CrypTen is a machine learning framework built on PyTorch that enables you to easily study and develop machine learning models using secure computing techniques. CrypTen allows you to develop models with the PyTorch API while performing computations on encrypted data – without revealing the protected information. Different parties can contribute information to the model or measurement without revealing what they contributed.
In this workshop, we will teach participants how to use CrypTen using interactive notebooks - participants should bring a laptop with Jupyter notebook installed. We will work through four common use scenarios for privacy preserving machine learning using secure multiparty computation to allow learning without sharing data:
- Feature Aggregation: multiple parties hold distinct sets of features, and want to perform computations over the joint feature set.
- Data Labeling: one party holds feature data while another party holds corresponding labels, and they would like to learn a relationship between the features and labels.
- Dataset Augmentation: several parties each hold a small number of observations, and would like to use all the observations in order to improve the statistical power of a measurement or model.
- Model Hiding: one party has access to a trained model, while another party would like to apply that model to its own data.
What we’ll cover:
- Installation / Setup (20 min)
- Machine Learning and CrypTen (10 min)
- Secure Multiparty Compute and Tensors in CrypTen (15 min)
- Training a Machine Learning Model on Encrypted Data (45 min)