Class: Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails

Inherits:

Object

• Object
• Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails

Defined in:

lib/google/cloud/dlp/v2/doc/google/privacy/dlp/v2/dlp.rb

Overview

Result of a risk analysis operation request.

Defined Under Namespace

Classes: CategoricalStatsResult, DeltaPresenceEstimationResult, KAnonymityResult, KMapEstimationResult, LDiversityResult, NumericalStatsResult

Instance Attribute Summary

• #categorical_stats_result ⇒ Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::CategoricalStatsResult
• #delta_presence_estimation_result ⇒ Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::DeltaPresenceEstimationResult
• #k_anonymity_result ⇒ Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::KAnonymityResult
• #k_map_estimation_result ⇒ Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::KMapEstimationResult
• #l_diversity_result ⇒ Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::LDiversityResult
• #numerical_stats_result ⇒ Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::NumericalStatsResult
• #requested_privacy_metric ⇒ Google::Privacy::Dlp::V2::PrivacyMetric

  Privacy metric to compute.

• #requested_source_table ⇒ Google::Privacy::Dlp::V2::BigQueryTable

  Input dataset to compute metrics over.

Instance Attribute Details

#categorical_stats_result ⇒ Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::CategoricalStatsResult

Returns:

• (Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::CategoricalStatsResult)

# File 'lib/google/cloud/dlp/v2/doc/google/privacy/dlp/v2/dlp.rb', line 891

class AnalyzeDataSourceRiskDetails
  # Result of the numerical stats computation.
  # @!attribute [rw] min_value
  #   @return [Google::Privacy::Dlp::V2::Value]
  #     Minimum value appearing in the column.
  # @!attribute [rw] max_value
  #   @return [Google::Privacy::Dlp::V2::Value]
  #     Maximum value appearing in the column.
  # @!attribute [rw] quantile_values
  #   @return [Array<Google::Privacy::Dlp::V2::Value>]
  #     List of 99 values that partition the set of field values into 100 equal
  #     sized buckets.
  class NumericalStatsResult; end

  # Result of the categorical stats computation.
  # @!attribute [rw] value_frequency_histogram_buckets
  #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::CategoricalStatsResult::CategoricalStatsHistogramBucket>]
  #     Histogram of value frequencies in the column.
  class CategoricalStatsResult
    # @!attribute [rw] value_frequency_lower_bound
    #   @return [Integer]
    #     Lower bound on the value frequency of the values in this bucket.
    # @!attribute [rw] value_frequency_upper_bound
    #   @return [Integer]
    #     Upper bound on the value frequency of the values in this bucket.
    # @!attribute [rw] bucket_size
    #   @return [Integer]
    #     Total number of values in this bucket.
    # @!attribute [rw] bucket_values
    #   @return [Array<Google::Privacy::Dlp::V2::ValueFrequency>]
    #     Sample of value frequencies in this bucket. The total number of
    #     values returned per bucket is capped at 20.
    # @!attribute [rw] bucket_value_count
    #   @return [Integer]
    #     Total number of distinct values in this bucket.
    class CategoricalStatsHistogramBucket; end
  end

  # Result of the k-anonymity computation.
  # @!attribute [rw] equivalence_class_histogram_buckets
  #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::KAnonymityResult::KAnonymityHistogramBucket>]
  #     Histogram of k-anonymity equivalence classes.
  class KAnonymityResult
    # The set of columns' values that share the same ldiversity value
    # @!attribute [rw] quasi_ids_values
    #   @return [Array<Google::Privacy::Dlp::V2::Value>]
    #     Set of values defining the equivalence class. One value per
    #     quasi-identifier column in the original KAnonymity metric message.
    #     The order is always the same as the original request.
    # @!attribute [rw] equivalence_class_size
    #   @return [Integer]
    #     Size of the equivalence class, for example number of rows with the
    #     above set of values.
    class KAnonymityEquivalenceClass; end

    # @!attribute [rw] equivalence_class_size_lower_bound
    #   @return [Integer]
    #     Lower bound on the size of the equivalence classes in this bucket.
    # @!attribute [rw] equivalence_class_size_upper_bound
    #   @return [Integer]
    #     Upper bound on the size of the equivalence classes in this bucket.
    # @!attribute [rw] bucket_size
    #   @return [Integer]
    #     Total number of equivalence classes in this bucket.
    # @!attribute [rw] bucket_values
    #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::KAnonymityResult::KAnonymityEquivalenceClass>]
    #     Sample of equivalence classes in this bucket. The total number of
    #     classes returned per bucket is capped at 20.
    # @!attribute [rw] bucket_value_count
    #   @return [Integer]
    #     Total number of distinct equivalence classes in this bucket.
    class KAnonymityHistogramBucket; end
  end

  # Result of the l-diversity computation.
  # @!attribute [rw] sensitive_value_frequency_histogram_buckets
  #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::LDiversityResult::LDiversityHistogramBucket>]
  #     Histogram of l-diversity equivalence class sensitive value frequencies.
  class LDiversityResult
    # The set of columns' values that share the same ldiversity value.
    # @!attribute [rw] quasi_ids_values
    #   @return [Array<Google::Privacy::Dlp::V2::Value>]
    #     Quasi-identifier values defining the k-anonymity equivalence
    #     class. The order is always the same as the original request.
    # @!attribute [rw] equivalence_class_size
    #   @return [Integer]
    #     Size of the k-anonymity equivalence class.
    # @!attribute [rw] num_distinct_sensitive_values
    #   @return [Integer]
    #     Number of distinct sensitive values in this equivalence class.
    # @!attribute [rw] top_sensitive_values
    #   @return [Array<Google::Privacy::Dlp::V2::ValueFrequency>]
    #     Estimated frequencies of top sensitive values.
    class LDiversityEquivalenceClass; end

    # @!attribute [rw] sensitive_value_frequency_lower_bound
    #   @return [Integer]
    #     Lower bound on the sensitive value frequencies of the equivalence
    #     classes in this bucket.
    # @!attribute [rw] sensitive_value_frequency_upper_bound
    #   @return [Integer]
    #     Upper bound on the sensitive value frequencies of the equivalence
    #     classes in this bucket.
    # @!attribute [rw] bucket_size
    #   @return [Integer]
    #     Total number of equivalence classes in this bucket.
    # @!attribute [rw] bucket_values
    #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::LDiversityResult::LDiversityEquivalenceClass>]
    #     Sample of equivalence classes in this bucket. The total number of
    #     classes returned per bucket is capped at 20.
    # @!attribute [rw] bucket_value_count
    #   @return [Integer]
    #     Total number of distinct equivalence classes in this bucket.
    class LDiversityHistogramBucket; end
  end

  # Result of the reidentifiability analysis. Note that these results are an
  # estimation, not exact values.
  # @!attribute [rw] k_map_estimation_histogram
  #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::KMapEstimationResult::KMapEstimationHistogramBucket>]
  #     The intervals [min_anonymity, max_anonymity] do not overlap. If a value
  #     doesn't correspond to any such interval, the associated frequency is
  #     zero. For example, the following records:
  #       {min_anonymity: 1, max_anonymity: 1, frequency: 17}
  #       {min_anonymity: 2, max_anonymity: 3, frequency: 42}
  #       {min_anonymity: 5, max_anonymity: 10, frequency: 99}
  #     mean that there are no record with an estimated anonymity of 4, 5, or
  #     larger than 10.
  class KMapEstimationResult
    # A tuple of values for the quasi-identifier columns.
    # @!attribute [rw] quasi_ids_values
    #   @return [Array<Google::Privacy::Dlp::V2::Value>]
    #     The quasi-identifier values.
    # @!attribute [rw] estimated_anonymity
    #   @return [Integer]
    #     The estimated anonymity for these quasi-identifier values.
    class KMapEstimationQuasiIdValues; end

    # A KMapEstimationHistogramBucket message with the following values:
    #   min_anonymity: 3
    #   max_anonymity: 5
    #   frequency: 42
    # means that there are 42 records whose quasi-identifier values correspond
    # to 3, 4 or 5 people in the overlying population. An important particular
    # case is when min_anonymity = max_anonymity = 1: the frequency field then
    # corresponds to the number of uniquely identifiable records.
    # @!attribute [rw] min_anonymity
    #   @return [Integer]
    #     Always positive.
    # @!attribute [rw] max_anonymity
    #   @return [Integer]
    #     Always greater than or equal to min_anonymity.
    # @!attribute [rw] bucket_size
    #   @return [Integer]
    #     Number of records within these anonymity bounds.
    # @!attribute [rw] bucket_values
    #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::KMapEstimationResult::KMapEstimationQuasiIdValues>]
    #     Sample of quasi-identifier tuple values in this bucket. The total
    #     number of classes returned per bucket is capped at 20.
    # @!attribute [rw] bucket_value_count
    #   @return [Integer]
    #     Total number of distinct quasi-identifier tuple values in this bucket.
    class KMapEstimationHistogramBucket; end
  end

  # Result of the δ-presence computation. Note that these results are an
  # estimation, not exact values.
  # @!attribute [rw] delta_presence_estimation_histogram
  #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::DeltaPresenceEstimationResult::DeltaPresenceEstimationHistogramBucket>]
  #     The intervals [min_probability, max_probability) do not overlap. If a
  #     value doesn't correspond to any such interval, the associated frequency
  #     is zero. For example, the following records:
  #       {min_probability: 0, max_probability: 0.1, frequency: 17}
  #       {min_probability: 0.2, max_probability: 0.3, frequency: 42}
  #       {min_probability: 0.3, max_probability: 0.4, frequency: 99}
  #     mean that there are no record with an estimated probability in [0.1, 0.2)
  #     nor larger or equal to 0.4.
  class DeltaPresenceEstimationResult
    # A tuple of values for the quasi-identifier columns.
    # @!attribute [rw] quasi_ids_values
    #   @return [Array<Google::Privacy::Dlp::V2::Value>]
    #     The quasi-identifier values.
    # @!attribute [rw] estimated_probability
    #   @return [Float]
    #     The estimated probability that a given individual sharing these
    #     quasi-identifier values is in the dataset. This value, typically called
    #     δ, is the ratio between the number of records in the dataset with these
    #     quasi-identifier values, and the total number of individuals (inside
    #     *and* outside the dataset) with these quasi-identifier values.
    #     For example, if there are 15 individuals in the dataset who share the
    #     same quasi-identifier values, and an estimated 100 people in the entire
    #     population with these values, then δ is 0.15.
    class DeltaPresenceEstimationQuasiIdValues; end

