zama-ai/concrete-ml
Concrete ML: Privacy Preserving ML framework using Fully Homomorphic Encryption (FHE), built on top of Concrete, with bindings to traditional ML frameworks.
Concrete ML helps data scientists analyze sensitive information securely by applying machine learning models directly to encrypted data. You provide your existing scikit-learn or PyTorch machine learning models and encrypted datasets, and the system produces predictions or insights while keeping the raw data private. This is ideal for data professionals in regulated industries needing to perform analytics without compromising confidentiality.
1,410 stars. Available on PyPI.
Use this if you need to run machine learning models on sensitive data, such as patient records, financial transactions, or classified government information, without ever decrypting it, ensuring privacy and compliance.
Not ideal if your primary concern is high-speed processing on unencrypted data or if you are not dealing with strict data privacy requirements, as homomorphic encryption adds computational overhead.
Stars
1,410
Forks
197
Language
Python
License
—
Category
Last pushed
Feb 17, 2026
Commits (30d)
0
Dependencies
18
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