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Pseudonymization and anonymization are closely related but 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.
In this blog post, we walk through how to evaluate synthetic data quality and performance for Machine Learning using components from the Statice SDK.
This article looks at the data challenges of healthcare companies and how to leverage synthetic health data to move past these obstacles with practical applications in healthcare.
In this post, we summarized the insights from the discussion between health data experts from AstraZeneca, Charité, InGef, and Statice during the panel session on synthetic data at the Medica conference 2021.
Our CEO Omar was invited to the Digital Insurance Podcast to discuss with Jonas Piela about digital privacy, data use in insurance, and innovation. Check the episode highlights in this blog post.
In this interview, Machine Learning and privacy researcher Franziska Boenisch explains the logic behind privacy attacks and defense mechanisms for machine learning models. Learn about model vulnerability, Differential Privacy, DP-SGD algorithm, and privacy assessment.
This article covers the main privacy risks associated with anonymized data. We also describe the levels of re-identification protection of data protection techniques; the privacy-preservation properties of synthetic data.
This post is part of a series of discussions with the Statice team. Borbala and José from our product team shared their learnings on synthetic structured data generation.
