count distribution. # @!attribute [rw] field # @return [Google::Privacy::Dlp::V2::FieldId] # Field to compute categorical stats on. All column types are # supported except for arrays and structs. However, it may be more # informative to use NumericalStats when the field type is supported, # depending on the data. class CategoricalStatsConfig; end # k-anonymity metric, used for analysis of reidentification risk. # @!attribute [rw] quasi_ids # @return [Array<Google::Privacy::Dlp::V2::FieldId>] # Set of fields to compute k-anonymity over. When multiple fields are # specified, they are considered a single composite key. Structs and # repeated data types are not supported; however, nested fields are # supported so long as they are not structs themselves or nested within # a repeated field. # @!attribute [rw] entity_id # @return [Google::Privacy::Dlp::V2::EntityId] # Optional message indicating that multiple rows might be associated to a # single individual. If the same entity_id is associated to multiple # quasi-identifier tuples over distict rows, we consider the entire # collection of tuples as the composite quasi-identifier. This collection # is a multiset: the order in which the different tuples appear in the # dataset is ignored, but their frequency is taken into account. # # Important note: a maximum of 1000 rows can be associated to a single # entity ID. If more rows are associated with the same entity ID, some # might be ignored. class KAnonymityConfig; end # l-diversity metric, used for analysis of reidentification risk. # @!attribute [rw] quasi_ids # @return [Array<Google::Privacy::Dlp::V2::FieldId>] # Set of quasi-identifiers indicating how equivalence classes are # defined for the l-diversity computation. When multiple fields are # specified, they are considered a single composite key. # @!attribute [rw] sensitive_attribute # @return [Google::Privacy::Dlp::V2::FieldId] # Sensitive field for computing the l-value. class LDiversityConfig; end # 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. # @!attribute [rw] quasi_ids # @return [Array<Google::Privacy::Dlp::V2::PrivacyMetric::KMapEstimationConfig::TaggedField>] # Fields considered to be quasi-identifiers. No two columns can have the # same tag. [required] # @!attribute [rw] region_code # @return [String] # 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. # @!attribute [rw] auxiliary_tables # @return [Array<Google::Privacy::Dlp::V2::PrivacyMetric::KMapEstimationConfig::AuxiliaryTable>] # Several auxiliary tables can be used in the analysis. Each custom_tag # used to tag a quasi-identifiers column must appear in exactly one column # of one auxiliary table. class KMapEstimationConfig # @!attribute [rw] field # @return [Google::Privacy::Dlp::V2::FieldId] # Identifies the column. [required] # @!attribute [rw] info_type # @return [Google::Privacy::Dlp::V2::InfoType] # A column can be tagged with a InfoType to use the relevant public # dataset as a statistical model of population, if available. We # currently support US ZIP codes, region codes, ages and genders. # To programmatically obtain the list of supported InfoTypes, use # ListInfoTypes with the supported_by=RISK_ANALYSIS filter. # @!attribute [rw] custom_tag # @return [String] # A column can be tagged with a custom tag. In this case, the user must # indicate an auxiliary table that contains statistical information on # the possible values of this column (below). # @!attribute [rw] inferred # @return [Google::Protobuf::Empty] # If no semantic tag is indicated, we infer the statistical model from # the distribution of values in the input data class TaggedField; end # 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). # @!attribute [rw] table # @return [Google::Privacy::Dlp::V2::BigQueryTable] # Auxiliary table location. [required] # @!attribute [rw] quasi_ids # @return [Array<Google::Privacy::Dlp::V2::PrivacyMetric::KMapEstimationConfig::AuxiliaryTable::QuasiIdField>] # Quasi-identifier columns. [required] # @!attribute [rw] relative_frequency # @return [Google::Privacy::Dlp::V2::FieldId] # The relative frequency column must contain a floating-point number # between 0 and 1 (inclusive). Null values are assumed to be zero. # [required] class AuxiliaryTable # A quasi-identifier column has a custom_tag, used