facebookresearch/CrypTen
A PyTorch-based framework implementing secure multiparty computation for privacy-preserving machine learning.
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CrypTen is a framework for privacy-preserving machine learning built on PyTorch. It implements Secure Multiparty Computation as its secure computing backend, providing a CrypTensor object that mimics PyTorch tensors to enable automatic differentiation and neural network training with cryptographic privacy guarantees. The library-based architecture allows ML practitioners to debug, experiment, and build secure ML models.
Frequently asked
- What is facebookresearch/CrypTen?
- A PyTorch-based framework implementing secure multiparty computation for privacy-preserving machine learning.
- Is CrypTen open source?
- Yes — facebookresearch/CrypTen is open source, released under the MIT license.
- What language is CrypTen written in?
- facebookresearch/CrypTen is primarily written in Python.
- How popular is CrypTen?
- facebookresearch/CrypTen has 1.6k stars on GitHub.
- Where can I find CrypTen?
- facebookresearch/CrypTen is on GitHub at https://github.com/facebookresearch/CrypTen.