    # A DeltaPresenceEstimationHistogramBucket message with the following
    # values:
    #   min_probability: 0.1
    #   max_probability: 0.2
    #   frequency: 42
    # means that there are 42 records for which δ is in [0.1, 0.2). An
    # important particular case is when min_probability = max_probability = 1:
    # then, every individual who shares this quasi-identifier combination is in
    # the dataset.
    # @!attribute [rw] min_probability
    #   @return [Float]
    #     Between 0 and 1.
    # @!attribute [rw] max_probability
    #   @return [Float]
    #     Always greater than or equal to min_probability.
    # @!attribute [rw] bucket_size
    #   @return [Integer]
    #     Number of records within these probability bounds.
    # @!attribute [rw] bucket_values
    #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::DeltaPresenceEstimationResult::DeltaPresenceEstimationQuasiIdValues>]
    #     Sample of quasi-identifier tuple values in this bucket. The total
    #     number of classes returned per bucket is capped at 20.
    # @!attribute [rw] bucket_value_count
    #   @return [Integer]
    #     Total number of distinct quasi-identifier tuple values in this bucket.
    class DeltaPresenceEstimationHistogramBucket; end
  end
end

#delta_presence_estimation_result ⇒ Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::DeltaPresenceEstimationResult

Returns:

• (Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::DeltaPresenceEstimationResult)

# File 'lib/google/cloud/dlp/v2/doc/google/privacy/dlp/v2/dlp.rb', line 891

class AnalyzeDataSourceRiskDetails
  # Result of the numerical stats computation.
  # @!attribute [rw] min_value
  #   @return [Google::Privacy::Dlp::V2::Value]
  #     Minimum value appearing in the column.
  # @!attribute [rw] max_value
  #   @return [Google::Privacy::Dlp::V2::Value]
  #     Maximum value appearing in the column.
  # @!attribute [rw] quantile_values
  #   @return [Array<Google::Privacy::Dlp::V2::Value>]
  #     List of 99 values that partition the set of field values into 100 equal
  #     sized buckets.
  class NumericalStatsResult; end

  # Result of the categorical stats computation.
  # @!attribute [rw] value_frequency_histogram_buckets
  #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::CategoricalStatsResult::CategoricalStatsHistogramBucket>]
  #     Histogram of value frequencies in the column.
  class CategoricalStatsResult
    # @!attribute [rw] value_frequency_lower_bound
    #   @return [Integer]
    #     Lower bound on the value frequency of the values in this bucket.
    # @!attribute [rw] value_frequency_upper_bound
    #   @return [Integer]
    #     Upper bound on the value frequency of the values in this bucket.
    # @!attribute [rw] bucket_size
    #   @return [Integer]
    #     Total number of values in this bucket.
    # @!attribute [rw] bucket_values
    #   @return [Array<Google::Privacy::Dlp::V2::ValueFrequency>]
    #     Sample of value frequencies in this bucket. The total number of
    #     values returned per bucket is capped at 20.
    # @!attribute [rw] bucket_value_count
    #   @return [Integer]
    #     Total number of distinct values in this bucket.
    class CategoricalStatsHistogramBucket; end
  end

  # Result of the k-anonymity computation.
  # @!attribute [rw] equivalence_class_histogram_buckets
  #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::KAnonymityResult::KAnonymityHistogramBucket>]
  #     Histogram of k-anonymity equivalence classes.
  class KAnonymityResult
    # The set of columns' values that share the same ldiversity value
    # @!attribute [rw] quasi_ids_values
    #   @return [Array<Google::Privacy::Dlp::V2::Value>]
    #     Set of values defining the equivalence class. One value per
    #     quasi-identifier column in the original KAnonymity metric message.
    #     The order is always the same as the original request.
    # @!attribute [rw] equivalence_class_size
    #   @return [Integer]
    #     Size of the equivalence class, for example number of rows with the
    #     above set of values.
    class KAnonymityEquivalenceClass; end

    # @!attribute [rw] equivalence_class_size_lower_bound
    #   @return [Integer]
    #     Lower bound on the size of the equivalence classes in this bucket.
    # @!attribute [rw] equivalence_class_size_upper_bound
    #   @return [Integer]
    #     Upper bound on the size of the equivalence classes in this bucket.
    # @!attribute [rw] bucket_size
    #   @return [Integer]
    #     Total number of equivalence classes in this bucket.
    # @!attribute [rw] bucket_values
    #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::KAnonymityResult::KAnonymityEquivalenceClass>]
    #     Sample of equivalence classes in this bucket. The total number of
    #     classes returned per bucket is capped at 20.
    # @!attribute [rw] bucket_value_count
    #   @return [Integer]
    #     Total number of distinct equivalence classes in this bucket.
    class KAnonymityHistogramBucket; end
  end

  # Result of the l-diversity computation.
  # @!attribute [rw] sensitive_value_frequency_histogram_buckets
  #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::LDiversityResult::LDiversityHistogramBucket>]
  #     Histogram of l-diversity equivalence class sensitive value frequencies.
  class LDiversityResult
    # The set of columns' values that share the same ldiversity value.
    # @!attribute [rw] quasi_ids_values
    #   @return [Array<Google::Privacy::Dlp::V2::Value>]
    #     Quasi-identifier values defining the k-anonymity equivalence
    #     class. The order is always the same as the original request.
    # @!attribute [rw] equivalence_class_size
    #   @return [Integer]
    #     Size of the k-anonymity equivalence class.
    # @!attribute [rw] num_distinct_sensitive_values
    #   @return [Integer]
    #     Number of distinct sensitive values in this equivalence class.
    # @!attribute [rw] top_sensitive_values
    #   @return [Array<Google::Privacy::Dlp::V2::ValueFrequency>]
    #     Estimated frequencies of top sensitive values.
    class LDiversityEquivalenceClass; end

    # @!attribute [rw] sensitive_value_frequency_lower_bound
    #   @return [Integer]
    #     Lower bound on the sensitive value frequencies of the equivalence
    #     classes in this bucket.
    # @!attribute [rw] sensitive_value_frequency_upper_bound
    #   @return [Integer]
    #     Upper bound on the sensitive value frequencies of the equivalence
    #     classes in this bucket.
    # @!attribute [rw] bucket_size
    #   @return [Integer]
    #     Total number of equivalence classes in this bucket.
    # @!attribute [rw] bucket_values
    #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::LDiversityResult::LDiversityEquivalenceClass>]
    #     Sample of equivalence classes in this bucket. The total number of
    #     classes returned per bucket is capped at 20.
    # @!attribute [rw] bucket_value_count
    #   @return [Integer]
    #     Total number of distinct equivalence classes in this bucket.
    class LDiversityHistogramBucket; end
  end

  # Result of the reidentifiability analysis. Note that these results are an
  # estimation, not exact values.
  # @!attribute [rw] k_map_estimation_histogram
  #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::KMapEstimationResult::KMapEstimationHistogramBucket>]
  #     The intervals [min_anonymity, max_anonymity] do not overlap. If a value
  #     doesn't correspond to any such interval, the associated frequency is
  #     zero. For example, the following records:
  #       {min_anonymity: 1, max_anonymity: 1, frequency: 17}
  #       {min_anonymity: 2, max_anonymity: 3, frequency: 42}
  #       {min_anonymity: 5, max_anonymity: 10, frequency: 99}
  #     mean that there are no record with an estimated anonymity of 4, 5, or
  #     larger than 10.
  class KMapEstimationResult
    # A tuple of values for the quasi-identifier columns.
    # @!attribute [rw] quasi_ids_values
    #   @return [Array<Google::Privacy::Dlp::V2::Value>]
    #     The quasi-identifier values.
    # @!attribute [rw] estimated_anonymity
    #   @return [Integer]
    #     The estimated anonymity for these quasi-identifier values.
    class KMapEstimationQuasiIdValues; end

    # A KMapEstimationHistogramBucket message with the following values:
    #   min_anonymity: 3
    #   max_anonymity: 5
    #   frequency: 42
    # means that there are 42 records whose quasi-identifier values correspond
    # to 3, 4 or 5 people in the overlying population. An important particular
    # case is when min_anonymity = max_anonymity = 1: the frequency field then
    # corresponds to the number of uniquely identifiable records.
    # @!attribute [rw] min_anonymity
    #   @return [Integer]
    #     Always positive.
    # @!attribute [rw] max_anonymity
    #   @return [Integer]
    #     Always greater than or equal to min_anonymity.
    # @!attribute [rw] bucket_size
    #   @return [Integer]
    #     Number of records within these anonymity bounds.
    # @!attribute [rw] bucket_values
    #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::KMapEstimationResult::KMapEstimationQuasiIdValues>]
    #     Sample of quasi-identifier tuple values in this bucket. The total
    #     number of classes returned per bucket is capped at 20.
    # @!attribute [rw] bucket_value_count
    #   @return [Integer]
    #     Total number of distinct quasi-identifier tuple values in this bucket.
    class KMapEstimationHistogramBucket; end
  end

  # Result of the δ-presence computation. Note that these results are an
  # estimation, not exact values.
  # @!attribute [rw] delta_presence_estimation_histogram
  #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::DeltaPresenceEstimationResult::DeltaPresenceEstimationHistogramBucket>]
  #     The intervals [min_probability, max_probability) do not overlap. If a
  #     value doesn't correspond to any such interval, the associated frequency
  #     is zero. For example, the following records:
  #       {min_probability: 0, max_probability: 0.1, frequency: 17}
  #       {min_probability: 0.2, max_probability: 0.3, frequency: 42}
  #       {min_probability: 0.3, max_probability: 0.4, frequency: 99}
  #     mean that there are no record with an estimated probability in [0.1, 0.2)
  #     nor larger or equal to 0.4.
  class DeltaPresenceEstimationResult
    # A tuple of values for the quasi-identifier columns.
    # @!attribute [rw] quasi_ids_values
    #   @return [Array<Google::Privacy::Dlp::V2::Value>]
    #     The quasi-identifier values.
    # @!attribute [rw] estimated_probability
    #   @return [Float]
    #     The estimated probability that a given individual sharing these
    #     quasi-identifier values is in the dataset. This value, typically called
    #     δ, is the ratio between the number of records in the dataset with these
    #     quasi-identifier values, and the total number of individuals (inside
    #     *and* outside the dataset) with these quasi-identifier values.
    #     For example, if there are 15 individuals in the dataset who share the
    #     same quasi-identifier values, and an estimated 100 people in the entire
    #     population with these values, then δ is 0.15.
    class DeltaPresenceEstimationQuasiIdValues; end

