https://github.com/pyladiesams/privacy-aware-ml-ds-nov2023
Learn how to apply core privacy principles and techniques to the data science and machine learning workflows with Python open source libraries for privacy-preserving machine learning.
https://github.com/pyladiesams/privacy-aware-ml-ds-nov2023
dataprivacy differential-privacy ppml privacy-by-design privacy-preserving-machine-learning
Last synced: 3 months ago
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Learn how to apply core privacy principles and techniques to the data science and machine learning workflows with Python open source libraries for privacy-preserving machine learning.
- Host: GitHub
- URL: https://github.com/pyladiesams/privacy-aware-ml-ds-nov2023
- Owner: pyladiesams
- License: mit
- Created: 2023-10-03T08:14:07.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2023-11-27T18:25:40.000Z (over 1 year ago)
- Last Synced: 2025-01-03T22:14:00.626Z (5 months ago)
- Topics: dataprivacy, differential-privacy, ppml, privacy-by-design, privacy-preserving-machine-learning
- Homepage:
- Size: 725 KB
- Stars: 2
- Watchers: 0
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Privacy-Aware Machine Learning and Data Science
## Workshop description
We'll learn how to apply core privacy principles and techniques to the data science and machine learning workflows. We'll also look at how to experiment with Python open-source libraries to ensure privacy-preservation in machine learning.
## Usage
* Follow the instructions at [this link](https://github.com/kjam/practical-data-privacy)
## About the workshop giver
[Katharine Jarmul](https://www.linkedin.com/in/katharinejarmul/) is a privacy activist and data scientist whose work and research focus on privacy and security in data science workflows. She recently authored [Practical Data Privacy](https://practicaldataprivacybook.com/) for O'Reilly and works as a Principal Data Scientist at Thoughtworks. Katharine has held numerous leadership and independent contributor roles at large companies and startups in the US and Germany - implementing data processing and machine learning systems with privacy and security built-in and developing forward-looking, privacy-first data strategy.
## Credits
This workshop was set up by @pyladiesams