DataSynthesizer

An open-source tool to generate synthetic data with differential privacy guarantees.

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Overview

DataSynthesizer is an open-source tool that uses generative models to create synthetic data that mimics the statistical properties of the original data while providing formal privacy guarantees through differential privacy. It is designed for researchers and practitioners who need to share and analyze sensitive data.

✨ Key Features

  • Open-source
  • Synthetic data generation for tabular data
  • Differential privacy
  • Data utility and privacy evaluation
  • Multiple generative models

🎯 Key Differentiators

  • Focus on differential privacy
  • Simplicity and ease of use for tabular data
  • Open-source and free to use

Unique Value: DataSynthesizer provides a free and open-source solution for generating synthetic data with strong privacy guarantees, making it accessible for a wide range of users.

🎯 Use Cases (4)

Privacy-preserving data sharing Data analysis on sensitive data Machine learning with private data Academic research

💡 Check With Vendor

Verify these considerations match your specific requirements:

  • Complex, high-dimensional data
  • Data with intricate dependencies

🏆 Alternatives

Synthetic Data Vault (SDV) Gretel (open-source components) MOSTLY AI (open-source components)

Compared to other open-source tools, DataSynthesizer has a strong focus on differential privacy, providing formal privacy guarantees for the generated data.

💻 Platforms

Desktop

✅ Offline Mode Available

🔌 Integrations

Python

💰 Pricing

Contact for pricing
Free Tier Available

Free tier: N/A (Open-source)

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