    # A DeltaPresenceEstimationHistogramBucket message with the following
    # values:
    #   min_probability: 0.1
    #   max_probability: 0.2
    #   frequency: 42
    # means that there are 42 records for which δ is in [0.1, 0.2). An
    # important particular case is when min_probability = max_probability = 1:
    # then, every individual who shares this quasi-identifier combination is in
    # the dataset.
    # @!attribute [rw] min_probability
    #   @return [Float]
    #     Between 0 and 1.
    # @!attribute [rw] max_probability
    #   @return [Float]
    #     Always greater than or equal to min_probability.
    # @!attribute [rw] bucket_size
    #   @return [Integer]
    #     Number of records within these probability bounds.
    # @!attribute [rw] bucket_values
    #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::DeltaPresenceEstimationResult::DeltaPresenceEstimationQuasiIdValues>]
    #     Sample of quasi-identifier tuple values in this bucket. The total
    #     number of classes returned per bucket is capped at 20.
    # @!attribute [rw] bucket_value_count
    #   @return [Integer]
    #     Total number of distinct quasi-identifier tuple values in this bucket.
    class DeltaPresenceEstimationHistogramBucket; end
  end
end

#k_anonymity_result ⇒ Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::KAnonymityResult

Returns:

• (Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::KAnonymityResult)

# File 'lib/google/cloud/dlp/v2/doc/google/privacy/dlp/v2/dlp.rb', line 891

class AnalyzeDataSourceRiskDetails
  # Result of the numerical stats computation.
  # @!attribute [rw] min_value
  #   @return [Google::Privacy::Dlp::V2::Value]
  #     Minimum value appearing in the column.
  # @!attribute [rw] max_value
  #   @return [Google::Privacy::Dlp::V2::Value]
  #     Maximum value appearing in the column.
  # @!attribute [rw] quantile_values
  #   @return [Array<Google::Privacy::Dlp::V2::Value>]
  #     List of 99 values that partition the set of field values into 100 equal
  #     sized buckets.
  class NumericalStatsResult; end

  # Result of the categorical stats computation.
  # @!attribute [rw] value_frequency_histogram_buckets
  #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::CategoricalStatsResult::CategoricalStatsHistogramBucket>]
  #     Histogram of value frequencies in the column.
  class CategoricalStatsResult
    # @!attribute [rw] value_frequency_lower_bound
    #   @return [Integer]
    #     Lower bound on the value frequency of the values in this bucket.
    # @!attribute [rw] value_frequency_upper_bound
    #   @return [Integer]
    #     Upper bound on the value frequency of the values in this bucket.
    # @!attribute [rw] bucket_size
    #   @return [Integer]
    #     Total number of values in this bucket.
    # @!attribute [rw] bucket_values
    #   @return [Array<Google::Privacy::Dlp::V2::ValueFrequency>]
    #     Sample of value frequencies in this bucket. The total number of
    #     values returned per bucket is capped at 20.
    # @!attribute [rw] bucket_value_count
    #   @return [Integer]
    #     Total number of distinct values in this bucket.
    class CategoricalStatsHistogramBucket; end
  end

  # Result of the k-anonymity computation.
  # @!attribute [rw] equivalence_class_histogram_buckets
  #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::KAnonymityResult::KAnonymityHistogramBucket>]
  #     Histogram of k-anonymity equivalence classes.
  class KAnonymityResult
    # The set of columns' values that share the same ldiversity value
    # @!attribute [rw] quasi_ids_values
    #   @return [Array<Google::Privacy::Dlp::V2::Value>]
    #     Set of values defining the equivalence class. One value per
    #     quasi-identifier column in the original KAnonymity metric message.
    #     The order is always the same as the original request.
    # @!attribute [rw] equivalence_class_size
    #   @return [Integer]
    #     Size of the equivalence class, for example number of rows with the
    #     above set of values.
    class KAnonymityEquivalenceClass; end

    # @!attribute [rw] equivalence_class_size_lower_bound
    #   @return [Integer]
    #     Lower bound on the size of the equivalence classes in this bucket.
    # @!attribute [rw] equivalence_class_size_upper_bound
    #   @return [Integer]
    #     Upper bound on the size of the equivalence classes in this bucket.
    # @!attribute [rw] bucket_size
    #   @return [Integer]
    #     Total number of equivalence classes in this bucket.
    # @!attribute [rw] bucket_values
    #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::KAnonymityResult::KAnonymityEquivalenceClass>]
    #     Sample of equivalence classes in this bucket. The total number of
    #     classes returned per bucket is capped at 20.
    # @!attribute [rw] bucket_value_count
    #   @return [Integer]
    #     Total number of distinct equivalence classes in this bucket.
    class KAnonymityHistogramBucket; end
  end

  # Result of the l-diversity computation.
  # @!attribute [rw] sensitive_value_frequency_histogram_buckets
  #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::LDiversityResult::LDiversityHistogramBucket>]
  #     Histogram of l-diversity equivalence class sensitive value frequencies.
  class LDiversityResult
    # The set of columns' values that share the same ldiversity value.
    # @!attribute [rw] quasi_ids_values
    #   @return [Array<Google::Privacy::Dlp::V2::Value>]
    #     Quasi-identifier values defining the k-anonymity equivalence
    #     class. The order is always the same as the original request.
    # @!attribute [rw] equivalence_class_size
    #   @return [Integer]
    #     Size of the k-anonymity equivalence class.
    # @!attribute [rw] num_distinct_sensitive_values
    #   @return [Integer]
    #     Number of distinct sensitive values in this equivalence class.
    # @!attribute [rw] top_sensitive_values
    #   @return [Array<Google::Privacy::Dlp::V2::ValueFrequency>]
    #     Estimated frequencies of top sensitive values.
    class LDiversityEquivalenceClass; end

    # @!attribute [rw] sensitive_value_frequency_lower_bound
    #   @return [Integer]
    #     Lower bound on the sensitive value frequencies of the equivalence
    #     classes in this bucket.
    # @!attribute [rw] sensitive_value_frequency_upper_bound
    #   @return [Integer]
    #     Upper bound on the sensitive value frequencies of the equivalence
    #     classes in this bucket.
    # @!attribute [rw] bucket_size
    #   @return [Integer]
    #     Total number of equivalence classes in this bucket.
    # @!attribute [rw] bucket_values
    #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::LDiversityResult::LDiversityEquivalenceClass>]
    #     Sample of equivalence classes in this bucket. The total number of
    #     classes returned per bucket is capped at 20.
    # @!attribute [rw] bucket_value_count
    #   @return [Integer]
    #     Total number of distinct equivalence classes in this bucket.
    class LDiversityHistogramBucket; end
  end

  # Result of the reidentifiability analysis. Note that these results are an
  # estimation, not exact values.
  # @!attribute [rw] k_map_estimation_histogram
  #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::KMapEstimationResult::KMapEstimationHistogramBucket>]
  #     The intervals [min_anonymity, max_anonymity] do not overlap. If a value
  #     doesn't correspond to any such interval, the associated frequency is
  #     zero. For example, the following records:
  #       {min_anonymity: 1, max_anonymity: 1, frequency: 17}
  #       {min_anonymity: 2, max_anonymity: 3, frequency: 42}
  #       {min_anonymity: 5, max_anonymity: 10, frequency: 99}
  #     mean that there are no record with an estimated anonymity of 4, 5, or
  #     larger than 10.
  class KMapEstimationResult
    # A tuple of values for the quasi-identifier columns.
    # @!attribute [rw] quasi_ids_values
    #   @return [Array<Google::Privacy::Dlp::V2::Value>]
    #     The quasi-identifier values.
    # @!attribute [rw] estimated_anonymity
    #   @return [Integer]
    #     The estimated anonymity for these quasi-identifier values.
    class KMapEstimationQuasiIdValues; end

    # A KMapEstimationHistogramBucket message with the following values:
    #   min_anonymity: 3
    #   max_anonymity: 5
    #   frequency: 42
    # means that there are 42 records whose quasi-identifier values correspond
    # to 3, 4 or 5 people in the overlying population. An important particular
    # case is when min_anonymity = max_anonymity = 1: the frequency field then
    # corresponds to the number of uniquely identifiable records.
    # @!attribute [rw] min_anonymity
    #   @return [Integer]
    #     Always positive.
    # @!attribute [rw] max_anonymity
    #   @return [Integer]
    #     Always greater than or equal to min_anonymity.
    # @!attribute [rw] bucket_size
    #   @return [Integer]
    #     Number of records within these anonymity bounds.
    # @!attribute [rw] bucket_values
    #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::KMapEstimationResult::KMapEstimationQuasiIdValues>]
    #     Sample of quasi-identifier tuple values in this bucket. The total
    #     number of classes returned per bucket is capped at 20.
    # @!attribute [rw] bucket_value_count
    #   @return [Integer]
    #     Total number of distinct quasi-identifier tuple values in this bucket.
    class KMapEstimationHistogramBucket; end
  end

  # Result of the δ-presence computation. Note that these results are an
  # estimation, not exact values.
  # @!attribute [rw] delta_presence_estimation_histogram
  #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::DeltaPresenceEstimationResult::DeltaPresenceEstimationHistogramBucket>]
  #     The intervals [min_probability, max_probability) do not overlap. If a
  #     value doesn't correspond to any such interval, the associated frequency
  #     is zero. For example, the following records:
  #       {min_probability: 0, max_probability: 0.1, frequency: 17}
  #       {min_probability: 0.2, max_probability: 0.3, frequency: 42}
  #       {min_probability: 0.3, max_probability: 0.4, frequency: 99}
  #     mean that there are no record with an estimated probability in [0.1, 0.2)
  #     nor larger or equal to 0.4.
  class DeltaPresenceEstimationResult
    # A tuple of values for the quasi-identifier columns.
    # @!attribute [rw] quasi_ids_values
    #   @return [Array<Google::Privacy::Dlp::V2::Value>]
    #     The quasi-identifier values.
    # @!attribute [rw] estimated_probability
    #   @return [Float]
    #     The estimated probability that a given individual sharing these
    #     quasi-identifier values is in the dataset. This value, typically called
    #     δ, is the ratio between the number of records in the dataset with these
    #     quasi-identifier values, and the total number of individuals (inside
    #     *and* outside the dataset) with these quasi-identifier values.
    #     For example, if there are 15 individuals in the dataset who share the
    #     same quasi-identifier values, and an estimated 100 people in the entire
    #     population with these values, then δ is 0.15.
    class DeltaPresenceEstimationQuasiIdValues; end

