PII Replacement

PII (Personally Identifiable Information) replacement is a critical privacy protection step that detects and replaces sensitive information in your datasets before synthesis. This ensures that the model never has the opportunity to learn the most sensitive information (e.g. names, addresses, identifiers) from the training data.

How It Works

The PII replacement pipeline operates in multiple stages:

1. Detection: Classifies PII entities within free text and entire columns.
2. Replacement: Substitutes PII using configurable rules.

Detection Methods

NeMo Safe Synthesizer supports multiple PII detection approaches described in the table below:

Method	Scope	Description	Key Features
LLM Classification	Entire columns	Leverages language models for column classification when the entire column is a single entity	- Contextual understanding of entities - Handles complex PII patterns - Flexible entity definitions - Configurable prompts and models
GLiNER PII	Free text	Uses the GLiNER PII model for entity recognition within free text columns	- Zero-shot entity detection - Supports custom entity types - High accuracy for standard PII categories - Configurable confidence thresholds

Replacement Methods

After detection, PII can be handled in multiple ways:

Strategy	Description	Example
Annotate	Add identified entity to original PII	Alice → <entity type="first_name" value="Alice">
Redact	Replace PII with a generic tag	Alice → <first_name>
Hash	Replace PII with a hashed value	Alice → 3bf676c57
Substitute	Replace PII with a context-relevant alternative	Alice → Erica

Supported Entity Types

GLiNER PII will attempt to identify any custom entity type you provide. However, it has specifically been fine-tuned to detect the following entities, organized by category:

Default entity set

The default configuration detects and replaces a focused subset of these entities: first_name, last_name, name, street_address, city, state, postcode, address, phone_number, fax_number, email, ssn, national_id, tax_id, and credit_debit_card. To detect additional entity types, add them to replace_pii.globals.classify.entities and replace_pii.globals.ner.ner_entities in your configuration. Also add replacement steps to replace_pii.steps.

Personal Information

• first_name - Given names
• last_name - Surnames and family names
• name - Full names
• age - Ages
• email - Email addresses
• phone_number - Phone numbers in various formats
• fax_number - Fax numbers in various formats

Addresses

• address - Complete physical addresses (for example, 123 Main Street, Anytown, CA 90210)
• street_address - Street addresses (for example, 123 Main Street)
• city - City names
• county - County names
• state - State/province names
• postcode - Postal/ZIP codes
• country - Country names

Personal Identifiers

• ssn - Social Security Numbers
• national_id - National ID numbers
• tax_id - Tax ID numbers
• certificate_license_number - Driver’s license numbers
• unique_identifier - Generic unique IDs
• customer_id - Customer identifiers
• employee_id - Employee identifiers

Financial Information

• credit_debit_card - Credit and debit card numbers
• cvv - Credit card verification code
• pin - Personal identification numbers
• account_number - Bank account numbers
• bank_routing_number - Bank routing numbers
• swift_bic - Swift/BIC codes
• iban - International bank account numbers

Medical Information

• medical_record_number - Medical record numbers
• health_plan_beneficiary_number - Insurance IDs
• biometric_identifier - Biometric data references

Technical Identifiers

• url - Web URLs
• ipv4 - IPv4 addresses
• ipv6 - IPv6 addresses
• mac_address - Hardware MAC addresses
• api_key - API keys and tokens
• user_name - Usernames
• password - Passwords
• http_cookie - HTTP Cookies
• device_identifier - Device IDs

Vehicle Identifiers

• vehicle_identifier - Vehicle identification numbers (VINs)
• license_plate - License plates

Geographic Information

• latitude - Latitude coordinates
• longitude - Longitude coordinates
• coordinate - Coordinate pairs

Quasi Identifiers

• date - Date values
• date_time - Date and time values
• blood_type - Blood type information
• gender - Gender information
• sexuality - Sexual orientation
• political_view - Political affiliations
• race - Race
• ethnicity - Ethnicity information
• religious_belief - Religious affiliations
• language - Language preferences
• education - Education level
• job_title - Professional titles
• employment_status - Employment information
• company_name - Organization names

Custom Entity Types

Beyond these built-in types, you can define custom entities using:

Custom entity configuration

globals:
  classify:
    enable_classify: true
    entities:
      - first_name
      - last_name
      - email
      - employee_id
      - project_code

Configuration

PII replacement is configured through the replace_pii section. For the full schema, refer to Configuration Reference -- Replacing PII.

replace_pii section

replace_pii:
  globals:
    locales:
      - en_US
  steps:
    - rows:
        update:
          - entity:
              - email
              - phone_number
            value: "column.entity | fake"

When to Use PII Replacement

Consider using PII replacement when:

• Your data contains names, addresses, or other direct identifiers
• Compliance requires PII removal before processing
• You want to ensure the model cannot memorize sensitive values
• You need to share synthetic data with external parties

PII replacement is on by default as a pre-processing step before synthesis.