cloneside

Synthetic Data Generation

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This solution provides a secure method to clone production data by automatically identifying and classifying personally identifiable information (PII), sensitive data, and safe data within a database, then employing strategies such as masking, pseudonymization, or synthetic data generation to ensure privacy. Its main features include in-place PII pseudonymization, generalization of quasi-identifiers, compliance with privacy standards like k-anonymity and l-diversity, as well as statistical modeling to generate realistic synthetic datasets that preserve data distribution and relational integrity. Designed for data engineers, analysts, and security professionals, it addresses challenges related to sharing, testing, and analyzing sensitive production data safely without compromising privacy or data fidelity.

cloneside can be found in Synthetic Data Generation categories.

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Details

cloneside Main Features

• Safe Cloning
  Clone production data safely by classifying PII and creating masks or synthetic data that keep data relationships intact.
• Output Result
  Get a safe, referentially intact database that keeps the same shape and relationships as the original.
• Safety Checks
  Use k-anonymity, l-diversity, and distribution checks to verify data privacy and quality.
• Synthetic Mode
  Generate new data from learned distributions that look real but do not match any original records.
• Schema Inspection
  Automatically examine every table, column, and foreign key without extra setup.
• Distribution Learning
  Learn the data patterns and distributions to generate realistic synthetic data.
• Classification of Data
  The system tags each column as PII, sensitive, or safe with a confidence score.
• Connection to Database
  Connect easily using a connection string like Postgres or MySQL or by using a data dump file.
• Data Strategy Selection
  Choose how to make data safe—either masking in place or generating synthetic data.
• Ensuring Data Integrity
  Both masking and synthetic methods keep the data’s structure and relations correct.
• Mask Mode (Pseudonymization)
  Replace PII with realistic fake data and generalize quasi-identifiers in place to protect privacy.

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