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  • Tonic Structural User Guide
  • About Tonic Structural
    • Structural data generation workflow
    • Structural deployment types
    • Structural implementation roles
    • Structural license plans
  • Logging into Structural for the first time
  • Getting started with the Structural free trial
  • Managing your user account
  • Frequently Asked Questions
  • Tutorial videos
  • Creating and managing workspaces
    • Managing workspaces
      • Viewing your list of workspaces
      • Creating, editing, or deleting a workspace
      • Workspace configuration settings
        • Workspace identification and connection type
        • Data connection settings
        • Configuring secrets managers for database connections
        • Data generation settings
        • Enabling and configuring upsert
        • Writing output to Tonic Ephemeral
        • Writing output to a container repository
        • Advanced workspace overrides
      • About the workspace management view
      • About workspace inheritance
      • Assigning tags to a workspace
      • Exporting and importing the workspace configuration
    • Managing access to workspaces
      • Sharing workspace access
      • Transferring ownership of a workspace
    • Viewing workspace jobs and job details
  • Configuring data generation
    • Privacy Hub
    • Database View
      • Viewing and configuring tables
      • Viewing the column list
      • Displaying sample data for a column
      • Configuring an individual column
      • Configuring multiple columns
      • Identifying similar columns
      • Commenting on columns
    • Table View
    • Working with document-based data
      • Performing scans on collections
      • Using Collection View
    • Identifying sensitive data
      • Running the Structural sensitivity scan
      • Manually indicating whether a column is sensitive
      • Built-in sensitivity types that Structural detects
      • Creating and managing custom sensitivity rules
    • Table modes
    • Generator information
      • Generator summary
      • Generator reference
        • Address
        • Algebraic
        • Alphanumeric String Key
        • Array Character Scramble
        • Array JSON Mask
        • Array Regex Mask
        • ASCII Key
        • Business Name
        • Categorical
        • Character Scramble
        • Character Substitution
        • Company Name
        • Conditional
        • Constant
        • Continuous
        • Cross Table Sum
        • CSV Mask
        • Custom Categorical
        • Date Truncation
        • Email
        • Event Timestamps
        • File Name
        • Find and Replace
        • FNR
        • Geo
        • HIPAA Address
        • Hostname
        • HStore Mask
        • HTML Mask
        • Integer Key
        • International Address
        • IP Address
        • JSON Mask
        • MAC Address
        • Mongo ObjectId Key
        • Name
        • Noise Generator
        • Null
        • Numeric String Key
        • Passthrough
        • Phone
        • Random Boolean
        • Random Double
        • Random Hash
        • Random Integer
        • Random Timestamp
        • Random UUID
        • Regex Mask
        • Sequential Integer
        • Shipping Container
        • SIN
        • SSN
        • Struct Mask
        • Timestamp Shift Generator
        • Unique Email
        • URL
        • UUID Key
        • XML Mask
      • Generator characteristics
        • Enabling consistency
        • Linking generators
        • Differential privacy
        • Partitioning a column
        • Data-free generators
        • Supporting uniqueness constraints
        • Format-preserving encryption (FPE)
      • Generator types
        • Composite generators
        • Primary key generators
    • Generator assignment and configuration
      • Reviewing and applying recommended generators
      • Assigning and configuring generators
      • Document View for file connector JSON columns
      • Generator hints and tips
      • Managing generator presets
      • Configuring and using Structural data encryption
      • Custom value processors
    • Subsetting data
      • About subsetting
      • Using table filtering for data warehouses and Spark-based data connectors
      • Viewing the current subsetting configuration
      • Subsetting and foreign keys
      • Configuring subsetting
      • Viewing and managing configuration inheritance
      • Viewing the subset creation steps
      • Viewing previous subsetting data generation runs
      • Generating cohesive subset data from related databases
      • Other subsetting hints and tips
    • Viewing and adding foreign keys
    • Viewing and resolving schema changes
    • Tracking changes to workspaces, generator presets, and sensitivity rules
    • Using the Privacy Report to verify data protection
  • Running data generation
    • Running data generation jobs
      • Types of data generation
      • Data generation process
      • Running data generation manually
      • Scheduling data generation
      • Issues that prevent data generation
    • Managing data generation performance
    • Viewing and downloading container artifacts
    • Post-job scripts
    • Webhooks
  • Installing and Administering Structural
    • Structural architecture
    • Using Structural securely
    • Deploying a self-hosted Structural instance
      • Deployment checklist
      • System requirements
      • Deploying with Docker Compose
      • Deploying on Kubernetes with Helm
      • Enabling the option to write output data to a container repository
