Machine Learning on Encrypted Data with CrypTen

Explore machine learning on encrypted data using CrypTen in this hands-on workshop presented by Mark Ibrahim from Facebook at PyCon US. Learn how to leverage secure computing techniques to develop models with the PyTorch API while performing computations on protected information. Dive into four common use scenarios for privacy-preserving machine learning: feature aggregation, data labeling, dataset augmentation, and model hiding. Through interactive notebooks, gain practical experience in installation and setup, understanding machine learning with CrypTen, working with secure multiparty compute and tensors, and training a machine learning model on encrypted data. Bring a laptop with Jupyter notebook installed to participate fully in this 26-minute session that bridges the gap between machine learning and data privacy.