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.
We present in this article why insurers need to make privacy a strategic decision to remain agile and how privacy-preserving data represents a renewed ability to process customer data safely and seamlessly.
In this new entry of our data interview series, we talked to Behrang Raji, data protection and technology specialist, about privacy, data regulations, and ethical AI.
Statice's CEO, Omar Ali Fdal, shares his take on the partnership with PwC Germany: what it means for Statice and what the future holds for our company.
In this new entry of our data interview series, we talked to Jeanette Knipp, Junior Associate at idalab, about AI and data access in healthcare.
Learn about the Statice software in this post. Discover details about its technical features and the properties of the synthetic data you can generate with it.
This article explores synthetic data generation. What's the logic behind synthetic tabular data generation? And what role can deep learning play? We look at two models, VAEs and GANs, and explain which approach we decided to follow at Statice.