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  • RDT: Reversible Data Transforms
  • Getting Started
    • Installation
    • Quickstart
  • Usage
    • Basic Concepts
    • HyperTransformer
      • Preparation
      • Configuration
      • Transformation
  • Transformers Glossary
    • Numerical
      • ClusterBasedNormalizer
      • FloatFormatter
      • GaussianNormalizer
      • LogScaler
      • LogitScaler
      • * OutlierEncoder
      • ❖ DPECDFNormalizer
      • ❖ DPLaplaceNoiser
      • ❖ ECDFNormalizer
      • ❖ XGaussianNormalizer
    • Categorical
      • LabelEncoder
      • OrderedLabelEncoder
      • FrequencyEncoder
      • OneHotEncoder
      • OrderedUniformEncoder
      • UniformEncoder
      • BinaryEncoder
      • ❖ DPDiscreteECDFNormalizer
      • ❖ DPResponseRandomizer
      • ❖ DPWeightedResponseRandomizer
    • Datetime
      • OptimizedTimestampEncoder
      • UnixTimestampEncoder
      • ❖ DPTimestampLaplaceNoiser
    • ID
      • AnonymizedFaker
      • IndexGenerator
      • RegexGenerator
      • Treat IDs as categorical labels
    • Generic PII Anonymization
      • AnonymizedFaker
      • PseudoAnonymizedFaker
    • * Deep Data Understanding
      • * Address
        • * RandomLocationGenerator
        • * RegionalAnonymizer
      • * Email
        • * DomainBasedAnonymizer
        • * DomainBasedMapper
        • * DomainExtractor
      • * GPS Coordinates
        • * RandomLocationGenerator
        • * GPSNoiser
        • * MetroAreaAnonymizer
      • * Phone Number
        • * AnonymizedGeoExtractor
        • * NewNumberMapper
        • * GeoExtractor
  • Resources
    • Use Cases
      • Contextual Anonymization
      • Differential Privacy
      • Statistical Preprocessing
    • For Businesses
    • For Developers
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  1. Transformers Glossary

* Deep Data Understanding

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Last updated 6 months ago

Do you have other types of data that have particular meanings in your businesses? These transformers understand real world concepts, and parse out their meanings for increased data modeling accuracy.

Use Contextual Anonymization for sensitive data. Contextual Anonymization is a novel technique invented at DataCebo to anonymize data with more nuanced meaning.

Contextual Anonymization combines the ability to anonymize Personal Identifiable Information (PII) while preserving underlying meaning of the data (deep data understanding).

Explore the Data Types

We offer deep data understanding and contextual anonymization for the data types listed below.

  • * : Physical locations around the world, for example an office or personal mailing address

  • * : email addresses associated with real users, businesses or other entities

  • * : precise locations around the world, defined by a latitude and longitude

  • * : cell phone or landline numbers

-> Learn more
Address
Email
GPS Coordinates
Phone Number

*SDV Enterprise Feature. This feature is available to our licensed users and is not currently in our public library. For more information, visit our page to .

Explore SDV