This blog post demonstrates how to enhance the machine learning model monitoring process for data drift using synthetic data.
This post shows how to possibly address imbalanced data with synthetic data in fraud detection contexts where access to positive labeled data is lacking.
This article sets out an overview of synthetic data from a data privacy perspective. It provides insight into how privacy technologies are used for GDPR compliance, how synthetic data works, and how to evaluate synthetic data compliance.
This blog post explores the origins and developments of differential privacy and provide a high-level explanation of its primary mechanisms. It also discusses differentially private machine learning and synthetic data and how enterprises can use them.
We've shipped a new product release! This new version of our synthetic data solution brings the ability to generate fake data for testing scenarios, detect and redact PII in PDF documents, multi-language support, and more! Check this post to learn more.
We've upgraded our products with new features! With more protection algorithms, advanced PII detection, and new supported data types, protecting your data has never been easier. Take a look at the updated Statice Platform and SDK.
AI biases have harmful consequences. From producing unfair or erroneous results to making a dent in your company’s reputation. No matter the cause, it’s better for your company to take care of AI bias and mitigate it in advance.
Through testing Statice's synthetic data solution, the data science team at Provinzial, the second largest public insurance group in Germany, aimed to revamp how they put their sensitive customer data to work. Learn more about this project.
Since the GDPR entered into force four years ago, the way companies have to handle personal data has changed drastically. Synthetic data holds great promise for this paradigm shift.