    # A DeltaPresenceEstimationHistogramBucket message with the following
    # values:
    #   min_probability: 0.1
    #   max_probability: 0.2
    #   frequency: 42
    # means that there are 42 records for which δ is in [0.1, 0.2). An
    # important particular case is when min_probability = max_probability = 1:
    # then, every individual who shares this quasi-identifier combination is in
    # the dataset.
    # @!attribute [rw] min_probability
    #   @return [Float]
    #     Between 0 and 1.
    # @!attribute [rw] max_probability
    #   @return [Float]
    #     Always greater than or equal to min_probability.
    # @!attribute [rw] bucket_size
    #   @return [Integer]
    #     Number of records within these probability bounds.
    # @!attribute [rw] bucket_values
    #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::DeltaPresenceEstimationResult::DeltaPresenceEstimationQuasiIdValues>]
    #     Sample of quasi-identifier tuple values in this bucket. The total
    #     number of classes returned per bucket is capped at 20.
    # @!attribute [rw] bucket_value_count
    #   @return [Integer]
    #     Total number of distinct quasi-identifier tuple values in this bucket.
    class DeltaPresenceEstimationHistogramBucket; end
  end
end

#k_map_estimation_result ⇒ Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::KMapEstimationResult

Returns:

• (Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::KMapEstimationResult)

# File 'lib/google/cloud/dlp/v2/doc/google/privacy/dlp/v2/dlp.rb', line 891

class AnalyzeDataSourceRiskDetails
  # Result of the numerical stats computation.
  # @!attribute [rw] min_value
  #   @return [Google::Privacy::Dlp::V2::Value]
  #     Minimum value appearing in the column.
  # @!attribute [rw] max_value
  #   @return [Google::Privacy::Dlp::V2::Value]
  #     Maximum value appearing in the column.
  # @!attribute [rw] quantile_values
  #   @return [Array<Google::Privacy::Dlp::V2::Value>]
  #     List of 99 values that partition the set of field values into 100 equal
  #     sized buckets.
  class NumericalStatsResult; end

  # Result of the categorical stats computation.
  # @!attribute [rw] value_frequency_histogram_buckets
  #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::CategoricalStatsResult::CategoricalStatsHistogramBucket>]
  #     Histogram of value frequencies in the column.
  class CategoricalStatsResult
    # @!attribute [rw] value_frequency_lower_bound
    #   @return [Integer]
    #     Lower bound on the value frequency of the values in this bucket.
    # @!attribute [rw] value_frequency_upper_bound
    #   @return [Integer]
    #     Upper bound on the value frequency of the values in this bucket.
    # @!attribute [rw] bucket_size
    #   @return [Integer]
    #     Total number of values in this bucket.
    # @!attribute [rw] bucket_values
    #   @return [Array<Google::Privacy::Dlp::V2::ValueFrequency>]
    #     Sample of value frequencies in this bucket. The total number of
    #     values returned per bucket is capped at 20.
    # @!attribute [rw] bucket_value_count
    #   @return [Integer]
    #     Total number of distinct values in this bucket.
    class CategoricalStatsHistogramBucket; end
  end

  # Result of the k-anonymity computation.
  # @!attribute [rw] equivalence_class_histogram_buckets
  #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::KAnonymityResult::KAnonymityHistogramBucket>]
  #     Histogram of k-anonymity equivalence classes.
  class KAnonymityResult
    # The set of columns' values that share the same ldiversity value
    # @!attribute [rw] quasi_ids_values
    #   @return [Array<Google::Privacy::Dlp::V2::Value>]
    #     Set of values defining the equivalence class. One value per
    #     quasi-identifier column in the original KAnonymity metric message.
    #     The order is always the same as the original request.
    # @!attribute [rw] equivalence_class_size
    #   @return [Integer]
    #     Size of the equivalence class, for example number of rows with the
    #     above set of values.
    class KAnonymityEquivalenceClass; end

    # @!attribute [rw] equivalence_class_size_lower_bound
    #   @return [Integer]
    #     Lower bound on the size of the equivalence classes in this bucket.
    # @!attribute [rw] equivalence_class_size_upper_bound
    #   @return [Integer]
    #     Upper bound on the size of the equivalence classes in this bucket.
    # @!attribute [rw] bucket_size
    #   @return [Integer]
    #     Total number of equivalence classes in this bucket.
    # @!attribute [rw] bucket_values
    #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::KAnonymityResult::KAnonymityEquivalenceClass>]
    #     Sample of equivalence classes in this bucket. The total number of
    #     classes returned per bucket is capped at 20.
    # @!attribute [rw] bucket_value_count
    #   @return [Integer]
    #     Total number of distinct equivalence classes in this bucket.
    class KAnonymityHistogramBucket; end
  end

  # Result of the l-diversity computation.
  # @!attribute [rw] sensitive_value_frequency_histogram_buckets
  #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::LDiversityResult::LDiversityHistogramBucket>]
  #     Histogram of l-diversity equivalence class sensitive value frequencies.
  class LDiversityResult
    # The set of columns' values that share the same ldiversity value.
    # @!attribute [rw] quasi_ids_values
    #   @return [Array<Google::Privacy::Dlp::V2::Value>]
    #     Quasi-identifier values defining the k-anonymity equivalence
    #     class. The order is always the same as the original request.
    # @!attribute [rw] equivalence_class_size
    #   @return [Integer]
    #     Size of the k-anonymity equivalence class.
    # @!attribute [rw] num_distinct_sensitive_values
    #   @return [Integer]
    #     Number of distinct sensitive values in this equivalence class.
    # @!attribute [rw] top_sensitive_values
    #   @return [Array<Google::Privacy::Dlp::V2::ValueFrequency>]
    #     Estimated frequencies of top sensitive values.
    class LDiversityEquivalenceClass; end

    # @!attribute [rw] sensitive_value_frequency_lower_bound
    #   @return [Integer]
    #     Lower bound on the sensitive value frequencies of the equivalence
    #     classes in this bucket.
    # @!attribute [rw] sensitive_value_frequency_upper_bound
    #   @return [Integer]
    #     Upper bound on the sensitive value frequencies of the equivalence
    #     classes in this bucket.
    # @!attribute [rw] bucket_size
    #   @return [Integer]
    #     Total number of equivalence classes in this bucket.
    # @!attribute [rw] bucket_values
    #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::LDiversityResult::LDiversityEquivalenceClass>]
    #     Sample of equivalence classes in this bucket. The total number of
    #     classes returned per bucket is capped at 20.
    # @!attribute [rw] bucket_value_count
    #   @return [Integer]
    #     Total number of distinct equivalence classes in this bucket.
    class LDiversityHistogramBucket; end
  end

  # Result of the reidentifiability analysis. Note that these results are an
  # estimation, not exact values.
  # @!attribute [rw] k_map_estimation_histogram
  #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::KMapEstimationResult::KMapEstimationHistogramBucket>]
  #     The intervals [min_anonymity, max_anonymity] do not overlap. If a value
  #     doesn't correspond to any such interval, the associated frequency is
  #     zero. For example, the following records:
  #       {min_anonymity: 1, max_anonymity: 1, frequency: 17}
  #       {min_anonymity: 2, max_anonymity: 3, frequency: 42}
  #       {min_anonymity: 5, max_anonymity: 10, frequency: 99}
  #     mean that there are no record with an estimated anonymity of 4, 5, or
  #     larger than 10.
  class KMapEstimationResult
    # A tuple of values for the quasi-identifier columns.
    # @!attribute [rw] quasi_ids_values
    #   @return [Array<Google::Privacy::Dlp::V2::Value>]
    #     The quasi-identifier values.
    # @!attribute [rw] estimated_anonymity
    #   @return [Integer]
    #     The estimated anonymity for these quasi-identifier values.
    class KMapEstimationQuasiIdValues; end

    # A KMapEstimationHistogramBucket message with the following values:
    #   min_anonymity: 3
    #   max_anonymity: 5
    #   frequency: 42
    # means that there are 42 records whose quasi-identifier values correspond
    # to 3, 4 or 5 people in the overlying population. An important particular
    # case is when min_anonymity = max_anonymity = 1: the frequency field then
    # corresponds to the number of uniquely identifiable records.
    # @!attribute [rw] min_anonymity
    #   @return [Integer]
    #     Always positive.
    # @!attribute [rw] max_anonymity
    #   @return [Integer]
    #     Always greater than or equal to min_anonymity.
    # @!attribute [rw] bucket_size
    #   @return [Integer]
    #     Number of records within these anonymity bounds.
    # @!attribute [rw] bucket_values
    #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::KMapEstimationResult::KMapEstimationQuasiIdValues>]
    #     Sample of quasi-identifier tuple values in this bucket. The total
    #     number of classes returned per bucket is capped at 20.
    # @!attribute [rw] bucket_value_count
    #   @return [Integer]
    #     Total number of distinct quasi-identifier tuple values in this bucket.
    class KMapEstimationHistogramBucket; end
  end

  # Result of the δ-presence computation. Note that these results are an
  # estimation, not exact values.
  # @!attribute [rw] delta_presence_estimation_histogram
  #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::DeltaPresenceEstimationResult::DeltaPresenceEstimationHistogramBucket>]
  #     The intervals [min_probability, max_probability) do not overlap. If a
  #     value doesn't correspond to any such interval, the associated frequency
  #     is zero. For example, the following records:
  #       {min_probability: 0, max_probability: 0.1, frequency: 17}
  #       {min_probability: 0.2, max_probability: 0.3, frequency: 42}
  #       {min_probability: 0.3, max_probability: 0.4, frequency: 99}
  #     mean that there are no record with an estimated probability in [0.1, 0.2)
  #     nor larger or equal to 0.4.
  class DeltaPresenceEstimationResult
    # A tuple of values for the quasi-identifier columns.
    # @!attribute [rw] quasi_ids_values
    #   @return [Array<Google::Privacy::Dlp::V2::Value>]
    #     The quasi-identifier values.
    # @!attribute [rw] estimated_probability
    #   @return [Float]
    #     The estimated probability that a given individual sharing these
    #     quasi-identifier values is in the dataset. This value, typically called
    #     δ, is the ratio between the number of records in the dataset with these
    #     quasi-identifier values, and the total number of individuals (inside
    #     *and* outside the dataset) with these quasi-identifier values.
    #     For example, if there are 15 individuals in the dataset who share the
    #     same quasi-identifier values, and an estimated 100 people in the entire
    #     population with these values, then δ is 0.15.
    class DeltaPresenceEstimationQuasiIdValues; end

    # A DeltaPresenceEstimationHistogramBucket message with the following
    # values:
    #   min_probability: 0.1
    #   max_probability: 0.2
    #   frequency: 42
    # means that there are 42 records for which δ is in [0.1, 0.2). An
    # important particular case is when min_probability = max_probability = 1:
    # then, every individual who shares this quasi-identifier combination is in
    # the dataset.
    # @!attribute [rw] min_probability
    #   @return [Float]
    #     Between 0 and 1.
    # @!attribute [rw] max_probability
    #   @return [Float]
    #     Always greater than or equal to min_probability.
    # @!attribute [rw] bucket_size
    #   @return [Integer]
    #     Number of records within these probability bounds.
    # @!attribute [rw] bucket_values
    #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::DeltaPresenceEstimationResult::DeltaPresenceEstimationQuasiIdValues>]
    #     Sample of quasi-identifier tuple values in this bucket. The total
    #     number of classes returned per bucket is capped at 20.
    # @!attribute [rw] bucket_value_count
    #   @return [Integer]
    #     Total number of distinct quasi-identifier tuple values in this bucket.
    class DeltaPresenceEstimationHistogramBucket; end
  end
end

