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  • Welcome to Syntho
  • Overview
    • Get started
      • Syntho bootcamp
        • 1. What is Syntho?
        • 2. Introduction data anonymization
        • 3. Connectors & workspace creation
        • 4. PII scan
        • 5. Generators
          • Mockers
          • Maskers
          • AI synthesize
          • Calculated columns
          • Free text de-identification
        • 6. Referential integrity & foreign keys
        • 7. Workspace synchronization & validation
        • 8. Workspace & user management
        • 9. Large workloads​
        • 10. AI synthesis: Data pre-processing when using
      • Prerequisites
      • Sample datasets
      • Introduction to data generators
      • AI-generated synthetic data
    • Frequently asked questions
  • Setup Workspaces
    • View workspaces
    • Create a workspace
      • Connect to a database
        • PostgreSQL
        • MySQL / MariaDB
        • Oracle
        • Microsoft SQL Server
        • DB2
        • Databricks
          • Importing Data into Databricks
        • Hive
        • SAP Sybase
        • Azure Data Lake Storage (ADLS)
        • Amazon Simple Storage Service (S3)
      • Workspace modes
    • Edit a workspace
    • Duplicate a workspace
    • Transfer workspace ownership
    • Share a workspace
    • Delete a workspace
    • Workspace default settings
  • Configure a Data Generation Job
    • Configure table settings
    • Configure column settings
      • AI synthesize
        • Sequence model
          • Prepare your sequence data
        • QA report
        • Additional privacy controls
        • Cross-table relationships limitations
      • Mock
        • Text
          • Supported languages
        • Numeric (integer)
        • Numeric (decimal)
        • Datetime
        • Other
      • Mask
        • Text
        • Numeric (integer)
        • Numeric (decimal)
        • Datetime
        • UUID
      • Duplicate
      • Exclude
      • Consistent mapping
      • Calculated columns
      • Key generators
        • Differences between key generators
      • JSON de-identification
    • Manage personally identifiable information (PII)
      • Privacy dashboard
      • Discover and de-identify PII columns
        • Identify PII columns manually
        • Automatic PII discovery with PII scanner
      • Remove columns from PII list
      • Automatic PII discovery and de-identification in free text columns
      • Supported PII & PHI entities
    • Manage foreign keys
      • Foreign key inheritance
      • Add virtual foreign keys
        • Add virtual foreign keys
        • Use foreign key scanner
        • Import foreign keys via JSON
        • Export foreign keys via JSON
      • Delete foreign keys
    • Validate and synchronize workspace
    • View and adjust generation settings
  • Deploy Syntho
    • Introduction
      • Syntho architecture
      • Requirements
        • Requirements for Docker deployments
        • Requirements for Kubernetes deployments
      • Access Docker images
        • Online
        • Offline
    • Deploy Syntho using Docker
      • Preparations
      • Deploy using Docker Compose
      • Run the application
      • Manually saving logs
      • Updating the application
      • Backup
    • Deploy Syntho using Kubernetes
      • Preparations
      • Deploy Ray using Helm
        • Upgrading Ray CRDs
        • Troubleshooting
      • Deploy Syntho using Helm
      • Validate the deployment
      • Troubleshooting
      • Saving logs
      • Upgrading the applications
      • Backup
    • Manage users and access
      • Single Sign-On (SSO) in Azure
      • Manage admin users
      • Manage non-admin users
    • Logs and monitoring
      • Does Syntho collect any data?
      • Temporary data storage by application
  • Syntho API
    • Syntho REST API
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  • Hasher
  • Datetime Noise

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  1. Configure a Data Generation Job
  2. Configure column settings
  3. Mask

Datetime

PreviousNumeric (decimal)NextUUID

Last updated 28 days ago

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Below is a list of available datetime mask functions.

Hasher

The Hasher anonymizes datetime values by applying a random offset while maintaining consistency. This ensures that the relative differences between timestamps remain intact while preventing direct identification of the original values. To ensure accurate ordering, please see .

Parameters

  • No parameters.

Example

If you configure:

Column names:
1951-12-07,
1966-12-26,
1971-09-23,
...

The results will be:

Column names:
1957-06-18,
1943-07-30,
2015-09-01,
...

Datetime Noise

Adds a random shift to a datetime value based on specified minimum and maximum shift parameters.

Parameters

  • Maximum negative noise: The smallest amount the date can be adjusted, relative to the original date.

    • Use negative numbers to shift the date into the past.

      • Example: If the date part is set to "Day" and the minimum shift is set to -5, this ensures the date will not be shifted earlier than 5 days prior to the original date.

      • A positive number shifts the date forward.

      • Example: If the minimum shift is 5, the date will not shift earlier than 5 days after the original date.

  • Maximum positive noise: The largest amount by which the date can be adjusted from the original value.

    • Use positive numbers to shift the date into the future.

      • Example: If the date part is set to "Day" and the maximum shift is set to 5, the date will not be shifted later than 5 days after the original date.

  • Noise type: The unit of time (e.g., second, minute, hour day, month, year) that will define the granularity of the shift.

    • The selected unit will be applied to both the minimum and maximum shift fields.

Example

If you configure:

Maximum negative noise: 2
Maximum positive noise: 2
Noise type: Day

1951-12-07,
1966-12-26,
1971-09-23,
...

The results will be:

1951-12-05,
1966-12-26,
1971-09-23,
...

Consistent mapping: Datetime Noise supports .

consistent mapping
ordering and indexing considerations
Hasher
Datetime Noise