to know which column # in the data corresponds to which column in the statistical model. # @!attribute [rw] field # @return [Google::Privacy::Dlp::V2::FieldId] # @!attribute [rw] custom_tag # @return [String] class QuasiIdField; end end end # 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. # @!attribute [rw] quasi_ids # @return [Array<Google::Privacy::Dlp::V2::QuasiId>] # Fields considered to be quasi-identifiers. No two fields can have the # same tag. [required] # @!attribute [rw] region_code # @return [String] # 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. # @!attribute [rw] auxiliary_tables # @return [Array<Google::Privacy::Dlp::V2::StatisticalTable>] # Several auxiliary tables can be used in the analysis. Each custom_tag # used to tag a quasi-identifiers field must appear in exactly one # field of one auxiliary table. class DeltaPresenceEstimationConfig; end end |
#delta_presence_estimation_config ⇒ Google::Privacy::Dlp::V2::PrivacyMetric::DeltaPresenceEstimationConfig
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# File 'lib/google/cloud/dlp/v2/doc/google/privacy/dlp/v2/dlp.rb', line 658 class PrivacyMetric # Compute numerical stats over an individual column, including # min, max, and quantiles. # @!attribute [rw] field # @return [Google::Privacy::Dlp::V2::FieldId] # Field to compute numerical stats on. Supported types are # integer, float, date, datetime, timestamp, time. class NumericalStatsConfig; end # Compute numerical stats over an individual column, including # number of distinct values and value count distribution. # @!attribute [rw] field # @return [Google::Privacy::Dlp::V2::FieldId] # Field to compute categorical stats on. All column types are # supported except for arrays and structs. However, it may be more # informative to use NumericalStats when the field type is supported, # depending on the data. class CategoricalStatsConfig; end # k-anonymity metric, used for analysis of reidentification risk. # @!attribute [rw] quasi_ids # @return [Array<Google::Privacy::Dlp::V2::FieldId>] # Set of fields to compute k-anonymity over. When multiple fields are # specified, they are considered a single composite key. Structs and # repeated data types are not supported; however, nested fields are # supported so long as they are not structs themselves or nested within # a repeated field. # @!attribute [rw] entity_id # @return [Google::Privacy::Dlp::V2::EntityId] # Optional message indicating that multiple rows might be associated to a # single individual. If the same entity_id is associated to multiple # quasi-identifier tuples over distict rows, we consider the entire # collection of tuples as the composite quasi-identifier. This collection # is a multiset: the order in which the different tuples appear in the # dataset is ignored, but their frequency is taken into account. # # Important note: a maximum of 1000 rows can be associated to a single # entity ID. If more rows are associated with the same entity ID, some # might be ignored. class KAnonymityConfig; end # l-diversity metric, used for analysis of reidentification risk. # @!attribute [rw] quasi_ids # @return [Array<Google::Privacy::Dlp::V2::FieldId>] # Set of quasi-identifiers indicating how equivalence classes are # defined for the l-diversity computation. When multiple fields are # specified, they are considered a single composite key. # @!attribute [rw] sensitive_attribute # @return [Google::Privacy::Dlp::V2::FieldId] # Sensitive field for computing the l-value. class LDiversityConfig; end # 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. # @!attribute [rw] quasi_ids # @return [Array<Google::Privacy::Dlp::V2::PrivacyMetric::KMapEstimationConfig::TaggedField>] # Fields considered to be quasi-identifiers. No two columns can have the # same tag. [required] # @!attribute [rw] region_code # @return [String] # 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. # @!attribute [rw] auxiliary_tables # @return [Array<Google::Privacy::Dlp::V2::PrivacyMetric::KMapEstimationConfig::AuxiliaryTable>] # Several auxiliary tables can be used in the analysis. Each custom_tag # used to tag a quasi-identifiers column must appear in exactly one column # of one auxiliary table. class KMapEstimationConfig # @!attribute [rw] field # @return [Google::Privacy::Dlp::V2::FieldId] # Identifies the column. [required] # @!attribute [rw] info_type # @return [Google::Privacy::Dlp::V2::InfoType] # A column can be tagged with a InfoType to use the relevant public # dataset as a statistical model of population, if available. We # currently support US ZIP codes, region codes, ages and genders. # To programmatically obtain the list of supported InfoTypes, use # ListInfoTypes with the supported_by=RISK_ANALYSIS filter. # @!attribute [rw] custom_tag # @return [String] # A column can be tagged with a custom tag. In this case, the user must # indicate an auxiliary table that contains statistical information on # the possible values of this column (below). # @!attribute [rw] inferred # @return [Google::Protobuf::Empty] # If no semantic tag is indicated, we infer the statistical model from # the distribution of values in the input data class TaggedField; end # 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). # @!attribute [rw] table # @return [Google::Privacy::Dlp::V2::BigQueryTable] # Auxiliary table location. [required] # @!attribute [rw] quasi_ids # @return [Array<Google::Privacy::Dlp::V2::PrivacyMetric::KMapEstimationConfig::AuxiliaryTable::QuasiIdField>] # Quasi-identifier columns. [required] # @!attribute [rw] relative_frequency # @return [Google::Privacy::Dlp::V2::FieldId] # The relative frequency column must contain a floating-point number # between 0 and 1 (inclusive). Null values are assumed to be zero. # [required] class AuxiliaryTable # A quasi-identifier column has a custom_tag, used to know which column # in the data corresponds to which column in the statistical model. # @!attribute [rw] field # @return [Google::Privacy::Dlp::V2::FieldId] # @!attribute [rw] custom_tag # @return [String] class QuasiIdField; end end end # 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. # @!attribute [rw] quasi_ids # @return [Array<Google::Privacy::Dlp::V2::QuasiId>] # Fields considered to be quasi-identifiers. No two fields can have the # same tag. [required] # @!attribute [rw] region_code # @return [String] # 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. # @!attribute [rw] auxiliary_tables # @return [Array<Google::Privacy::Dlp::V2::StatisticalTable>] # Several auxiliary tables can be used in the analysis. Each custom_tag # used to tag a quasi-identifiers field must appear in exactly one # field of one auxiliary table. class DeltaPresenceEstimationConfig; end end |
#k_anonymity_config ⇒ Google::Privacy::Dlp::V2::PrivacyMetric::KAnonymityConfig
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# File 'lib/google/cloud/dlp/v2/doc/google/privacy/dlp/v2/dlp.rb', line 658 class PrivacyMetric # Compute numerical stats over an individual column, including # min, max, and quantiles. # @!attribute [rw] field # @return [Google::Privacy::Dlp::V2::FieldId] # Field to compute numerical stats on. Supported types are # integer, float, date, datetime, timestamp, time. class NumericalStatsConfig; end # Compute numerical stats over an individual column, including # number of distinct values and value count distribution. # @!attribute [rw] field # @return [Google::Privacy::Dlp::V2::FieldId] # Field to compute categorical stats on. All column types are # supported except for arrays and structs. However, it may be more # informative to use NumericalStats when the field type is supported, # depending on the data. class CategoricalStatsConfig; end # k-anonymity metric, used for analysis of reidentification risk. # @!attribute [rw] quasi_ids # @return [Array<Google::Privacy::Dlp::V2::FieldId>] # Set of fields to compute k-anonymity over. When multiple fields are # specified, they are considered a single composite key. Structs and # repeated data types are not supported; however, nested fields are # supported so long as they are not structs themselves or nested within # a repeated field. # @!attribute [rw] entity_id # @return [Google::Privacy::Dlp::V2::EntityId] # Optional message indicating that multiple rows might be associated to a # single individual. If the same entity_id is associated to multiple # quasi-identifier tuples over distict rows, we consider the entire # collection of tuples as the composite quasi-identifier. This collection # is a multiset: the order in which the different tuples