Synthetic Data
I have spent a lot of time thinking and writing about synthetic data. It is the subject of my PhD.
These posts cover how synthetic data is generated, how to measure whether it is any good, and the trade-offs between privacy and utility that nobody escapes. I write from health research but the same problems show up in finance, pharma, and anywhere the real data is sensitive.
Representativeness in Synthetic Data: What It Means and How to Measure It
Understanding the concept of representativeness in synthetic data and the methods used to measure it.
How Synthetic Data Is Used in Healthcare, Research and Beyond
Explore real-world use cases for synthetic data in healthcare, clinical trials, finance and more.
Multiple Imputation and Perturbation: Why They're Not Built for Synthetic Data
This blog explores why multiple imputation and perturbation are not suitable for generating synthetic data.
What are GANs and how can they generate synthetic data?
This blog explores Generative Adversarial Networks (GANs) and how they can be used to generate synthetic healthcare data.
What is Synthetic Data and Why Does it Matter?
This blog is the first in a series exploring synthetic data, its benefits, and its applications in various fields.