FHE Computer
Build and run your privacy-preserving applications in minutes without needing to migrate existing infrastructure or change your development workflow.
Unlocking Confidential Computation
The concept of decentralized computer is incredibly powerful, but without the ability to process private input securely, its use cases are limited. Fair Math unlocks new possibilities, making confidential computation accessible and pushing technology to the next level.
Core Components
Four-layer architecture enabling scalable, secure, and efficient FHE computations
Application Layer
FHE components and algorithms for processing encrypted and plaintext data. Royalty distribution system for component developers.
Orchestration Layer
Optimizes execution and manages fault-tolerance policies for efficient task coordination and system reliability.
Execution Layer
Fair Math actors serve as computational units, specialized for FHE components or general-purpose processing tasks.
Network Layer
Heterogeneous FHE nodes serving as co-processor for L1/L2 networks, providing distributed computation infrastructure.
Technical Presentations
Research and technical talks on FHE Computer architecture
Real-World Applications
Real-world applications of Web3 and Web2 technologies in various industries and everyday scenarios
Darkpool
Protect trading information, prevent price manipulation and data leaks.
Privacy Preserving AI
Data security in using AI to joint data analysis and build models without violating privacy.
Private DeFi
Conduct transactions and invest in digital assets without disclosing personal information.
FHE Vector Database
Secure similarity search and retrieval on encrypted vector embeddings for AI applications.
Dive Deeper into the Technology
Read our comprehensive whitepaper published at IACR ePrint Archive to understand the technical foundations and implementation details of the decentralized FHE computer.