appear in the # dataset is ignored, but their frequency is taken into account. # # Important note: a maximum of 1000 rows can be associated to a single # entity ID. If more rows are associated with the same entity ID, some # might be ignored. class KAnonymityConfig; end # l-diversity metric, used for analysis of reidentification risk. # @!attribute [rw] quasi_ids # @return [Array<Google::Privacy::Dlp::V2::FieldId>] # Set of quasi-identifiers indicating how equivalence classes are # defined for the l-diversity computation. When multiple fields are # specified, they are considered a single composite key. # @!attribute [rw] sensitive_attribute # @return [Google::Privacy::Dlp::V2::FieldId] # Sensitive field for computing the l-value. class LDiversityConfig; end # 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. # @!attribute [rw] quasi_ids # @return [Array<Google::Privacy::Dlp::V2::PrivacyMetric::KMapEstimationConfig::TaggedField>] # Fields considered to be quasi-identifiers. No two columns can have the # same tag. [required] # @!attribute [rw] region_code # @return [String] # 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. # @!attribute [rw] auxiliary_tables # @return [Array<Google::Privacy::Dlp::V2::PrivacyMetric::KMapEstimationConfig::AuxiliaryTable>] # Several auxiliary tables can be used in the analysis. Each custom_tag # used to tag a quasi-identifiers column must appear in exactly one column # of one auxiliary table. class KMapEstimationConfig # @!attribute [rw] field # @return [Google::Privacy::Dlp::V2::FieldId] # Identifies the column. [required] # @!attribute [rw] info_type # @return [Google::Privacy::Dlp::V2::InfoType] # A column can be tagged with a InfoType to use the relevant public # dataset as a statistical model of population, if available. We # currently support US ZIP codes, region codes, ages and genders. # To programmatically obtain the list of supported InfoTypes, use # ListInfoTypes with the supported_by=RISK_ANALYSIS filter. # @!attribute [rw] custom_tag # @return [String] # A column can be tagged with a custom tag. In this case, the user must # indicate an auxiliary table that contains statistical information on # the possible values of this column (below). # @!attribute [rw] inferred # @return [Google::Protobuf::Empty] # If no semantic tag is indicated, we infer the statistical model from # the distribution of values in the input data class TaggedField; end # 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). # @!attribute [rw] table # @return [Google::Privacy::Dlp::V2::BigQueryTable] # Auxiliary table location. [required] # @!attribute [rw] quasi_ids # @return [Array<Google::Privacy::Dlp::V2::PrivacyMetric::KMapEstimationConfig::AuxiliaryTable::QuasiIdField>] # Quasi-identifier columns. [required] # @!attribute [rw] relative_frequency # @return [Google::Privacy::Dlp::V2::FieldId] # The relative frequency column must contain a floating-point number # between 0 and 1 (inclusive). Null values are assumed to be zero. # [required] class AuxiliaryTable # A quasi-identifier column has a custom_tag, used to know which column # in the data corresponds to which column in the statistical model. # @!attribute [rw] field # @return [Google::Privacy::Dlp::V2::FieldId] # @!attribute [rw] custom_tag # @return [String] class QuasiIdField; end end end # 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. # @!attribute [rw] quasi_ids # @return [Array<Google::Privacy::Dlp::V2::QuasiId>] # Fields considered to be quasi-identifiers. No two fields can have the # same tag. [required] # @!attribute [rw] region_code # @return [String] # 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. # @!attribute [rw] auxiliary_tables # @return [Array<Google::Privacy::Dlp::V2::StatisticalTable>] # Several auxiliary tables can be used in the analysis. Each custom_tag # used to tag a quasi-identifiers field must appear in exactly one # field of one auxiliary table. class DeltaPresenceEstimationConfig; end end |
#k_map_estimation_config ⇒ Google::Privacy::Dlp::V2::PrivacyMetric::KMapEstimationConfig