        • Setting up a Kubernetes cluster to use to write output data to a container repository
        • Required access to write destination data to a container repository
      • Entering and updating your license key
      • Setting up host integration
      • Working with the application database
      • Setting up a secret
      • Setting a custom certificate
    • Using Structural Cloud
      • Structural Cloud notes
      • Setting up and managing a Structural Cloud pay-as-you-go subscription
      • Structural Cloud onboarding
    • Managing user access to Structural
      • Structural organizations
      • Determining whether users can create accounts
      • Creating a new account in an existing organization
      • Single sign-on (SSO)
        • Structural user authentication with SSO
        • Enabling and configuring SSO on Structural Cloud
        • Synchronizing SSO groups with Structural
        • Viewing the list of SSO groups in Tonic Structural
        • AWS IAM Identity Center
        • Duo
        • GitHub
        • Google
        • Keycloak
        • Microsoft Entra ID (previously Azure Active Directory)
        • Okta
        • OpenID Connect (OIDC)
        • SAML
      • Managing Structural users
      • Managing permissions
        • About permission sets
        • Built-in permission sets
        • Available permissions
        • Viewing the lists of global and workspace permission sets
        • Configuring custom permission sets
        • Selecting default permission sets
        • Configuring access to global permission sets
        • Setting initial access to all global permissions
        • Granting Account Admin access for a Structural Cloud organization
    • Structural monitoring and logging
      • Monitoring Structural services
      • Performing health checks
      • Downloading the usage report
      • Tracking user access and permissions
      • Redacted and diagnostic (unredacted) logs
      • Data that Tonic.ai collects
      • Verifying and enabling telemetry sharing
    • Configuring environment settings
    • Updating Structural
  • Connecting to your data
    • About data connectors
    • Overview for database administrators
    • Data connector summary
    • Amazon DynamoDB
      • System requirements and limitations for DynamoDB
      • Structural differences and limitations with DynamoDB
      • Before you create a DynamoDB workspace
      • Configuring DynamoDB workspace data connections
    • Amazon EMR
      • Structural process overview for Amazon EMR
      • System requirements for Amazon EMR
      • Structural differences and limitations with Amazon EMR
      • Before you create an Amazon EMR workspace
        • Creating IAM roles for Structural and Amazon EMR
        • Creating Athena workgroups
        • Configuration for cross-account setups
      • Configuring Amazon EMR workspace data connections
    • Amazon Redshift
      • Structural process overview for Amazon Redshift
      • Structural differences and limitations with Amazon Redshift
      • Before you create an Amazon Redshift workspace
        • Required AWS instance profile permissions for Amazon Redshift
        • Setting up the AWS Lambda role for Amazon Redshift
        • AWS KMS permissions for Amazon SQS message encryption
        • Amazon Redshift-specific Structural environment settings
        • Source and destination database permissions for Amazon Redshift
      • Configuring Amazon Redshift workspace data connections
    • Databricks
      • Structural process overview for Databricks
      • System requirements for Databricks
      • Structural differences and limitations with Databricks
      • Before you create a Databricks workspace
        • Granting access to storage
        • Setting up your Databricks cluster
        • Configuring the destination database schema creation
      • Configuring Databricks workspace data connections
    • Db2 for LUW
      • System requirements for Db2 for LUW
      • Structural differences and limitations with Db2 for LUW
      • Before you create a Db2 for LUW workspace
      • Configuring Db2 for LUW workspace data connections
    • File connector
      • Overview of the file connector process
      • Supported file and content types
      • Structural differences and limitations with the file connector
      • Before you create a file connector workspace
      • Configuring the file connector storage type and output options
      • Managing file groups in a file connector workspace
      • Downloading generated file connector files
    • Google BigQuery
      • Structural differences and limitations with Google BigQuery
      • Before you create a Google BigQuery workspace
      • Configuring Google BigQuery workspace data connections
      • Resolving schema changes for de-identified views
    • MongoDB
      • System requirements for MongoDB
      • Structural differences and limitations with MongoDB
      • Configuring MongoDB workspace data connections
      • Other MongoDB hints and tips
    • MySQL
      • System requirements for MySQL
      • Before you create a MySQL workspace
      • Configuring MySQL workspace data connections
    • Oracle
      • Known limitations for Oracle schema objects
      • System requirements for Oracle
      • Structural differences and limitations with Oracle
      • Before you create an Oracle workspace
      • Configuring Oracle workspace data connections
    • PostgreSQL
      • System requirements for PostgreSQL
      • Before you create a PostgreSQL workspace
      • Configuring PostgreSQL workspace data connections
    • Salesforce
      • System requirements for Salesforce
      • Structural differences and limitations with Salesforce
