Learning the tools that preserve user privacy is going to become an increasingly important skillset for all aspiring data scientists to learn in the coming months. Legal frameworks like GDPR are being proposed all around the world as people realize how valuable their data is, so data scientists need to accept that they'll have to handle data differently than in the past. In this video, I'll demo 3 important privacy techniques; differential privacy, secure multi party computation, and federated learning. We'll use these techniques to train a mode built with Python l to predict diabetes while keep user data anonymous. Enjoy!
Code for this video:
https://github.com/OpenMined/PySyft/tree/master/examples/tutorials
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https://iamtrask.github.io/2017/03/17/safe-ai/
https://mortendahl.github.io/
https://florian.github.io/federated-learning/
https://towardsdatascience.com/whats-new-in-deep-learning-research-understanding-federated-learning-b14e7c3c6f89
http://www.cleverhans.io/privacy/2018/04/29/privacy-and-machine-learning.html
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