#l_diversity_result ⇒ Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::LDiversityResult

Returns:

• (Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::LDiversityResult)

# File 'lib/google/cloud/dlp/v2/doc/google/privacy/dlp/v2/dlp.rb', line 891

class AnalyzeDataSourceRiskDetails
  # Result of the numerical stats computation.
  # @!attribute [rw] min_value
  #   @return [Google::Privacy::Dlp::V2::Value]
  #     Minimum value appearing in the column.
  # @!attribute [rw] max_value
  #   @return [Google::Privacy::Dlp::V2::Value]
  #     Maximum value appearing in the column.
  # @!attribute [rw] quantile_values
  #   @return [Array<Google::Privacy::Dlp::V2::Value>]
  #     List of 99 values that partition the set of field values into 100 equal
  #     sized buckets.
  class NumericalStatsResult; end

  # Result of the categorical stats computation.
  # @!attribute [rw] value_frequency_histogram_buckets
  #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::CategoricalStatsResult::CategoricalStatsHistogramBucket>]
  #     Histogram of value frequencies in the column.
  class CategoricalStatsResult
    # @!attribute [rw] value_frequency_lower_bound
    #   @return [Integer]
    #     Lower bound on the value frequency of the values in this bucket.
    # @!attribute [rw] value_frequency_upper_bound
    #   @return [Integer]
    #     Upper bound on the value frequency of the values in this bucket.
    # @!attribute [rw] bucket_size
    #   @return [Integer]
    #     Total number of values in this bucket.
    # @!attribute [rw] bucket_values
    #   @return [Array<Google::Privacy::Dlp::V2::ValueFrequency>]
    #     Sample of value frequencies in this bucket. The total number of
    #     values returned per bucket is capped at 20.
    # @!attribute [rw] bucket_value_count
    #   @return [Integer]
    #     Total number of distinct values in this bucket.
    class CategoricalStatsHistogramBucket; end
  end

  # Result of the k-anonymity computation.
  # @!attribute [rw] equivalence_class_histogram_buckets
  #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::KAnonymityResult::KAnonymityHistogramBucket>]
  #     Histogram of k-anonymity equivalence classes.
  class KAnonymityResult
    # The set of columns' values that share the same ldiversity value
    # @!attribute [rw] quasi_ids_values
    #   @return [Array<Google::Privacy::Dlp::V2::Value>]
    #     Set of values defining the equivalence class. One value per
    #     quasi-identifier column in the original KAnonymity metric message.
    #     The order is always the same as the original request.
    # @!attribute [rw] equivalence_class_size
    #   @return [Integer]
    #     Size of the equivalence class, for example number of rows with the
    #     above set of values.
    class KAnonymityEquivalenceClass; end

    # @!attribute [rw] equivalence_class_size_lower_bound
    #   @return [Integer]
    #     Lower bound on the size of the equivalence classes in this bucket.
    # @!attribute [rw] equivalence_class_size_upper_bound
    #   @return [Integer]
    #     Upper bound on the size of the equivalence classes in this bucket.
    # @!attribute [rw] bucket_size
    #   @return [Integer]
    #     Total number of equivalence classes in this bucket.
    # @!attribute [rw] bucket_values
    #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::KAnonymityResult::KAnonymityEquivalenceClass>]
    #     Sample of equivalence classes in this bucket. The total number of
    #     classes returned per bucket is capped at 20.
    # @!attribute [rw] bucket_value_count
    #   @return [Integer]
    #     Total number of distinct equivalence classes in this bucket.
    class KAnonymityHistogramBucket; end
  end

  # Result of the l-diversity computation.
  # @!attribute [rw] sensitive_value_frequency_histogram_buckets
  #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::LDiversityResult::LDiversityHistogramBucket>]
  #     Histogram of l-diversity equivalence class sensitive value frequencies.
  class LDiversityResult
    # The set of columns' values that share the same ldiversity value.
    # @!attribute [rw] quasi_ids_values
    #   @return [Array<Google::Privacy::Dlp::V2::Value>]
    #     Quasi-identifier values defining the k-anonymity equivalence
    #     class. The order is always the same as the original request.
    # @!attribute [rw] equivalence_class_size
    #   @return [Integer]
    #     Size of the k-anonymity equivalence class.
    # @!attribute [rw] num_distinct_sensitive_values
    #   @return [Integer]
    #     Number of distinct sensitive values in this equivalence class.
    # @!attribute [rw] top_sensitive_values
    #   @return [Array<Google::Privacy::Dlp::V2::ValueFrequency>]
    #     Estimated frequencies of top sensitive values.
    class LDiversityEquivalenceClass; end

    # @!attribute [rw] sensitive_value_frequency_lower_bound
    #   @return [Integer]
    #     Lower bound on the sensitive value frequencies of the equivalence
    #     classes in this bucket.
    # @!attribute [rw] sensitive_value_frequency_upper_bound
    #   @return [Integer]
    #     Upper bound on the sensitive value frequencies of the equivalence
    #     classes in this bucket.
    # @!attribute [rw] bucket_size
    #   @return [Integer]
    #     Total number of equivalence classes in this bucket.
    # @!attribute [rw] bucket_values
    #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::LDiversityResult::LDiversityEquivalenceClass>]
    #     Sample of equivalence classes in this bucket. The total number of
    #     classes returned per bucket is capped at 20.
    # @!attribute [rw] bucket_value_count
    #   @return [Integer]
    #     Total number of distinct equivalence classes in this bucket.
    class LDiversityHistogramBucket; end
  end

  # Result of the reidentifiability analysis. Note that these results are an
  # estimation, not exact values.
  # @!attribute [rw] k_map_estimation_histogram
  #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::KMapEstimationResult::KMapEstimationHistogramBucket>]
  #     The intervals [min_anonymity, max_anonymity] do not overlap. If a value
  #     doesn't correspond to any such interval, the associated frequency is
  #     zero. For example, the following records:
  #       {min_anonymity: 1, max_anonymity: 1, frequency: 17}
  #       {min_anonymity: 2, max_anonymity: 3, frequency: 42}
  #       {min_anonymity: 5, max_anonymity: 10, frequency: 99}
  #     mean that there are no record with an estimated anonymity of 4, 5, or
  #     larger than 10.
  class KMapEstimationResult
    # A tuple of values for the quasi-identifier columns.
    # @!attribute [rw] quasi_ids_values
    #   @return [Array<Google::Privacy::Dlp::V2::Value>]
    #     The quasi-identifier values.
    # @!attribute [rw] estimated_anonymity
    #   @return [Integer]
    #     The estimated anonymity for these quasi-identifier values.
    class KMapEstimationQuasiIdValues; end

    # A KMapEstimationHistogramBucket message with the following values:
    #   min_anonymity: 3
    #   max_anonymity: 5
    #   frequency: 42
    # means that there are 42 records whose quasi-identifier values correspond
    # to 3, 4 or 5 people in the overlying population. An important particular
    # case is when min_anonymity = max_anonymity = 1: the frequency field then
    # corresponds to the number of uniquely identifiable records.
    # @!attribute [rw] min_anonymity
    #   @return [Integer]
    #     Always positive.
    # @!attribute [rw] max_anonymity
    #   @return [Integer]
    #     Always greater than or equal to min_anonymity.
    # @!attribute [rw] bucket_size
    #   @return [Integer]
    #     Number of records within these anonymity bounds.
    # @!attribute [rw] bucket_values
    #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::KMapEstimationResult::KMapEstimationQuasiIdValues>]
    #     Sample of quasi-identifier tuple values in this bucket. The total
    #     number of classes returned per bucket is capped at 20.
    # @!attribute [rw] bucket_value_count
    #   @return [Integer]
    #     Total number of distinct quasi-identifier tuple values in this bucket.
    class KMapEstimationHistogramBucket; end
  end

  # Result of the δ-presence computation. Note that these results are an
  # estimation, not exact values.
  # @!attribute [rw] delta_presence_estimation_histogram
  #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::DeltaPresenceEstimationResult::DeltaPresenceEstimationHistogramBucket>]
  #     The intervals [min_probability, max_probability) do not overlap. If a
  #     value doesn't correspond to any such interval, the associated frequency
  #     is zero. For example, the following records:
  #       {min_probability: 0, max_probability: 0.1, frequency: 17}
  #       {min_probability: 0.2, max_probability: 0.3, frequency: 42}
  #       {min_probability: 0.3, max_probability: 0.4, frequency: 99}
  #     mean that there are no record with an estimated probability in [0.1, 0.2)
  #     nor larger or equal to 0.4.
  class DeltaPresenceEstimationResult
    # A tuple of values for the quasi-identifier columns.
    # @!attribute [rw] quasi_ids_values
    #   @return [Array<Google::Privacy::Dlp::V2::Value>]
    #     The quasi-identifier values.
    # @!attribute [rw] estimated_probability
    #   @return [Float]
    #     The estimated probability that a given individual sharing these
    #     quasi-identifier values is in the dataset. This value, typically called
    #     δ, is the ratio between the number of records in the dataset with these
    #     quasi-identifier values, and the total number of individuals (inside
    #     *and* outside the dataset) with these quasi-identifier values.
    #     For example, if there are 15 individuals in the dataset who share the
    #     same quasi-identifier values, and an estimated 100 people in the entire
    #     population with these values, then δ is 0.15.
    class DeltaPresenceEstimationQuasiIdValues; end

    # A DeltaPresenceEstimationHistogramBucket message with the following
    # values:
    #   min_probability: 0.1
    #   max_probability: 0.2
    #   frequency: 42
    # means that there are 42 records for which δ is in [0.1, 0.2). An
    # important particular case is when min_probability = max_probability = 1:
    # then, every individual who shares this quasi-identifier combination is in
    # the dataset.
    # @!attribute [rw] min_probability
    #   @return [Float]
    #     Between 0 and 1.
    # @!attribute [rw] max_probability
    #   @return [Float]
    #     Always greater than or equal to min_probability.
    # @!attribute [rw] bucket_size
    #   @return [Integer]
    #     Number of records within these probability bounds.
    # @!attribute [rw] bucket_values
    #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::DeltaPresenceEstimationResult::DeltaPresenceEstimationQuasiIdValues>]
    #     Sample of quasi-identifier tuple values in this bucket. The total
    #     number of classes returned per bucket is capped at 20.
    # @!attribute [rw] bucket_value_count
    #   @return [Integer]
    #     Total number of distinct quasi-identifier tuple values in this bucket.
    class DeltaPresenceEstimationHistogramBucket; end
  end
end