      • Before you create a Salesforce workspace
      • Configuring Salesforce workspace data connections
    • Snowflake on AWS
      • Structural process overviews for Snowflake on AWS
      • Structural differences and limitations with Snowflake on AWS
      • Before you create a Snowflake on AWS workspace
        • Required AWS instance profile permissions for Snowflake on AWS
        • Other configuration for Lambda processing
        • Source and destination database permissions for Snowflake on AWS
        • Configuring whether Structural creates the Snowflake on AWS destination database schema
      • Configuring Snowflake on AWS workspace data connections
    • Snowflake on Azure
      • Structural process overview for Snowflake on Azure
      • Structural differences and limitations with Snowflake on Azure
      • Before you create a Snowflake on Azure workspace
      • Configuring Snowflake on Azure workspace data connections
    • Spark SDK
      • Structural process overview for the Spark SDK
      • Structural differences and limitations with the Spark SDK
      • Configuring Spark SDK workspace data connections
      • Using Spark to run de-identification of the data
    • SQL Server
      • System requirements for SQL Server
      • Before you create a SQL Server workspace
      • Configuring SQL Server workspace data connections
    • Yugabyte
      • System requirements for Yugabyte
      • Structural differences and limitations with Yugabyte
      • Before you create a Yugabyte workspace
      • Configuring Yugabyte workspace data connections
      • Troubleshooting Yugabyte data generation issues
  • Using the Structural API
    • About the Structural API
    • Getting an API token
    • Getting the workspace ID
    • Using the Structural API to perform tasks
      • Configure environment settings
      • Manage generator presets
        • Retrieving the list of generator presets
        • Structure of a generator preset
        • Creating a custom generator preset
        • Updating an existing generator preset
        • Deleting a generator preset
      • Manage custom sensitivity rules
      • Create a workspace
      • Connect to source and destination data
      • Manage file groups in a file connector workspace
      • Assign table modes and filters to source database tables
      • Set column sensitivity
      • Assign generators to columns
        • Getting the generator IDs and available metadata
        • Updating generator configurations
        • Structure of a generator assignment
        • Generator API reference
          • Address (AddressGenerator)
          • Algebraic (AlgebraicGenerator)
          • Alphanumeric String Key (AlphaNumericPkGenerator)
          • Array Character Scramble (ArrayTextMaskGenerator)
          • Array JSON Mask (ArrayJsonMaskGenerator)
          • Array Regex Mask (ArrayRegexMaskGenerator)
          • ASCII Key (AsciiPkGenerator)
          • Business Name (BusinessNameGenerator)
          • Categorical (CategoricalGenerator)
          • Character Scramble (TextMaskGenerator)
          • Character Substitution (StringMaskGenerator)
          • Company Name (CompanyNameGenerator)
          • Conditional (ConditionalGenerator)
          • Constant (ConstantGenerator)
          • Continuous (GaussianGenerator)
          • Cross Table Sum (CrossTableAggregateGenerator)
          • CSV Mask (CsvMaskGenerator)
          • Custom Categorical (CustomCategoricalGenerator)
          • Date Truncation (DateTruncationGenerator)
          • Email (EmailGenerator)
          • Event Timestamps (EventGenerator)
          • File Name (FileNameGenerator)
          • Find and Replace (FindAndReplaceGenerator)
          • FNR (FnrGenerator)
          • Geo (GeoGenerator)
          • HIPAA Address (HipaaAddressGenerator)
          • Hostname (HostnameGenerator)
          • HStore Mask (HStoreMaskGenerator)
          • HTML Mask (HtmlMaskGenerator)
          • Integer Key (IntegerPkGenerator)
          • International Address (InternationalAddressGenerator)
          • IP Address (IPAddressGenerator)
          • JSON Mask (JsonMaskGenerator)
          • MAC Address (MACAddressGenerator)
          • Mongo ObjectId Key (ObjectIdPkGenerator)
          • Name (NameGenerator)
          • Noise Generator (NoiseGenerator)
          • Null (NullGenerator)
          • Numeric String Key (NumericStringPkGenerator)
          • Passthrough (PassthroughGenerator)
          • Phone (USPhoneNumberGenerator)
          • Random Boolean (RandomBooleanGenerator)
          • Random Double (RandomDoubleGenerator)
          • Random Hash (RandomStringGenerator)
          • Random Integer (RandomIntegerGenerator)
          • Random Timestamp (RandomTimestampGenerator)
          • Random UUID (UUIDGenerator)
          • Regex Mask (RegexMaskGenerator)
          • Sequential Integer (UniqueIntegerGenerator)
          • Shipping Container (ShippingContainerGenerator)
          • SIN (SINGenerator)
          • SSN (SsnGenerator)
          • Struct Mask (StructMaskGenerator)
          • Timestamp Shift (TimestampShiftGenerator)
          • Unique Email (UniqueEmailGenerator)
          • URL (UrlGenerator)
          • UUID Key (UuidPkGenerator)
          • XML Mask (XmlMaskGenerator)
      • Configure subsetting
      • Check for and resolve schema changes
      • Run data generation jobs
      • Schedule data generation jobs
    • Example script: Starting a data generation job
    • Example script: Polling for a job status and creating a Docker package
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  1. Configuring data generation
  2. Generator information

