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Data Anonymization

The YData SDK provides powerful tools for data anonymization, ensuring privacy while maintaining data utility. This section covers the various anonymization techniques, privacy metrics, and best practices for protecting sensitive information in your datasets.

Overview

Data anonymization is crucial for:

  • Protecting Personally Identifiable Information (PII)
  • Ensuring regulatory compliance (GDPR, CCPA, HIPAA)
  • Enabling safe data sharing and analysis
  • Maintaining data utility for business purposes

Core Features

1. Privacy Protection

  • PII Detection
  • Automatic identification of sensitive fields
  • Support for multiple data types (text, numeric, categorical)
  • Custom pattern recognition

  • Regular expression matching

  • Data Masking

  • Field-level masking
  • Partial masking options
  • Custom masking patterns
  • Format preservation

2. Utility Preservation

Data Quality

  • Statistical Properties
  • Mean preservation
  • Variance preservation
  • Distribution preservation

  • Correlation preservation

  • Business Value

  • Domain-specific rules
  • Value relationships
  • Business constraints
  • Custom utility rules

Getting Started Examples

For practical examples of using anonymization features, check out our Getting Started guides: