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Synthetic Data: Insights, updates and more
Learn more about synthetic data, our product and successful use cases

Contextual Anonymization for GPS Data in SDV
GPS data is often PII but anonymizing GPS data while preserving useful context is difficult. Learn about the challenges and how we solved them in this post.

Meet the SDV Enterprise Multi-Table Synthesizers
To create multi-table synthetic data, it's important to learn the connections between tables. Explore 3 different approaches to the challenge.
Boosting Fraud-Detection Accuracy with Synthetic Data
Using a model from the Synthetic Data Vault (SDV), a UCLA team has shown that credit card fraud-detection can be dramatically improved by generating synthetic case data consistent with past examples of fraud. They show that they can reduce the false negatives by a factor of 20x.

How ING Belgium Uses DataCebo’s SDV Enterprise to Create Synthetic Data for 100x the Test Coverage
ING Belgium uses SDV Enterprise for testing payments. Synthetic payments lead to 100x the test coverage in less than 1/10th the time.

Launching SDV Enterprise: A Generative AI Solution for Tabular Data
Our first commercial product, SDV Enterprise, takes the popular, public SDV software to the next level.

Train an SDV Generative AI: A random subsample is all you need
It is said that Generative AI requires large amounts of data for training. We've tested the SDV and found that a smaller subsample is all you need.

Improving synthetic data up to +40% (without building new ML models)
See how we improved synthetic data without modifying the generative algorithms or affecting performance.

Contextual Anonymization: The latest innovation for PII
Can you anonymize PII without sacrificing usability? Explore the latest techniques in anonymization.

Why we changed the SDV license to BSL (and how that impacts our users)
The Business Source License allows the SDV team to continue innovating while also enabling many types of usage.

Announcing SDV 1.0: Towards programmable synthetic data stack
The SDV 1.0 library has formalized key principles for generative AI. See what's new and enrich your synthetic data project.
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