Generator summary

Last updated 1 month ago

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The following table summarizes the available generators. The table includes generator characteristics that you might take into account when you select the generator to use for a column.

Generator hints and tips also provides some suggestions for generators to use for specific use cases.

Information in the table

The generator summary includes the following columns:

  • Generator - The name of the generator, linked to the entry in the .

  • Description - An overview description of the generator.

  • Supported features - Includes the following information:

    • The that the generator supports

    • Whether the generator is a or a

    • The generator

Generator
Description
Supported features

Generates replacement values for U.S. mailing addresses. You select the address component or format for the replacement values. For example, the column might only contain a street address or a postal code, or it might contain a full address.

Consistency - Self and other Linkable Differential privacy if not consistent Data-free if not consistent Privacy ranking: - 1 if not consistent - 4 if consistent

Identifies the algebraic relationship between 3 or more numeric values, including at least one non-integer. Based on the relationship, generates new values to match. If there is no relationship, uses the Categorical generator.

Linkable - linking is required Privacy ranking: 3

Generates unique alphanumeric strings of the same length as the input. For example, for the origin value ABC123, the output value is a six-character alphanumeric string such as D24N05.

Consistency - Self only Primary key generator Unique columns allowed Format-preserving encryption (FPE) Privacy ranking: - 3 if not consistent - 4 if consistent

Within an array, replaces letters with random other letters, and numbers with random other numbers. Preserves punctuation and whitespace.

Consistency - Self only Privacy ranking: - 3 if not consistent - 4 if consistent

Used to transform array values in JSON.

To identify values to transform, you provide a list of JSONPaths. For each JSONPath, you assign a sub-generator to apply to matching values.

Composite generator. Feature support is based on the sub-generators. Privacy ranking: 5

Used to transform values in an array. To identify values to transform, you provide a regular expression. For each capture group in an expression, you assign a sub-generator to apply to matching values.

Composite generator. Feature support is based on the sub-generators. Privacy ranking: 5

Generates unique alpha-numeric strings based on any printable ASCII characters. You can optionally exclude lowercase letters from the generated values. The replacement value does not preserve the length of the original value.

Consistency - Self only Primary key generator Unique columns allowed Format-preserving encryption (FPE) Privacy ranking: - 3 if not consistent - 4 if consistent

Generates a random company name-like string.

