PPML-Resource and ppml-tutorial
These are ecosystem siblings where one is a curated educational resource collection and the other is a hands-on tutorial implementation—both serving the same PPML learning ecosystem but at different depths, with the resource repository likely linking to or complementing the tutorial as practical reference material.
About PPML-Resource
Ye-D/PPML-Resource
Materials about Privacy-Preserving Machine Learning
This resource provides a curated list of academic papers focused on privacy-preserving machine learning. It helps researchers and practitioners stay current with the latest advancements by providing an organized collection of relevant publications. The output is a categorized list of papers, useful for anyone working with sensitive data and machine learning.
About ppml-tutorial
leriomaggio/ppml-tutorial
Privacy-Preserving Machine Learning (PPML) Tutorial
This project helps data scientists, machine learning engineers, and researchers train machine learning models even when they cannot directly access or share sensitive data due to strict privacy regulations. It takes raw, confidential datasets that cannot leave their 'silos' and outputs machine learning models with strong privacy guarantees, preventing reconstruction of sensitive training data.
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