Privacy-Preserving
Machine Learning
Use Zama Concrete ML to leverage the power of Machine Learning while ensuring your users' privacy and data security with Fully Homomorphic Encryption (FHE).
FHE Unlocks a Myriad of New Use Cases with Encrypted Computation
Healthcare
Improve patient care while maintaining privacy by allowing secure, confidential data sharing between healthcare providers.
Finance
Facilitate secure financial data analysis for risk management and fraud detection, keeping client information encrypted and safe.
Advertising
Create targeted advertising and campaign insights in a post-cookie era, ensuring user privacy through encrypted data analysis.
Defense
Enable data collaboration between different agencies, while keeping it confidential from each other, enhancing efficiency and data security, without revealing secrets.
Biometrics
Give the ability to create user authentication applications without having to reveal their identities.
Government
Enable governments to create digitized versions of their services without having to trust cloud providers.
Check out our real life demos (with code examples) on Hugging Face.
Implement Machine Learning Algorithms Operating on Encrypted Data Using FHE
Concrete ML enables the handling of sensitive data in a secure manner, so data scientists can leverage the power of Machine Learning for new use cases where the data needs to be protected.
Private Inference
Run secure, privacy-preserving predictions using your ML models over encrypted data.
Private Training
Train models over encrypted datasets, owned by parties that don't trust each other.
Secure Collaboration
Collaborate on sensitive data with untrusted third parties without compromising privacy inside data clean rooms.
Private LLMs & IP Protection
Unleash the power of LLMs like ChatGPT on your data while keeping important information confidential.
Want to learn more? Contact us.
Leverage the Power of FHE Without Having to Learn Cryptography
Ease of use for data-scientists
Use familiar APIs from scikit-learn and PyTorch to automatically convert machine learning models into their FHE equivalent.
Support for various models and customization
Concrete ML comes with built-in models that are ready-to-use and FHE-friendly, mimicking the user interfaces of their scikit-learn and XGBoost counterparts.
Collaborative Secure Computation
Opening up new avenues for collaborative research and development across various sectors.
Create data clean rooms where you can encrypt and process information from multiple sources, ensuring each participant only sees the final results, not others' sensitive data. Ideal for finance, healthcare, research, and more – where collaboration is key, but data security is crucial.
Machine Learning Model Support
Concrete ML provides for several of the most popular and traditional models.
Linear Models
- Linear Regression
- Logistic Regression
- Generalized Linear Models
- SVM
- ElasticNet
- Lasso
- Ridge
Tree-based Models
- Decision Trees
- Random Forest
- XGBoost
Neural Networks
- Built-in Multi-Layer Perception
- CNNs
- VGG
Versatility by Design — Concrete, a Modular Framework
Concrete ML is built on-top of Concrete, Zama's open source FHE compiler, making it a very modular framework, and ready to integrate future innovations.
Read more about Concrete: Zama's FHE compiler.
More Resources
Read our latest privacy-preserving machine learning blog posts and learn more with our developer tutorials and presentations.
Talk to the Zama team to explore FHE.
Do you want to know more about Zama's privacy-preserving machine learning solutions?
We're happy to discuss your use cases and explore together what is possible.
Or see the code on Github.