#numerical_stats_result ⇒ Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::NumericalStatsResult

Returns:

• (Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::NumericalStatsResult)

# File 'lib/google/cloud/dlp/v2/doc/google/privacy/dlp/v2/dlp.rb', line 891

class AnalyzeDataSourceRiskDetails
  # Result of the numerical stats computation.
  # @!attribute [rw] min_value
  #   @return [Google::Privacy::Dlp::V2::Value]
  #     Minimum value appearing in the column.
  # @!attribute [rw] max_value
  #   @return [Google::Privacy::Dlp::V2::Value]
  #     Maximum value appearing in the column.
  # @!attribute [rw] quantile_values
  #   @return [Array<Google::Privacy::Dlp::V2::Value>]
  #     List of 99 values that partition the set of field values into 100 equal
  #     sized buckets.
  class NumericalStatsResult; end

  # Result of the categorical stats computation.
  # @!attribute [rw] value_frequency_histogram_buckets
  #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::CategoricalStatsResult::CategoricalStatsHistogramBucket>]
  #     Histogram of value frequencies in the column.
  class CategoricalStatsResult
    # @!attribute [rw] value_frequency_lower_bound
    #   @return [Integer]
    #     Lower bound on the value frequency of the values in this bucket.
    # @!attribute [rw] value_frequency_upper_bound
    #   @return [Integer]
    #     Upper bound on the value frequency of the values in this bucket.
    # @!attribute [rw] bucket_size
    #   @return [Integer]
    #     Total number of values in this bucket.
    # @!attribute [rw] bucket_values
    #   @return [Array<Google::Privacy::Dlp::V2::ValueFrequency>]
    #     Sample of value frequencies in this bucket. The total number of
    #     values returned per bucket is capped at 20.
    # @!attribute [rw] bucket_value_count
    #   @return [Integer]
    #     Total number of distinct values in this bucket.
    class CategoricalStatsHistogramBucket; end
  end

  # Result of the k-anonymity computation.
  # @!attribute [rw] equivalence_class_histogram_buckets
  #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::KAnonymityResult::KAnonymityHistogramBucket>]
  #     Histogram of k-anonymity equivalence classes.
  class KAnonymityResult
    # The set of columns' values that share the same ldiversity value
    # @!attribute [rw] quasi_ids_values
    #   @return [Array<Google::Privacy::Dlp::V2::Value>]
    #     Set of values defining the equivalence class. One value per
    #     quasi-identifier column in the original KAnonymity metric message.
    #     The order is always the same as the original request.
    # @!attribute [rw] equivalence_class_size
    #   @return [Integer]
    #     Size of the equivalence class, for example number of rows with the
    #     above set of values.
    class KAnonymityEquivalenceClass; end

    # @!attribute [rw] equivalence_class_size_lower_bound
    #   @return [Integer]
    #     Lower bound on the size of the equivalence classes in this bucket.
    # @!attribute [rw] equivalence_class_size_upper_bound
    #   @return [Integer]
    #     Upper bound on the size of the equivalence classes in this bucket.
    # @!attribute [rw] bucket_size
    #   @return [Integer]
    #     Total number of equivalence classes in this bucket.
    # @!attribute [rw] bucket_values
    #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::KAnonymityResult::KAnonymityEquivalenceClass>]
    #     Sample of equivalence classes in this bucket. The total number of
    #     classes returned per bucket is capped at 20.
    # @!attribute [rw] bucket_value_count
    #   @return [Integer]
    #     Total number of distinct equivalence classes in this bucket.
    class KAnonymityHistogramBucket; end
  end

  # Result of the l-diversity computation.
  # @!attribute [rw] sensitive_value_frequency_histogram_buckets
  #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::LDiversityResult::LDiversityHistogramBucket>]
  #     Histogram of l-diversity equivalence class sensitive value frequencies.
  class LDiversityResult
    # The set of columns' values that share the same ldiversity value.
    # @!attribute [rw] quasi_ids_values
    #   @return [Array<Google::Privacy::Dlp::V2::Value>]
    #     Quasi-identifier values defining the k-anonymity equivalence
    #     class. The order is always the same as the original request.
    # @!attribute [rw] equivalence_class_size
    #   @return [Integer]
    #     Size of the k-anonymity equivalence class.
    # @!attribute [rw] num_distinct_sensitive_values
    #   @return [Integer]
    #     Number of distinct sensitive values in this equivalence class.
    # @!attribute [rw] top_sensitive_values
    #   @return [Array<Google::Privacy::Dlp::V2::ValueFrequency>]
    #     Estimated frequencies of top sensitive values.
    class LDiversityEquivalenceClass; end

    # @!attribute [rw] sensitive_value_frequency_lower_bound
    #   @return [Integer]
    #     Lower bound on the sensitive value frequencies of the equivalence
    #     classes in this bucket.
    # @!attribute [rw] sensitive_value_frequency_upper_bound
    #   @return [Integer]
    #     Upper bound on the sensitive value frequencies of the equivalence
    #     classes in this bucket.
    # @!attribute [rw] bucket_size
    #   @return [Integer]
    #     Total number of equivalence classes in this bucket.
    # @!attribute [rw] bucket_values
    #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::LDiversityResult::LDiversityEquivalenceClass>]
    #     Sample of equivalence classes in this bucket. The total number of
    #     classes returned per bucket is capped at 20.
    # @!attribute [rw] bucket_value_count
    #   @return [Integer]
    #     Total number of distinct equivalence classes in this bucket.
    class LDiversityHistogramBucket; end
  end

  # Result of the reidentifiability analysis. Note that these results are an
  # estimation, not exact values.
  # @!attribute [rw] k_map_estimation_histogram
  #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::KMapEstimationResult::KMapEstimationHistogramBucket>]
  #     The intervals [min_anonymity, max_anonymity] do not overlap. If a value
  #     doesn't correspond to any such interval, the associated frequency is
  #     zero. For example, the following records:
  #       {min_anonymity: 1, max_anonymity: 1, frequency: 17}
  #       {min_anonymity: 2, max_anonymity: 3, frequency: 42}
  #       {min_anonymity: 5, max_anonymity: 10, frequency: 99}
  #     mean that there are no record with an estimated anonymity of 4, 5, or
  #     larger than 10.
  class KMapEstimationResult
    # A tuple of values for the quasi-identifier columns.
    # @!attribute [rw] quasi_ids_values
    #   @return [Array<Google::Privacy::Dlp::V2::Value>]
    #     The quasi-identifier values.
    # @!attribute [rw] estimated_anonymity
    #   @return [Integer]
    #     The estimated anonymity for these quasi-identifier values.
    class KMapEstimationQuasiIdValues; end

    # A KMapEstimationHistogramBucket message with the following values:
    #   min_anonymity: 3
    #   max_anonymity: 5
    #   frequency: 42
    # means that there are 42 records whose quasi-identifier values correspond
    # to 3, 4 or 5 people in the overlying population. An important particular
    # case is when min_anonymity = max_anonymity = 1: the frequency field then
    # corresponds to the number of uniquely identifiable records.
    # @!attribute [rw] min_anonymity
    #   @return [Integer]
    #     Always positive.
    # @!attribute [rw] max_anonymity
    #   @return [Integer]
    #     Always greater than or equal to min_anonymity.
    # @!attribute [rw] bucket_size
    #   @return [Integer]
    #     Number of records within these anonymity bounds.
    # @!attribute [rw] bucket_values
    #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::KMapEstimationResult::KMapEstimationQuasiIdValues>]
    #     Sample of quasi-identifier tuple values in this bucket. The total
    #     number of classes returned per bucket is capped at 20.
    # @!attribute [rw] bucket_value_count
    #   @return [Integer]
    #     Total number of distinct quasi-identifier tuple values in this bucket.
    class KMapEstimationHistogramBucket; end
  end

  # Result of the δ-presence computation. Note that these results are an
  # estimation, not exact values.
  # @!attribute [rw] delta_presence_estimation_histogram
  #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::DeltaPresenceEstimationResult::DeltaPresenceEstimationHistogramBucket>]
  #     The intervals [min_probability, max_probability) do not overlap. If a
  #     value doesn't correspond to any such interval, the associated frequency
  #     is zero. For example, the following records:
  #       {min_probability: 0, max_probability: 0.1, frequency: 17}
  #       {min_probability: 0.2, max_probability: 0.3, frequency: 42}
  #       {min_probability: 0.3, max_probability: 0.4, frequency: 99}
  #     mean that there are no record with an estimated probability in [0.1, 0.2)
  #     nor larger or equal to 0.4.
  class DeltaPresenceEstimationResult
    # A tuple of values for the quasi-identifier columns.
    # @!attribute [rw] quasi_ids_values
    #   @return [Array<Google::Privacy::Dlp::V2::Value>]
    #     The quasi-identifier values.
    # @!attribute [rw] estimated_probability
    #   @return [Float]
    #     The estimated probability that a given individual sharing these
    #     quasi-identifier values is in the dataset. This value, typically called
    #     δ, is the ratio between the number of records in the dataset with these
    #     quasi-identifier values, and the total number of individuals (inside
    #     *and* outside the dataset) with these quasi-identifier values.
    #     For example, if there are 15 individuals in the dataset who share the
    #     same quasi-identifier values, and an estimated 100 people in the entire
    #     population with these values, then δ is 0.15.
    class DeltaPresenceEstimationQuasiIdValues; end

    # A DeltaPresenceEstimationHistogramBucket message with the following
    # values:
    #   min_probability: 0.1
    #   max_probability: 0.2
    #   frequency: 42
    # means that there are 42 records for which δ is in [0.1, 0.2). An
    # important particular case is when min_probability = max_probability = 1:
    # then, every individual who shares this quasi-identifier combination is in
    # the dataset.
    # @!attribute [rw] min_probability
    #   @return [Float]
    #     Between 0 and 1.
    # @!attribute [rw] max_probability
    #   @return [Float]
    #     Always greater than or equal to min_probability.
    # @!attribute [rw] bucket_size
    #   @return [Integer]
    #     Number of records within these probability bounds.
    # @!attribute [rw] bucket_values
    #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::DeltaPresenceEstimationResult::DeltaPresenceEstimationQuasiIdValues>]
    #     Sample of quasi-identifier tuple values in this bucket. The total
    #     number of classes returned per bucket is capped at 20.
    # @!attribute [rw] bucket_value_count
    #   @return [Integer]
    #     Total number of distinct quasi-identifier tuple values in this bucket.
    class DeltaPresenceEstimationHistogramBucket; end
  end
end

#requested_privacy_metric ⇒ Google::Privacy::Dlp::V2::PrivacyMetric

Returns Privacy metric to compute.

