Class: Google::Apis::DlpV2beta1::GooglePrivacyDlpV2beta1PrivacyMetric

Inherits:
Object
  • Object
show all
Includes:
Core::Hashable, Core::JsonObjectSupport
Defined in:
generated/google/apis/dlp_v2beta1/classes.rb,
generated/google/apis/dlp_v2beta1/representations.rb,
generated/google/apis/dlp_v2beta1/representations.rb

Overview

Privacy metric to compute for reidentification risk analysis.

Instance Attribute Summary collapse

Instance Method Summary collapse

Methods included from Core::JsonObjectSupport

#to_json

Methods included from Core::Hashable

process_value, #to_h

Constructor Details

#initialize(**args) ⇒ GooglePrivacyDlpV2beta1PrivacyMetric

Returns a new instance of GooglePrivacyDlpV2beta1PrivacyMetric



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# File 'generated/google/apis/dlp_v2beta1/classes.rb', line 2686

def initialize(**args)
   update!(**args)
end

Instance Attribute Details

#categorical_stats_config ⇒ Google::Apis::DlpV2beta1::GooglePrivacyDlpV2beta1CategoricalStatsConfig

Compute numerical stats over an individual column, including number of distinct values and value count distribution. Corresponds to the JSON property categoricalStatsConfig

Returns:



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# File 'generated/google/apis/dlp_v2beta1/classes.rb', line 2658

def categorical_stats_config
  @categorical_stats_config
end

#k_anonymity_config ⇒ Google::Apis::DlpV2beta1::GooglePrivacyDlpV2beta1KAnonymityConfig

k-anonymity metric, used for analysis of reidentification risk. Corresponds to the JSON property kAnonymityConfig

Returns:



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# File 'generated/google/apis/dlp_v2beta1/classes.rb', line 2663

def k_anonymity_config
  @k_anonymity_config
end

#k_map_estimation_config ⇒ Google::Apis::DlpV2beta1::GooglePrivacyDlpV2beta1KMapEstimationConfig

Reidentifiability metric. This corresponds to a risk model similar to what is called "journalist risk" in the literature, except the attack dataset is statistically modeled instead of being perfectly known. This can be done using publicly available data (like the US Census), or using a custom statistical model (indicated as one or several BigQuery tables), or by extrapolating from the distribution of values in the input dataset. Corresponds to the JSON property kMapEstimationConfig

Returns:



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# File 'generated/google/apis/dlp_v2beta1/classes.rb', line 2673

def k_map_estimation_config
  @k_map_estimation_config
end

#l_diversity_config ⇒ Google::Apis::DlpV2beta1::GooglePrivacyDlpV2beta1LDiversityConfig

l-diversity metric, used for analysis of reidentification risk. Corresponds to the JSON property lDiversityConfig

Returns:



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# File 'generated/google/apis/dlp_v2beta1/classes.rb', line 2678

def l_diversity_config
  @l_diversity_config
end

#numerical_stats_config ⇒ Google::Apis::DlpV2beta1::GooglePrivacyDlpV2beta1NumericalStatsConfig

Compute numerical stats over an individual column, including min, max, and quantiles. Corresponds to the JSON property numericalStatsConfig

Returns:



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# File 'generated/google/apis/dlp_v2beta1/classes.rb', line 2684

def numerical_stats_config
  @numerical_stats_config
end

Instance Method Details

#update!(**args) ⇒ Object

Update properties of this object



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# File 'generated/google/apis/dlp_v2beta1/classes.rb', line 2691

def update!(**args)
  @categorical_stats_config = args[:categorical_stats_config] if args.key?(:categorical_stats_config)
  @k_anonymity_config = args[:k_anonymity_config] if args.key?(:k_anonymity_config)
  @k_map_estimation_config = args[:k_map_estimation_config] if args.key?(:k_map_estimation_config)
  @l_diversity_config = args[:l_diversity_config] if args.key?(:l_diversity_config)
  @numerical_stats_config = args[:numerical_stats_config] if args.key?(:numerical_stats_config)
end