SDGym VS Main

I would suggest starting with "differentially private synthetic data generation". These methods utilize differential privacy and mostly protect against membership inference attacks, are very popular in the ML/DL community. I would also suggest reading up on privacy preserving ML methods in general and adversarial attacks against them (membership inference, inversion, reconstruction, property inference), but if you're keen on reading some code, check out sd-gym: https://github.com/sdv-dev/SDGym. The authors have collected implementations for a lot of PPSDG methods. Also I strongly suggest reading McMahan's 2016 paper: https://arxiv.org/abs/1607.00133.

For the time being, the book is available only in PDF format on my e-Store here, with numerous links, backlinks, index, glossary, large bibliography and navigation features to make it easy to browse. This book is a compact yet comprehensive resource on the topic, the first of its kind. The quality of the formatting and color illustrations is unusually high. I plan on adding new books in the future: the next one will be on chaotic dynamical systems with applications. However, the book on synthetic data has been accepted by a major publisher and a print version will be available. But it may take a while before it gets released, and the PDF version has useful features that can not be rendered well in print nor on devices such as Kindle. Once published in the computer science series with the publisher in question, the PDF version may no longer be available. You can check out the content on my GitHub repository, here where the Python code, sample chapters, and datasets also reside.