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# File 'lib/google/cloud/dlp/v2/doc/google/privacy/dlp/v2/dlp.rb', line 658 class PrivacyMetric # Compute numerical stats over an individual column, including # min, max, and quantiles. # @!attribute [rw] field # @return [Google::Privacy::Dlp::V2::FieldId] # Field to compute numerical stats on. Supported types are # integer, float, date, datetime, timestamp, time. class NumericalStatsConfig; end # Compute numerical stats over an individual column, including # number of distinct values and value count distribution. # @!attribute [rw] field # @return [Google::Privacy::Dlp::V2::FieldId] # Field to compute categorical stats on. All column types are # supported except for arrays and structs. However, it may be more # informative to use NumericalStats when the field type is supported, # depending on the data. class CategoricalStatsConfig; end # k-anonymity metric, used for analysis of reidentification risk. # @!attribute [rw] quasi_ids # @return [Array<Google::Privacy::Dlp::V2::FieldId>] # Set of fields to compute k-anonymity over. When multiple fields are # specified, they are considered a single composite key. Structs and # repeated data types are not supported; however, nested fields are # supported so long as they are not structs themselves or nested within # a repeated field. # @!attribute [rw] entity_id # @return [Google::Privacy::Dlp::V2::EntityId] # Optional message indicating that multiple rows might be associated to a # single individual. If the same entity_id is associated to multiple # quasi-identifier tuples over distict rows, we consider the entire # collection of tuples as the composite quasi-identifier. This collection # is a multiset: the order in which the different tuples appear in the # dataset is ignored, but their frequency is taken into account. # # Important note: a maximum of 1000 rows can be associated to a single # entity ID. If more rows are associated with the same entity ID, some # might be ignored. class KAnonymityConfig; end # l-diversity metric, used for analysis of reidentification risk. # @!attribute [rw] quasi_ids # @return [Array<Google::Privacy::Dlp::V2::FieldId>] # Set of quasi-identifiers indicating how equivalence classes are # defined for the l-diversity computation. When multiple fields are # specified, they are considered a single composite key. # @!attribute [rw] sensitive_attribute # @return [Google::Privacy::Dlp::V2::FieldId] # Sensitive field for computing the l-value. class LDiversityConfig; end # 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. # @!attribute [rw] quasi_ids # @return [Array<Google::Privacy::Dlp::V2::PrivacyMetric::KMapEstimationConfig::TaggedField>] # Fields considered to be quasi-identifiers. No two columns can have the # same tag. [required] # @!attribute [rw] region_code # @return [String] # 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. # @!attribute [rw] auxiliary_tables # @return [Array<Google::Privacy::Dlp::V2::PrivacyMetric::KMapEstimationConfig::AuxiliaryTable>] # Several auxiliary tables can be used in the analysis. Each custom_tag # used to tag a quasi-identifiers column must appear in exactly one column # of one auxiliary table. class KMapEstimationConfig # @!attribute [rw] field # @return [Google::Privacy::Dlp::V2::FieldId] # Identifies the column. [required] # @!attribute [rw] info_type # @return [Google::Privacy::Dlp::V2::InfoType] # A column can be tagged with a InfoType to use the relevant public # dataset as a statistical model of population, if available. We # currently support US ZIP codes, region codes, ages and genders. # To programmatically obtain the list of supported InfoTypes, use # ListInfoTypes with the supported_by=RISK_ANALYSIS filter. # @!attribute [rw] custom_tag # @return [String] # A column can be tagged with a custom tag. In this case, the user must # indicate an auxiliary table that contains statistical information on # the possible values of this column (below). # @!attribute [rw] inferred # @return [Google::Protobuf::Empty] # If no semantic tag is indicated, we infer the statistical model from # the distribution of values in the input data class TaggedField; end # 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). # @!attribute [rw] table # @return [Google::Privacy::Dlp::V2::BigQueryTable] # Auxiliary table location. [required] # @!attribute [rw] quasi_ids # @return [Array<Google::Privacy::Dlp::V2::PrivacyMetric::KMapEstimationConfig::AuxiliaryTable::QuasiIdField>] # Quasi-identifier