Confidential Decentralized AI Using Fully Homomorphic Encryption
Perform inference and training on encrypted data without decrypting it, ensuring sensitive information to remain confidential throughout the AI processing lifecycle.
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Read white paper
Read white paper
Decentralized AI runs on public infrastructure, exposing all user data and model parameters.
Concrete ML Enables Confidential Decentralized AI Using Fully Homomorphic Encryption
And unlocks a myriad of new use cases.
Healthcare
Enable AI diagnosis and collaborative medical research onchain.
Advertising
Allow privacy-preserving, onchain advertising.
Games
Enable AI in onchain games that require hidden states.
Biometrics
Authenticate users and filter bots without revealing real identities.
Finance
Enable confidential credit scoring and AI-powered DeFi.
IoT Security
Secure and manage IoT devices with confidential AI, ensuring data privacy and integrity on edge devices.
E2E Encryption
Data remains confidential throughout the AI processing lifecycle.
Inference and Training Onchain
Inference and training is done without revealing the user inputs, training data or model weights.
Python Support
Concrete ML converts Python code to FHE. Data scientists can use it with frameworks like Scikit-Learn and PyTorch.
Consensus
Concrete ML produces deterministic encrypted outputs, allowing for consensus and slashing.
Optimistic Fraud Proofs
FHE models can be integrated in Optimistic ML frameworks, as results can be recomputed by anyone.
Validation Sampling
Concrete ML can expose intermediary ciphertexts to enable verifying a random sample of the computation.
Use a Turnkey FHE Solution to Make Your Stablecoin Confidential
Concrete ML
Confidential computing
Inference and training is done without revealing the user inputs, training data or model weights.
Inference and training is done without revealing the user inputs, training data or model weights.
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Threshold Key Management System
Access control
Zama's Threshold KMS enables managing access of the encrypted results directly via smart contracts onchain.
Zama's Threshold KMS enables managing access of the encrypted results directly via smart contracts onchain.
Ready to implement FHE?
Take a deep dive into our tech or contact our team to get started.
Are you a developer?
Discover our open-source libraries on Github and explore our developer resources to implement FHE with Zama.
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Talk to the Zama team
Leverage FHE using Zama’s libraries, products, and solutions to unlock new privacy-enhancing capabilities.
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