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.
If you want to build a fair AI project and use data ethically, you have to know the types of data bias to spot them before they wreck your machine learning models.
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Pseudonymization and anonymization fall under different categories according to the GDPR. If you want to use sensitive data while meeting all data protection obligations, make sure you understand all the nuances of those methods.
From improving time to data to removing privacy constraints, learn how to use synthetic data in machine learning to enhance AI projects.
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