Synthetics

A step-by-step guide to creating high quality synthetic time-series datasets with Python.

Answers to some questions about synthetic data that audience members submitted during Gretel's talk at The Rise of Privacy Tech’s Data Privacy Week 2022 conference.

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

Gretel’s new synthetic report is here, featuring a high-level score and metrics to help you assess the quality of your synthetic data.

Data sharing is central to modern business but entails risks. Synthetic data can enable data sharing while reducing the risk of privacy-compromising linkage attacks.

Explore state-of-the-art image synthetics for business documents using diffusion models.

Generate synthetic time series data with Gretel.ai’s open-source PyTorch implementation of DoppelGANger.

Machine learning models can be trained to recognize patterns in datasets. By utilizing algorithms, they can learn to make decisions based on these patterns.

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

Learn the simplest way to generate synthetic data without setting up your own infrastructure and GPUs.