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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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
Features
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.

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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.

They're already powered by Zama

As our partnership with Zama unfolds, we see the potential for FHEML to not just enhance DePin infrastructure, but to catalyze mass adoption across Privasea’s diverse range of use cases. Together, we’re not just safeguarding data; we’re pioneering a new era of privacy and security.

— David Jiao, Founder and CEO at Privasea

Ready to implement FHE?

Get in touch to discuss your project with the team

Are you a developer?

Everything we do at Zama is open source, check our Github libraries, and learn how to implement FHE with our developer resources.
See on Github

Talk to the Zama team

Learn how you can leverage the power of our Concrete ML and Threshold Key Management Service for your project.
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