columns. [required] # @!attribute [rw] relative_frequency # @return [Google::Privacy::Dlp::V2::FieldId] # The relative frequency column must contain a floating-point number # between 0 and 1 (inclusive). Null values are assumed to be zero. # [required] class AuxiliaryTable # A quasi-identifier column has a custom_tag, used to know which column # in the data corresponds to which column in the statistical model. # @!attribute [rw] field # @return [Google::Privacy::Dlp::V2::FieldId] # @!attribute [rw] custom_tag # @return [String] class QuasiIdField; end end end # 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. # @!attribute [rw] quasi_ids # @return [Array<Google::Privacy::Dlp::V2::QuasiId>] # Fields considered to be quasi-identifiers. No two fields can have the # same tag. [required] # @!attribute [rw] region_code # @return [String] # 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. # @!attribute [rw] auxiliary_tables # @return [Array<Google::Privacy::Dlp::V2::StatisticalTable>] # Several auxiliary tables can be used in the analysis. Each custom_tag # used to tag a quasi-identifiers field must appear in exactly one # field of one auxiliary table. class DeltaPresenceEstimationConfig; end end |
#l_diversity_config ⇒ Google::Privacy::Dlp::V2::PrivacyMetric::LDiversityConfig
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# File 'lib/google/cloud/dlp/v2/doc/google/privacy/dlp/v2/dlp.rb', line 658 class PrivacyMetric # Compute numerical stats over an individual column, including # min, max, and quantiles. # @!attribute [rw] field # @return [Google::Privacy::Dlp::V2::FieldId] # Field to compute numerical stats on. Supported types are # integer, float, date, datetime, timestamp, time. class NumericalStatsConfig; end # Compute numerical stats over an individual column, including # number of distinct values and value count distribution. # @!attribute [rw] field # @return [Google::Privacy::Dlp::V2::FieldId] # Field to compute categorical stats on. All column types are # supported except for arrays and structs. However, it may be more # informative to use NumericalStats when the field type is supported, # depending on the data. class CategoricalStatsConfig; end # k-anonymity metric, used for analysis of reidentification risk. # @!attribute [rw] quasi_ids # @return [Array<Google::Privacy::Dlp::V2::FieldId>] # Set of fields to compute k-anonymity over. When multiple fields are # specified, they are considered a single composite key. Structs and # repeated data types are not supported; however, nested fields are # supported so long as they are not structs themselves or nested within # a repeated field. # @!attribute [rw] entity_id # @return [Google::Privacy::Dlp::V2::EntityId] # Optional message indicating that multiple rows might be associated to a # single individual. If the same entity_id is associated to multiple # quasi-identifier tuples over distict rows, we consider the entire # collection of tuples as the composite quasi-identifier. This collection # is a multiset: the order in which the different tuples appear in the # dataset is ignored, but their frequency is taken into account. # # Important note: a maximum of 1000 rows can be associated to a single # entity ID. If more rows are associated with the same entity ID, some # might be ignored. class KAnonymityConfig; end # l-diversity metric, used for analysis of reidentification risk. # @!attribute [rw] quasi_ids # @return [Array<Google::Privacy::Dlp::V2::FieldId>] # Set of quasi-identifiers indicating how equivalence classes are # defined for the l-diversity computation. When multiple fields are # specified, they are considered a single composite key. # @!attribute [rw] sensitive_attribute # @return [Google::Privacy::Dlp::V2::FieldId] # Sensitive field for computing the l-value. class LDiversityConfig; end # 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. # @!attribute [rw] quasi_ids # @return [Array<Google::Privacy::Dlp::V2::PrivacyMetric::KMapEstimationConfig::TaggedField>] # Fields considered to be quasi-identifiers. No two columns can have the # same tag. [required] # @!attribute [rw] region_code # @return [String] # 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. # @!attribute [rw] auxiliary_tables # @return [Array<Google::Privacy::Dlp::V2::PrivacyMetric::KMapEstimationConfig::AuxiliaryTable>] # Several auxiliary tables can be used in the analysis. Each custom_tag # used to tag a quasi-identifiers column must appear in exactly one column # of one auxiliary table. class KMapEstimationConfig # @!attribute [rw] field # @return [Google::Privacy::Dlp::V2::FieldId] # Identifies the column. [required] # @!attribute [rw] info_type # @return [Google::Privacy::Dlp::V2::InfoType] # A column can be tagged with a InfoType to use the relevant public # dataset as a statistical model of population, if available. We # currently support US ZIP codes, region codes, ages and genders. # To programmatically obtain the list of supported InfoTypes, use # ListInfoTypes with the supported_by=RISK_ANALYSIS filter. # @!attribute [rw] custom_tag # @return [String] # A column can be tagged with a custom tag. In this case, the user must # indicate an auxiliary table that contains statistical information on # the possible values of this column (below). # @!attribute [rw] inferred # @return [Google::Protobuf::Empty] # If no semantic tag is indicated, we infer the statistical model from # the distribution of values in the input data class TaggedField; end # 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). # @!attribute [rw] table # @return [Google::Privacy::Dlp::V2::BigQueryTable] # Auxiliary table location. [required] # @!attribute [rw] quasi_ids # @return [Array<Google::Privacy::Dlp::V2::PrivacyMetric::KMapEstimationConfig::AuxiliaryTable::QuasiIdField>] # Quasi-identifier columns. [required] # @!attribute [rw] relative_frequency # @return [Google::Privacy::Dlp::V2::FieldId] # The relative frequency column must contain a floating-point number # between 0 and 1 (inclusive). Null values are assumed to be zero. # [required] class AuxiliaryTable # A quasi-identifier column has a custom_tag, used to know which column # in the data corresponds to which column in the statistical model. # @!attribute [rw] field # @return [Google::Privacy::Dlp::V2::FieldId] # @!attribute [rw] custom_tag # @return [String] class QuasiIdField; end end end # 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. # @!attribute [rw] quasi_ids # @return [Array<Google::Privacy::Dlp::V2::QuasiId>] # Fields considered to be quasi-identifiers. No two fields can have the # same tag. [required] # @!attribute [rw] region_code # @return [String] # 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. # @!attribute [rw] auxiliary_tables # @return [Array<Google::Privacy::Dlp::V2::StatisticalTable>] # Several auxiliary tables can be used in the analysis. Each custom_tag # used to tag a quasi-identifiers field must appear in exactly one # field of one auxiliary table. class DeltaPresenceEstimationConfig; end end |
#numerical_stats_config ⇒ Google::Privacy::Dlp::V2::PrivacyMetric::NumericalStatsConfig
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# File 'lib/google/cloud/dlp/v2/doc/google/privacy/dlp/v2/dlp.rb', line 658 class PrivacyMetric # Compute numerical stats over an individual column, including # min, max, and quantiles. # @!attribute [rw] field # @return [Google::Privacy::Dlp::V2::FieldId] # Field to compute numerical stats on. Supported types are # integer, float, date, datetime, timestamp, time. class NumericalStatsConfig; end # Compute numerical stats over an individual column, including # number of distinct values and value count distribution. # @!attribute [rw] field # @return [Google::Privacy::Dlp::V2::FieldId] # Field to compute categorical stats on. All column types are # supported except for arrays and structs. However, it may be more # informative to use NumericalStats when the field type is supported, # depending on the data. class CategoricalStatsConfig; end # k-anonymity metric, used for analysis of reidentification risk. # @!attribute [rw] quasi_ids # @return [Array<Google::Privacy::Dlp::V2::FieldId>] # Set of fields to compute k-anonymity over. When multiple fields are # specified, they are considered a single composite key. Structs and # repeated data types are not supported; however, nested fields are # supported so long as they are not structs themselves or nested within # a repeated field. # @!attribute [rw] entity_id # @return [Google::Privacy::Dlp::V2::EntityId] # Optional message indicating