Consistency - Self or other Differential privacy if not consistent Data-free if not consistent Privacy ranking: - 1 if not consistent - 4 if consistent

Shuffles the original values for a column to different rows. Maintains the overall frequency of each value. For example, a column contains the values Small (3 times), Medium (4 times), and Large (5 times). In the transformed data, each value appears the same number of times, but the values are shuffled to different rows.

Linkable Differential privacy is configurable Privacy ranking: - 2 with differential privacy - 3 without differential privacy

Replaces letters with random other letters and numbers with random other numbers. Preserves punctuation, whitespace, and mathematical symbols.

Consistency - Self only Privacy ranking: - 3 if not consistent - 4 if consistent

Replaces characters with other random characters. Preserves punctuation, capitalization, and whitepace. A replacement character is always from within the same Unicode Block as the source character. A source character is always mapped to the same destination character. For example, M might always map to V.

Always self-consistent Unique columns allowed Privacy ranking: 4

Consistency - Self or other Differential privacy if not consistent Data-free if not consistent Privacy ranking: - 1 if not consistent - 4 if consistent

Applies different generators to rows conditionally based on the column value. For example, apply the Character Scramble generator for values other than Test. You configure a list of conditions. Each condition performs a check against the column value. For each condition, you assign a sub-generator to apply to matching values.

Unique columns allowed Composite generator. Other feature support is based on the sub-generators. Privacy ranking: If a fallback generator is selected, then the lower of 5 or the fallback generator. 5 if no fallback generator is selected.

Uses a single specified value to replace all of the values in the column. The replacement value must be compatible with the column data type.

Differential privacy Data-free Privacy ranking: 1

Generates a continuous distribution to fit the underlying data. Can link to other columns to create multivariate distributions. Can also be partitioned by other columns.

Linkable Differential privacy is configurable Privacy ranking: - 2 with differential privacy - 3 without differential privacy

Populates the column using the sum of values from a column in another table. To select the rows to use, uses a foreign key value that matches the primary key value for the current row. For example, to transform the Total_Sales column in the Customers table, from the Transactions table, use the sum of the Amount values for rows where the Customer_ID value matches the primary key value for the current customer.

Privacy ranking: 3

Used to mask text in a delimited format.

Parses the text as a row where the columns are delimited by a specified character. For each index, you assign a sub-generator to apply to the index value.

Composite generator. Feature support is based on the sub-generators. Privacy ranking: 5

Replaces the original column value with a value from list of values that you provide.

Consistency - Self and other Linkable Differential privacy if not consistent Data-free if not consistent Privacy ranking: - 1 if not consistent - 4 if consistent

Truncates dates or timestamps to a specific date or time component. For example, you might truncate a date value to the month or a timestamp to the hour.

Privacy ranking: 5

Scrambles characters in an email address.

Preserves the formatting and keeps the @ and .. You can identify specific email domains to not scramble.

Consistency - Self only Privacy ranking: - 3 if not consistent - 4 if consistent

Generates timestamps that fit an event distribution. You can link columns to create a sequence of events across multiple columns. You can also partition the generator by other columns.

Linkable Privacy ranking: 3

Scrambles characters in a file name.

Preserves the formatting and the file extension.

Consistency - Self only Privacy ranking: - 3 if not consistent - 4 if consistent

Replaces all instances of the find string with the replace string. For the find string, you can optionally provide a regular expression.

Privacy ranking: 5

Transforms Norwegian national identity numbers. You can optionally preserve the gender and birthdate portions of the identifier values.

Consistency - Self and other Unique columns allowed Privacy ranking - 3 if not consistent - 4 if consistent

Used to transform columns that contain latitude and longitude values.

Linkable Unique columns allowed Privacy ranking: 3

Can be used to generate cities, states, zip codes, and latitude/longitude values that follow HIPAA guidelines for safe harbor.

Consistency - Self only Privacy ranking: - 3 if not consistent - 4 if consistent

Generates random host names, based on the English language.

Consistency - Self and other Differential privacy if not consistent Data-free if not consistent Privacy ranking: - 1 if not consistent - 4 if consistent

Used to transform values in an HStore column in a PostgreSQL database. You specify a list of keys for which to transform the values. For each key, you assign a generator to apply to the key value.

