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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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  • General Datetime Transformers
  • Differential Privacy Transformers
  1. Transformers Glossary

Datetime

Previous❖ DPWeightedResponseRandomizerNextOptimizedTimestampEncoder

Last updated 18 days ago

Datetime columns indicate a point of time. This can be at any granularity: to the nearest day, minute, second or even nanosecond. Typically, the datetime will be represented as a string.

Always double check to see if the datetime columns are being detected. A datetime column might be incorrectly detected as categorical if it's in a non-standard format.

General Datetime Transformers

These transformers encode your datetime data as numerical values, ready for data science and machine learning.

Differential Privacy Transformers

These transformers use differential privacy techniques to add noise or reshape your column of datetime data. As a result, your column — and any statistics about it — can be shared with differential privacy guarantees.

Encode datetimes as numerical values based on the Unix timestamp.

Encode datetimes as numerical values using the least common denominator.

Privatize the data by adding Laplacian noise

❖

UnixTimestampEncoder
OptimizedTimestamp Encoder
DPTimestampLaplaceNoiser
For example, you might be storing the last day users have logged into your site.

❖ SDV Enterprise Bundle. This feature is available as part of the Differential Privacy Bundle, an optional add-on to SDV Enterprise. For more information, please visit the page. Coming soon!

Differential Privacy Bundle