Returns:

• (Google::Privacy::Dlp::V2::PrivacyMetric) —

  Privacy metric to compute.

# File 'lib/google/cloud/dlp/v2/doc/google/privacy/dlp/v2/dlp.rb', line 891

class AnalyzeDataSourceRiskDetails
  # Result of the numerical stats computation.
  # @!attribute [rw] min_value
  #   @return [Google::Privacy::Dlp::V2::Value]
  #     Minimum value appearing in the column.
  # @!attribute [rw] max_value
  #   @return [Google::Privacy::Dlp::V2::Value]
  #     Maximum value appearing in the column.
  # @!attribute [rw] quantile_values
  #   @return [Array<Google::Privacy::Dlp::V2::Value>]
  #     List of 99 values that partition the set of field values into 100 equal
  #     sized buckets.
  class NumericalStatsResult; end

  # Result of the categorical stats computation.
  # @!attribute [rw] value_frequency_histogram_buckets
  #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::CategoricalStatsResult::CategoricalStatsHistogramBucket>]
  #     Histogram of value frequencies in the column.
  class CategoricalStatsResult
    # @!attribute [rw] value_frequency_lower_bound
    #   @return [Integer]
    #     Lower bound on the value frequency of the values in this bucket.
    # @!attribute [rw] value_frequency_upper_bound
    #   @return [Integer]
    #     Upper bound on the value frequency of the values in this bucket.
    # @!attribute [rw] bucket_size
    #   @return [Integer]
    #     Total number of values in this bucket.
    # @!attribute [rw] bucket_values
    #   @return [Array<Google::Privacy::Dlp::V2::ValueFrequency>]
    #     Sample of value frequencies in this bucket. The total number of
    #     values returned per bucket is capped at 20.
    # @!attribute [rw] bucket_value_count
    #   @return [Integer]
    #     Total number of distinct values in this bucket.
    class CategoricalStatsHistogramBucket; end
  end

  # Result of the k-anonymity computation.
  # @!attribute [rw] equivalence_class_histogram_buckets
  #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::KAnonymityResult::KAnonymityHistogramBucket>]
  #     Histogram of k-anonymity equivalence classes.
  class KAnonymityResult
    # The set of columns' values that share the same ldiversity value
    # @!attribute [rw] quasi_ids_values
    #   @return [Array<Google::Privacy::Dlp::V2::Value>]
    #     Set of values defining the equivalence class. One value per
    #     quasi-identifier column in the original KAnonymity metric message.
    #     The order is always the same as the original request.
    # @!attribute [rw] equivalence_class_size
    #   @return [Integer]
    #     Size of the equivalence class, for example number of rows with the
    #     above set of values.
    class KAnonymityEquivalenceClass; end

    # @!attribute [rw] equivalence_class_size_lower_bound
    #   @return [Integer]
    #     Lower bound on the size of the equivalence classes in this bucket.
    # @!attribute [rw] equivalence_class_size_upper_bound
    #   @return [Integer]
    #     Upper bound on the size of the equivalence classes in this bucket.
    # @!attribute [rw] bucket_size
    #   @return [Integer]
    #     Total number of equivalence classes in this bucket.
    # @!attribute [rw] bucket_values
    #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::KAnonymityResult::KAnonymityEquivalenceClass>]
    #     Sample of equivalence classes in this bucket. The total number of
    #     classes returned per bucket is capped at 20.
    # @!attribute [rw] bucket_value_count
    #   @return [Integer]
    #     Total number of distinct equivalence classes in this bucket.
    class KAnonymityHistogramBucket; end
  end

  # Result of the l-diversity computation.
  # @!attribute [rw] sensitive_value_frequency_histogram_buckets
  #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::LDiversityResult::LDiversityHistogramBucket>]
  #     Histogram of l-diversity equivalence class sensitive value frequencies.
  class LDiversityResult
    # The set of columns' values that share the same ldiversity value.
    # @!attribute [rw] quasi_ids_values
    #   @return [Array<Google::Privacy::Dlp::V2::Value>]
    #     Quasi-identifier values defining the k-anonymity equivalence
    #     class. The order is always the same as the original request.
    # @!attribute [rw] equivalence_class_size
    #   @return [Integer]
    #     Size of the k-anonymity equivalence class.
    # @!attribute [rw] num_distinct_sensitive_values
    #   @return [Integer]
    #     Number of distinct sensitive values in this equivalence class.
    # @!attribute [rw] top_sensitive_values
    #   @return [Array<Google::Privacy::Dlp::V2::ValueFrequency>]
    #     Estimated frequencies of top sensitive values.
    class LDiversityEquivalenceClass; end

    # @!attribute [rw] sensitive_value_frequency_lower_bound
    #   @return [Integer]
    #     Lower bound on the sensitive value frequencies of the equivalence
    #     classes in this bucket.
    # @!attribute [rw] sensitive_value_frequency_upper_bound
    #   @return [Integer]
    #     Upper bound on the sensitive value frequencies of the equivalence
    #     classes in this bucket.
    # @!attribute [rw] bucket_size
    #   @return [Integer]
    #     Total number of equivalence classes in this bucket.
    # @!attribute [rw] bucket_values
    #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::LDiversityResult::LDiversityEquivalenceClass>]
    #     Sample of equivalence classes in this bucket. The total number of
    #     classes returned per bucket is capped at 20.
    # @!attribute [rw] bucket_value_count
    #   @return [Integer]
    #     Total number of distinct equivalence classes in this bucket.
    class LDiversityHistogramBucket; end
  end

  # Result of the reidentifiability analysis. Note that these results are an
  # estimation, not exact values.
  # @!attribute [rw] k_map_estimation_histogram
  #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::KMapEstimationResult::KMapEstimationHistogramBucket>]
  #     The intervals [min_anonymity, max_anonymity] do not overlap. If a value
  #     doesn't correspond to any such interval, the associated frequency is
  #     zero. For example, the following records:
  #       {min_anonymity: 1, max_anonymity: 1, frequency: 17}
  #       {min_anonymity: 2, max_anonymity: 3, frequency: 42}
  #       {min_anonymity: 5, max_anonymity: 10, frequency: 99}
  #     mean that there are no record with an estimated anonymity of 4, 5, or
  #     larger than 10.
  class KMapEstimationResult
    # A tuple of values for the quasi-identifier columns.
    # @!attribute [rw] quasi_ids_values
    #   @return [Array<Google::Privacy::Dlp::V2::Value>]
    #     The quasi-identifier values.
    # @!attribute [rw] estimated_anonymity
    #   @return [Integer]
    #     The estimated anonymity for these quasi-identifier values.
    class KMapEstimationQuasiIdValues; end

    # A KMapEstimationHistogramBucket message with the following values:
    #   min_anonymity: 3
    #   max_anonymity: 5
    #   frequency: 42
    # means that there are 42 records whose quasi-identifier values correspond
    # to 3, 4 or 5 people in the overlying population. An important particular
    # case is when min_anonymity = max_anonymity = 1: the frequency field then
    # corresponds to the number of uniquely identifiable records.
    # @!attribute [rw] min_anonymity
    #   @return [Integer]
    #     Always positive.
    # @!attribute [rw] max_anonymity
    #   @return [Integer]
    #     Always greater than or equal to min_anonymity.
    # @!attribute [rw] bucket_size
    #   @return [Integer]
    #     Number of records within these anonymity bounds.
    # @!attribute [rw] bucket_values
    #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::KMapEstimationResult::KMapEstimationQuasiIdValues>]
    #     Sample of quasi-identifier tuple values in this bucket. The total
    #     number of classes returned per bucket is capped at 20.
    # @!attribute [rw] bucket_value_count
    #   @return [Integer]
    #     Total number of distinct quasi-identifier tuple values in this bucket.
    class KMapEstimationHistogramBucket; end
  end

  # Result of the δ-presence computation. Note that these results are an
  # estimation, not exact values.
  # @!attribute [rw] delta_presence_estimation_histogram
  #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::DeltaPresenceEstimationResult::DeltaPresenceEstimationHistogramBucket>]
  #     The intervals [min_probability, max_probability) do not overlap. If a
  #     value doesn't correspond to any such interval, the associated frequency
  #     is zero. For example, the following records:
  #       {min_probability: 0, max_probability: 0.1, frequency: 17}
  #       {min_probability: 0.2, max_probability: 0.3, frequency: 42}
  #       {min_probability: 0.3, max_probability: 0.4, frequency: 99}
  #     mean that there are no record with an estimated probability in [0.1, 0.2)
  #     nor larger or equal to 0.4.
  class DeltaPresenceEstimationResult
    # A tuple of values for the quasi-identifier columns.
    # @!attribute [rw] quasi_ids_values
    #   @return [Array<Google::Privacy::Dlp::V2::Value>]
    #     The quasi-identifier values.
    # @!attribute [rw] estimated_probability
    #   @return [Float]
    #     The estimated probability that a given individual sharing these
    #     quasi-identifier values is in the dataset. This value, typically called
    #     δ, is the ratio between the number of records in the dataset with these
    #     quasi-identifier values, and the total number of individuals (inside
    #     *and* outside the dataset) with these quasi-identifier values.
    #     For example, if there are 15 individuals in the dataset who share the
    #     same quasi-identifier values, and an estimated 100 people in the entire
    #     population with these values, then δ is 0.15.
    class DeltaPresenceEstimationQuasiIdValues; end

    # A DeltaPresenceEstimationHistogramBucket message with the following
    # values:
    #   min_probability: 0.1
    #   max_probability: 0.2
    #   frequency: 42
    # means that there are 42 records for which δ is in [0.1, 0.2). An
    # important particular case is when min_probability = max_probability = 1:
    # then, every individual who shares this quasi-identifier combination is in
    # the dataset.
    # @!attribute [rw] min_probability
    #   @return [Float]
    #     Between 0 and 1.
    # @!attribute [rw] max_probability
    #   @return [Float]
    #     Always greater than or equal to min_probability.
    # @!attribute [rw] bucket_size
    #   @return [Integer]
    #     Number of records within these probability bounds.
    # @!attribute [rw] bucket_values
    #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::DeltaPresenceEstimationResult::DeltaPresenceEstimationQuasiIdValues>]
    #     Sample of quasi-identifier tuple values in this bucket. The total
    #     number of classes returned per bucket is capped at 20.
    # @!attribute [rw] bucket_value_count
    #   @return [Integer]
    #     Total number of distinct quasi-identifier tuple values in this bucket.
    class DeltaPresenceEstimationHistogramBucket; end
  end
end

#requested_source_table ⇒ Google::Privacy::Dlp::V2::BigQueryTable

Returns Input dataset to compute metrics over.