that multiple rows might be associated to a # single individual. If the same entity_id is associated to multiple # quasi-identifier tuples over distict rows, we consider the entire # collection of tuples as the composite quasi-identifier. This collection # is a multiset: the order in which the different tuples appear in the # dataset is ignored, but their frequency is taken into account. # # Important note: a maximum of 1000 rows can be associated to a single # entity ID. If more rows are associated with the same entity ID, some # might be ignored. class KAnonymityConfig; end # l-diversity metric, used for analysis of reidentification risk. # @!attribute [rw] quasi_ids # @return [Array<Google::Privacy::Dlp::V2::FieldId>] # Set of quasi-identifiers indicating how equivalence classes are # defined for the l-diversity computation. When multiple fields are # specified, they are considered a single composite key. # @!attribute [rw] sensitive_attribute # @return [Google::Privacy::Dlp::V2::FieldId] # Sensitive field for computing the l-value. class LDiversityConfig; end # 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. # @!attribute [rw] quasi_ids # @return [Array<Google::Privacy::Dlp::V2::PrivacyMetric::KMapEstimationConfig::TaggedField>] # Fields considered to be quasi-identifiers. No two columns can have the # same tag. [required] # @!attribute [rw] region_code # @return [String] # 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. # @!attribute [rw] auxiliary_tables # @return [Array<Google::Privacy::Dlp::V2::PrivacyMetric::KMapEstimationConfig::AuxiliaryTable>] # Several auxiliary tables can be used in the analysis. Each custom_tag # used to tag a quasi-identifiers column must appear in exactly one column # of one auxiliary table. class KMapEstimationConfig # @!attribute [rw] field # @return [Google::Privacy::Dlp::V2::FieldId] # Identifies the column. [required] # @!attribute [rw] info_type # @return [Google::Privacy::Dlp::V2::InfoType] # A column can be tagged with a InfoType to use the relevant public # dataset as a statistical model of population, if available. We # currently support US ZIP codes, region codes, ages and genders. # To programmatically obtain the list of supported InfoTypes, use # ListInfoTypes with the supported_by=RISK_ANALYSIS filter. # @!attribute [rw] custom_tag # @return [String] # A column can be tagged with a custom tag. In this case, the user must # indicate an auxiliary table that contains statistical information on # the possible values of this column (below). # @!attribute [rw] inferred # @return [Google::Protobuf::Empty] # If no semantic tag is indicated, we infer the statistical model from # the distribution of values in the input data class TaggedField; end # 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). # @!attribute [rw] table # @return [Google::Privacy::Dlp::V2::BigQueryTable] # Auxiliary table location. [required] # @!attribute [rw] quasi_ids # @return [Array<Google::Privacy::Dlp::V2::PrivacyMetric::KMapEstimationConfig::AuxiliaryTable::QuasiIdField>] # Quasi-identifier columns. [required] # @!attribute [rw] relative_frequency # @return [Google::Privacy::Dlp::V2::FieldId] # The relative frequency column must contain a floating-point number # between 0 and 1 (inclusive). Null values are assumed to be zero. # [required] class AuxiliaryTable # A quasi-identifier column has a custom_tag, used to know which column # in the data corresponds to which column in the statistical model. # @!attribute [rw] field # @return [Google::Privacy::Dlp::V2::FieldId] # @!attribute [rw] custom_tag # @return [String] class QuasiIdField; end end end # 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. # @!attribute [rw] quasi_ids # @return [Array<Google::Privacy::Dlp::V2::QuasiId>] # Fields considered to be quasi-identifiers. No two fields can have the # same tag. [required] # @!attribute [rw] region_code # @return [String] # 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. # @!attribute [rw] auxiliary_tables # @return [Array<Google::Privacy::Dlp::V2::StatisticalTable>] # Several auxiliary tables can be used in the analysis. Each custom_tag # used to tag a quasi-identifiers field must appear in exactly one # field of one auxiliary table. class DeltaPresenceEstimationConfig; end end |