Composite generator. Feature support is based on the sub-generators. Privacy ranking: 5

Used to transform columns that contain HTML content. To identify the values to transform, you provide a list of path expressions. For each path expression, you assign a generator to apply to the matching value.

Composite generator. Feature support is based on the sub-generators. Privacy ranking: 5

Generates unique integer values.

By default, the generated values are within the range of the column’s data type.

You can also specify a range for the generated values. The source values must be within that range.

Consistency - Self only Differential privacy if not consistent Data-free if not consistent Primary key generator Unique columns allowed Format-preserving encryption (FPE) Privacy ranking: - 1 if not consistent - 4 if consistent

For Canadian mailing addresses, can generate:

  • Street name

  • Postal code

For United Kingdom (UK) mailing addresses, can generate postal codes.

Consistency - Self only Differential privacy if not consistent Data-free if not consistent Privacy ranking: - 1 if not consistent - 4 if consistent

Generates a random IP address-formatted string. You specify the percentage of IPv4 addresses. The remaining addresses are IPv6.

Consistency - Self or other Differential privacy if not consistent Data-free if not consistent Privacy ranking: - 1 if not consistent - 4 if consistent

Used to transform values in JSON columns. To identify values to transform, you provide a list of JSONPaths.

For each JSONPath, you assign a sub-generator to apply to matching values.

Composite generator. Feature support is based on the sub-generators. Privacy ranking: 5

Generates a random MAC address formatted string.

Consistency - Self only Differential privacy if not consistent Data-free if not consistent Format-preserving encryption (FPE) Privacy ranking: - 1 if not consistent - 4 if consistent

Generates unique MongoDB objectId values. Can be assigned to text columns that contain MongoDB ObjectId values. The column value must be 12 bytes long.

Consistency - Self only Privacy ranking: - 3 if not consistent - 4 if consistent

Generates a random name string from a dictionary of first and last names. You specify the name format. For example, a column might contain only a first name, or a full name that is last name first.

Consistency - Self or other Differential privacy if not consistent Data-free if not consistent Privacy ranking: - 1 if not consistent - 4 if consistent

Masks values in numeric columns.

Either adds or multiplies the original value by random noise.

Consistency - Self or other Privacy ranking: - 3 if not consistent - 4 if consistent

Replaces all of the column values with NULL values.

Differential privacy Data-free Unique columns allowed Privacy ranking: 1

Generates unique numeric strings of the same length as the input numeric string.

Consistency - Self only Primary key generator Unique columns allowed Format-preserving encryption (FPE) Privacy ranking: - 3 if not consistent - 4 if consistent

Default generator. Does not perform any transformation on the source data.

Unique columns allowed Privacy ranking: 6

Generates a random telephone number that matches the country or region and format of the input telephone number. For invalid telephone numbers, either replaces individual numbers or generates a valid replacement number.

Consistency - Self only Privacy ranking: 3

Generates a random boolean value. You specify the percentage of true values. The remaining values are false.

Differential privacy Data-free Privacy ranking: 1

Generates a random double number that is between the specified minimum (inclusive) and maximum (exclusive) values.

Differential privacy Data-free Privacy ranking: 1

Generates a random hash string.

Differential privacy Data-free Privacy ranking: 1

Returns a random integer that is between the specified minimum (inclusive) and maximum (exclusive) values.

Differential privacy Data-free Privacy ranking: 1

Generates random dates, times, and timestamps that fall within a specified range.

Differential privacy Data-free Privacy ranking: 1

Generates a random new UUID string.

Differential privacy Data-free Unique columns allowed Privacy ranking: 1

To identify values to transform, you provide a regular expression.

For each capture group in an expression, you assign a sub-generator to apply to matching values.

Unique columns allowed Composite generator. Other feature support is based on the sub-generators. Privacy ranking: 5

Generates a column of unique integer values that start with specified value, and then increment by 1 for each processed row.

Linkable Unique columns allowed Privacy ranking: 3

Generates values of ISO 6346 compliant shipping container codes. The codes are all in the freight ("U") category.

Consistency - Self or other Differential privacy if not consistent Data-free if not consistent Privacy ranking: - 1 if not consistent - 4 if consistent

Generates a new valid Canadian Social Insurance Number. Preserves the formatting from the original value.