Returns:

• (Google::Privacy::Dlp::V2::BigQueryTable) —

  Input dataset to compute metrics over.

# File 'lib/google/cloud/dlp/v2/doc/google/privacy/dlp/v2/dlp.rb', line 891

class AnalyzeDataSourceRiskDetails
  # Result of the numerical stats computation.
  # @!attribute [rw] min_value
  #   @return [Google::Privacy::Dlp::V2::Value]
  #     Minimum value appearing in the column.
  # @!attribute [rw] max_value
  #   @return [Google::Privacy::Dlp::V2::Value]
  #     Maximum value appearing in the column.
  # @!attribute [rw] quantile_values
  #   @return [Array<Google::Privacy::Dlp::V2::Value>]
  #     List of 99 values that partition the set of field values into 100 equal
  #     sized buckets.
  class NumericalStatsResult; end

  # Result of the categorical stats computation.
  # @!attribute [rw] value_frequency_histogram_buckets
  #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::CategoricalStatsResult::CategoricalStatsHistogramBucket>]
  #     Histogram of value frequencies in the column.
  class CategoricalStatsResult
    # @!attribute [rw] value_frequency_lower_bound
    #   @return [Integer]
    #     Lower bound on the value frequency of the values in this bucket.
    # @!attribute [rw] value_frequency_upper_bound
    #   @return [Integer]
    #     Upper bound on the value frequency of the values in this bucket.
    # @!attribute [rw] bucket_size
    #   @return [Integer]
    #     Total number of values in this bucket.
    # @!attribute [rw] bucket_values
    #   @return [Array<Google::Privacy::Dlp::V2::ValueFrequency>]
    #     Sample of value frequencies in this bucket. The total number of
    #     values returned per bucket is capped at 20.
    # @!attribute [rw] bucket_value_count
    #   @return [Integer]
    #     Total number of distinct values in this bucket.
    class CategoricalStatsHistogramBucket; end
  end

  # Result of the k-anonymity computation.
  # @!attribute [rw] equivalence_class_histogram_buckets
  #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::KAnonymityResult::KAnonymityHistogramBucket>]
  #     Histogram of k-anonymity equivalence classes.
  class KAnonymityResult
    # The set of columns' values that share the same ldiversity value
    # @!attribute [rw] quasi_ids_values
    #   @return [Array<Google::Privacy::Dlp::V2::Value>]
    #     Set of values defining the equivalence class. One value per
    #     quasi-identifier column in the original KAnonymity metric message.
    #     The order is always the same as the original request.
    # @!attribute [rw] equivalence_class_size
    #   @return [Integer]
    #     Size of the equivalence class, for example number of rows with the
    #     above set of values.
    class KAnonymityEquivalenceClass; end

    # @!attribute [rw] equivalence_class_size_lower_bound
    #   @return [Integer]
    #     Lower bound on the size of the equivalence classes in this bucket.
    # @!attribute [rw] equivalence_class_size_upper_bound
    #   @return [Integer]
    #     Upper bound on the size of the equivalence classes in this bucket.
    # @!attribute [rw] bucket_size
    #   @return [Integer]
    #     Total number of equivalence classes in this bucket.
    # @!attribute [rw] bucket_values
    #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::KAnonymityResult::KAnonymityEquivalenceClass>]
    #     Sample of equivalence classes in this bucket. The total number of
    #     classes returned per bucket is capped at 20.
    # @!attribute [rw] bucket_value_count
    #   @return [Integer]
    #     Total number of distinct equivalence classes in this bucket.
    class KAnonymityHistogramBucket; end
  end

  # Result of the l-diversity computation.
  # @!attribute [rw] sensitive_value_frequency_histogram_buckets
  #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::LDiversityResult::LDiversityHistogramBucket>]
  #     Histogram of l-diversity equivalence class sensitive value frequencies.
  class LDiversityResult
    # The set of columns' values that share the same ldiversity value.
    # @!attribute [rw] quasi_ids_values
    #   @return [Array<Google::Privacy::Dlp::V2::Value>]
    #     Quasi-identifier values defining the k-anonymity equivalence
    #     class. The order is always the same as the original request.
    # @!attribute [rw] equivalence_class_size
    #   @return [Integer]
    #     Size of the k-anonymity equivalence class.
    # @!attribute [rw] num_distinct_sensitive_values
    #   @return [Integer]
    #     Number of distinct sensitive values in this equivalence class.
    # @!attribute [rw] top_sensitive_values
    #   @return [Array<Google::Privacy::Dlp::V2::ValueFrequency>]
    #     Estimated frequencies of top sensitive values.
    class LDiversityEquivalenceClass; end

    # @!attribute [rw] sensitive_value_frequency_lower_bound
    #   @return [Integer]
    #     Lower bound on the sensitive value frequencies of the equivalence
    #     classes in this bucket.
    # @!attribute [rw] sensitive_value_frequency_upper_bound
    #   @return [Integer]
    #     Upper bound on the sensitive value frequencies of the equivalence
    #     classes in this bucket.
    # @!attribute [rw] bucket_size
    #   @return [Integer]
    #     Total number of equivalence classes in this bucket.
    # @!attribute [rw] bucket_values
    #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::LDiversityResult::LDiversityEquivalenceClass>]
    #     Sample of equivalence classes in this bucket. The total number of
    #     classes returned per bucket is capped at 20.
    # @!attribute [rw] bucket_value_count
    #   @return [Integer]
    #     Total number of distinct equivalence classes in this bucket.
    class LDiversityHistogramBucket; end
  end

  # Result of the reidentifiability analysis. Note that these results are an
  # estimation, not exact values.
  # @!attribute [rw] k_map_estimation_histogram
  #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::KMapEstimationResult::KMapEstimationHistogramBucket>]
  #     The intervals [min_anonymity, max_anonymity] do not overlap. If a value
  #     doesn't correspond to any such interval, the associated frequency is
  #     zero. For example, the following records:
  #       {min_anonymity: 1, max_anonymity: 1, frequency: 17}
  #       {min_anonymity: 2, max_anonymity: 3, frequency: 42}
  #       {min_anonymity: 5, max_anonymity: 10, frequency: 99}
  #     mean that there are no record with an estimated anonymity of 4, 5, or
  #     larger than 10.
  class KMapEstimationResult
    # A tuple of values for the quasi-identifier columns.
    # @!attribute [rw] quasi_ids_values
    #   @return [Array<Google::Privacy::Dlp::V2::Value>]
    #     The quasi-identifier values.
    # @!attribute [rw] estimated_anonymity
    #   @return [Integer]
    #     The estimated anonymity for these quasi-identifier values.
    class KMapEstimationQuasiIdValues; end

    # A KMapEstimationHistogramBucket message with the following values:
    #   min_anonymity: 3
    #   max_anonymity: 5
    #   frequency: 42
    # means that there are 42 records whose quasi-identifier values correspond
    # to 3, 4 or 5 people in the overlying population. An important particular
    # case is when min_anonymity = max_anonymity = 1: the frequency field then
    # corresponds to the number of uniquely identifiable records.
    # @!attribute [rw] min_anonymity
    #   @return [Integer]
    #     Always positive.
    # @!attribute [rw] max_anonymity
    #   @return [Integer]
    #     Always greater than or equal to min_anonymity.
    # @!attribute [rw] bucket_size
    #   @return [Integer]
    #     Number of records within these anonymity bounds.
    # @!attribute [rw] bucket_values
    #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::KMapEstimationResult::KMapEstimationQuasiIdValues>]
    #     Sample of quasi-identifier tuple values in this bucket. The total
    #     number of classes returned per bucket is capped at 20.
    # @!attribute [rw] bucket_value_count
    #   @return [Integer]
    #     Total number of distinct quasi-identifier tuple values in this bucket.
    class KMapEstimationHistogramBucket; end
  end

  # Result of the δ-presence computation. Note that these results are an
  # estimation, not exact values.
  # @!attribute [rw] delta_presence_estimation_histogram
  #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::DeltaPresenceEstimationResult::DeltaPresenceEstimationHistogramBucket>]
  #     The intervals [min_probability, max_probability) do not overlap. If a
  #     value doesn't correspond to any such interval, the associated frequency
  #     is zero. For example, the following records:
  #       {min_probability: 0, max_probability: 0.1, frequency: 17}
  #       {min_probability: 0.2, max_probability: 0.3, frequency: 42}
  #       {min_probability: 0.3, max_probability: 0.4, frequency: 99}
  #     mean that there are no record with an estimated probability in [0.1, 0.2)
  #     nor larger or equal to 0.4.
  class DeltaPresenceEstimationResult
    # A tuple of values for the quasi-identifier columns.
    # @!attribute [rw] quasi_ids_values
    #   @return [Array<Google::Privacy::Dlp::V2::Value>]
    #     The quasi-identifier values.
    # @!attribute [rw] estimated_probability
    #   @return [Float]
    #     The estimated probability that a given individual sharing these
    #     quasi-identifier values is in the dataset. This value, typically called
    #     δ, is the ratio between the number of records in the dataset with these
    #     quasi-identifier values, and the total number of individuals (inside
    #     *and* outside the dataset) with these quasi-identifier values.
    #     For example, if there are 15 individuals in the dataset who share the
    #     same quasi-identifier values, and an estimated 100 people in the entire
    #     population with these values, then δ is 0.15.
    class DeltaPresenceEstimationQuasiIdValues; end

    # A DeltaPresenceEstimationHistogramBucket message with the following
    # values:
    #   min_probability: 0.1
    #   max_probability: 0.2
    #   frequency: 42
    # means that there are 42 records for which δ is in [0.1, 0.2). An
    # important particular case is when min_probability = max_probability = 1:
    # then, every individual who shares this quasi-identifier combination is in
    # the dataset.
    # @!attribute [rw] min_probability
    #   @return [Float]
    #     Between 0 and 1.
    # @!attribute [rw] max_probability
    #   @return [Float]
    #     Always greater than or equal to min_probability.
    # @!attribute [rw] bucket_size
    #   @return [Integer]
    #     Number of records within these probability bounds.
    # @!attribute [rw] bucket_values
    #   @return [Array<Google::Privacy::Dlp::V2::AnalyzeDataSourceRiskDetails::DeltaPresenceEstimationResult::DeltaPresenceEstimationQuasiIdValues>]
    #     Sample of quasi-identifier tuple values in this bucket. The total
    #     number of classes returned per bucket is capped at 20.
    # @!attribute [rw] bucket_value_count
    #   @return [Integer]
    #     Total number of distinct quasi-identifier tuple values in this bucket.
    class DeltaPresenceEstimationHistogramBucket; end
  end
end