Learning Pathways
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2.0

Your First Job: Gretel TabDP

Hello everyone, I’m Lipika Ramaswamy, a senior applied scientist and differential privacy lead at Gretel. I’m excited to introduce you to the first Gretel synthesizer model you’ll be working with — Gretel TabDP.

Gretel Tabular DP is a differentially private graph-based generative model, which creates synthetic versions of sensitive data with provable mathematical guarantees of privacy.

When working with a dataset containing largely categorical values, Gretel Tabular DP can produce synthetic records that maintain high statistical symmetry with the original data, even with very conservative differential privacy budgets (ε < 1, δ < 10-7), in just 10 minutes. That’s the focus of this module.

Let’s now look at how you can use TabDP to generate a single artifact.

[walkthrough steps for generating a single artifact]

That’s it for this session. If you want to learn more about Gretel TabDP or differential privacy, checkout these resources below. Also, be sure and answer the question and the end of this video to complete this session.

Quiz

Which of the following are true about differential privacy?
Differential privacy introduces noise at the time of model training
Differential privacy gives a mathematical guarantee on the privacy of your data
Differential privacy is validated by the National Institute of Standards and Technologies as the gold standard for providing robust privacy protection
Differential privacy on your data can be achieved using Gretel’s TabDP model
All of the above

Resources

Anonymize tabular data to meet GDPR privacy requirements
Differential Privacy and Synthetic Text Generation with Gretel

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1.0
Getting Started with Gretel
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2.0
Your First Job: Gretel TabDP
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