CrypTen

CrypTen is a tool that lets machine learning researchers train and run AI models on encrypted data without ever exposing the raw information. Imagine you want to build a predictive model using sensitive data from multiple organizations—hospital records, financial information, or personal metrics—but none of the parties involved want to share their actual data. CrypTen solves this by keeping all the data encrypted throughout the entire process, so the model learns patterns without anyone seeing the underlying numbers.

The way it works is deceptively simple from a user's perspective. CrypTen wraps data in encrypted containers called CrypTensors that behave almost exactly like PyTorch tensors—the standard tool most machine learning engineers already use. This means if you know PyTorch, you can write CrypTen code that looks nearly identical. Behind the scenes, the framework uses mathematical techniques called Secure Multiparty Computation to let multiple computers work with encrypted data simultaneously, perform calculations, and reach results without revealing secrets to each other. It's built as a full tensor library rather than just a bolt-on encryption layer, which makes debugging and experimentation much more practical for real research work.

This is most useful for research teams, privacy-focused startups, or institutions handling regulated data. A bank could collaborate with a tech company to build a fraud detection model without either party exposing customer records. A healthcare network could train predictive algorithms across multiple hospitals without centralizing patient data. The framework includes tutorials and working examples—like training classifiers on MNIST, running inference on ImageNet models, and even training neural networks end-to-end on encrypted data—so researchers can see concrete applications.

The README notes this is still a research framework, not ready for production use. It only supports Linux and Mac, and computation happens on CPUs, not GPUs, which means training is slower than standard machine learning. But for teams exploring how to do serious machine learning while keeping data private, it's a genuine library rather than a simplified proof-of-concept, which matters for understanding real-world tradeoffs.