Privacy

Test data generation is the process of creating new data that replicates an original dataset. Here’s how developers and data engineers use it.

Gretel introduces Reinforcement Learning from Privacy Feedback (RLPF), a novel approach to reduce the likelihood of a language model leaking private information.

Create privacy-safe synthetic patient data with Gretel, ensuring compliance, secure sharing, and actionable insights for AI and machine learning in healthcare.

Gretel releases industry standard synthetic tabular data privacy evaluation and risk-based scoring system.

Gretel Navigator’s synthetic data generation outperformed OpenAI's GPT-4 by 25.6%, surpassed Llama3-70b by 48.1%, and exceeded human expert-curated data by 73.6%.

Synthetic data is algorithmically generated data that mirrors the statistical properties of the dataset it’s based on. Learn how to make high-quality synthetic data.

How our new Synthetic Data Accelerator Program with AWS will help enterprises scale responsible AI systems fast.

Gretel releases a new synthetic financial document dataset to empower AI developers in building customized and highly performant sensitive data detection systems.

A look at how using Gretel’s Privacy Filters to immunize synthetic datasets against adversarial attacks can impact machine learning accuracy.

Take a deep dive on training Gretel’s open-source, synthetic data library to generate electronic health records that protect individual privacy (PII).