Consistency - Self only Data-free if not consistent Unique columns allowed Format-preserving encryption (FPE) Privacy ranking: - 1 if not consistent - 4 if consistent

Generates a new valid United States Social Security Number. For numeric columns, the dashes (xxx-xx-xxxx) are always excluded. Otherwise, you can specify the percentage of values for which to include the dashes.

Consistency - Self or other Differential privacy if not consistent Data-free if not consistent Privacy ranking: - 1 if not consistent - 4 if consistent

Used to transform StructFields within a StructType in Spark databases (Databricks and Amazon EMR). To identify the StructField value to transform, you provide a path expression. For each path expression, you assign a sub-generator to apply to the matching values.

Composite generator. Feature support is based on the sub-generators. Privacy ranking: 5

Shifts timestamps by a random amount of a specific unit of time, within a set range. The range can start before the original value.

Consistency - Self or other Privacy ranking: - 3 if not consistent - 4 if consistent

Generates unique email addresses.

Replaces the username with a randomly generated GUID, and masks the domain with a character scramble.

Consistency - Self only Unique columns allowed Privacy ranking: - 3 if not consistent - 4 if consistent

Used to transform URLs. Preserves the formatting. Keeps the URL scheme and top-level domain intact.

Unique columns allowed Privacy ranking: 3

Generates UUIDs.

Consistency - Self only Primary key generator Unique columns allowed Format-preserving encryption (FPE) Privacy ranking: - 3 if not consistent - 4 if consistent

Used to transform values in XML columns. To identify the values to transform, you provide XPaths. For each XPath, you assign a sub-generator to apply to the matching values.

Composite generator. Feature support is based on the sub-generators. Privacy ranking: 5

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Address
AddressGenerator
Algebraic
AlgebraicGenerator
Alphanumeric String Key
AlphaNumericPkGenerator
Array Character Scramble
ArrayTextMaskGenerator
Array JSON Mask
ArrayJsonMaskGenerator
Array Regex Mask
ArrayRegexMaskGenerator
ASCII Key
AsciiPkGenerator
Business Name
BusinessNameGenerator
Categorical
CategoricalGenerator
Character Scramble
TextMaskGenerator
Character Substitution
StringMaskGenerator
Company Name
CompanyNameGenerator
Conditional
ConditionalGenerator
Constant
ConstantGenerator
Continuous
GaussianGenerator
Cross Table Sum
CrossTableAggregateGenerator
CSV Mask
CsvMaskGenerator
Custom Categorical
CustomCategoricalGenerator
Date Truncation
DateTruncationGenerator
Email
EmailGenerator
Event Timestamps
EventGenerator
File Name
FileNameGenerator
Find and Replace
FindAndReplaceGenerator
FNR
FnrGenerator
Geo
GeoGenerator
HIPAA Address
HipaaAddressGenerator
Hostname
HostnameGenerator
HStore Mask
HStoreMaskGenerator
HTML Mask
HtmlMaskGenerator
Integer Key
IntegerPkGenerator
International Address
InternationalAddressGenerator
IP Address
IPAddressGenerator
JSON Mask
JsonMaskGenerator
MAC Address
MACAddressGenerator
Mongo ObjectId Key
ObjectIdPkGenerator
Name
NameGenerator
Noise Generator
NoiseGenerator
Null
NullGenerator
Numeric String Key
NumericStringPkGenerator
Passthrough
PassthroughGenerator
Phone
USPhoneNumberGenerator
Random Boolean
RandomBooleanGenerator
Random Double
RandomDoubleGenerator
Random Hash
RandomStringGenerator
Random Integer
RandomIntegerGenerator
Random Timestamp
RandomTimestampGenerator
Random UUID
UUIDGenerator
Regex Mask
RegexMaskGenerator
Sequential Integer
UniqueIntegerGenerator
Shipping Container
ShippingContainerGenerator
SIN
SINGenerator
SSN
SsnGenerator
Struct Mask
StructMaskGenerator
Timestamp Shift
TimestampShiftGenerator
Unique Email
UniqueEmailGenerator
URL
UrlGenerator
UUID Key
UuidPkGenerator
XML Mask
XmlMaskGenerator
generator reference
generator characteristics
composite generator
primary key generator
Business Name
privacy ranking