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Synthetic Data: Insights, updates and more
Learn more about synthetic data, our product and successful use cases
Differential Privacy for Synthetic Data (Part II): Trust-but-Verify
You can trust that your software is applying differential privacy, but can you verify it for yourself? Use our framework to measure privacy for any synthesizer.
Differential Privacy for Synthetic Data (Part II): Trust-but-Verify
You can trust that your software is applying differential privacy, but can you verify it for yourself? Use our framework to measure privacy for any synthesizer.
7 signs a synthetic data software violates privacy
Are you evaluating synthetic data vendors? Look out for these signs that their software might be violating privacy.
Differential Privacy for Synthetic Data (Part I): Synthesizer Disclosure
Synthesizers are game-changers for data disclosure and differential privacy. Use them to create unlimited, differentially private synthetic data.
Introducing AI Connectors: Database Integration for Synthetic Data
AI Connectors allow users to create robust, referentially sound synthetic data by connecting to an existing database and automatically creating highly accurate metadata, regardless of the underlying database technology.
Synthetic Data in 2024: The Year In Review
Check out how 2024 has been the biggest year for synthetic data. Google, Apple, Meta, OpenAI all emphasized the importance of using synthetic data in their AI model development. While Snowflake, Databricks, DataCebo and several others released new tooling required to create synthetic data.
Synthesizers are data diversification engines – embrace them!
Synthesizers can create diverse data that is also high quality. Check out how these two vital traits inform each other and drive great business outcomes.
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.
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