Privacy

Gretel partners with Google Cloud to harness the power of synthetic data and accelerate safer generative AI adoption in the enterprise.

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.

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

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

How to measure and minimize personally identifiable information (PII) risk in synthetic data.

Safely leverage sensitive or proprietary text data for advanced language model training and fine-tuning

Gretel Tabular DP is a fast and powerful new model to generate high quality tabular synthetic data with mathematical guarantees of privacy

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%.

Leveraging Gretel's privacy-preserving synthetic data generation platform to fine-tune Azure OpenAI Service models in the financial domain.

How we implemented a practical attack on a synthetic data model to validate its ability to protect sensitive information under different parameter settings.

How to safely fine-tune LLMs on sensitive medical text for healthcare AI applications using Gretel and Amazon Bedrock

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.

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

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

How differential privacy can generate provably private synthetic text data for a variety of enterprise AI applications.

Discover the streamlined Gretel Python SDK. Start building with synthetic data in just 3 lines of code 🚀

Here's how a hospital uses Gretel to help forecast staffing and resource needs for their emergency care unit, and to identify emerging trends in outbreaks.

Gretel’s suite of privacy-enhancing tools and generative AI models are now available on Google Cloud Marketplace.

How to utilize Gretel to create high-quality synthetic tabular data that you can use as training data for a classification model in Vertex AI.

We’re going to train and build our synthetic dataset off of a real-time public feed of e-bike ride-share data called the GBFS (General Bike-share Feed)

Implementing a practical attack to measure un-intended memorization in synthetic data models.

This article clarifies some common misconceptions about differential privacy and what it guarantees.

Use Gretel’s NLP setting to label PII including people names and geographic locations in free text.

Use Gretel Synthetics and Colaboratory’s free GPUs to train a model to automatically generate fake, anonymized data with differential privacy guarantees.

Use Gretel.ai's APIs to continuously detect and protect sensitive data including credit cards, credentials, names, and addresses.

What if we could ensure that personal data was protected, benefiting not just the individual but also giving developers faster, worry-free access to data?

Create differentially private, synthetic versions of datasets and meet compliance requirements to keep sensitive data within your approved environment.

Gretel Outpost is a free integration architecture that automates the steps that a security team would take in assessing the risk or exposure to data.

Quickly and safely aggregate geolocation data for location density analysis using a hexagonal grid system.

Learn more about how Gretel's REST APIs automatically build a metastore that makes it easy to understand what is inside of your data.

In this post, we will dive into what privacy engineering is, why it’s important, and some of the core use cases we are seeing that are enabled by privacy.

Create synthetic data that’s safer than ever. Our simple configuration file settings enable you to secure both your data and model from adversarial attacks.

Explore how to create a batch interface with the latest version of Gretel Synthetics on Google Colaboratory.

The Gretel research team's favorite trends and takeaways from the NeurlPS 35th Annual Conference on Neural Information Processing Systems.

Beta2 for Gretel.ai is all about delivering privacy engineering as a service through clean, simple APIs.

We decided to examine the privacy preserving capabilities of the Contact Tracing proposal, how it would be implemented, and what privacy concerns exist.

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

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