menisadi/awesome-differential-privacy

Curated list of differential privacy libraries, courses, and readings

What it solves

Differential privacy is a mathematical framework for sharing statistical insights about a dataset while provably limiting what can be learned about any individual in it. This list helps people find trustworthy, open-source tools and learning materials to apply differential privacy, whether they are just starting out or already working on production systems.

How it works

This is an "awesome list" — a hand-picked collection of links on GitHub. It is organized into five sections: Code and Projects (libraries and frameworks), Learn (courses, textbooks, and blog tutorials), Read (articles and primers), Industry Articles (practical use cases from companies like Apple, Google, and Microsoft), and YouTube (video lectures and explainers). Each item links to the actual resource, so visitors can jump directly to the tool or reading they need.

Who it's for

Developers, data scientists, researchers, and privacy-conscious organizations who want to understand or deploy differential privacy. Beginners will find friendly introductions and courses, while practitioners can choose from production-grade libraries like OpenDP, Opacus, and TensorFlow Privacy.

Highlights

  • Curated list of code libraries: OpenDP, Google's Differential Privacy Library, Opacus, TensorFlow Privacy, Diffprivlib, and more
  • Learning resources: the foundational textbook "The Algorithmic Foundations of Differential Privacy", video courses, and a non-technical blog series
  • Industry articles: how Apple, Google, and Microsoft use differential privacy in practice
  • A section of YouTube talks, including a collaboration with the US Census Bureau

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