Verified Computing Framework
A robust system for secure, transparent, and verifiable AI computation.
Introduction
The Verified Computing Framework is a cornerstone of the LexAI ecosystem, designed to ensure that all AI computations are not only secure but also verifiable. In a decentralized AI landscape, trust is paramount. This framework provides the cryptographic assurances needed to validate that AI processes run correctly and without tampering, whether they are executing on-chain or off-chain.
Why Verifiable Computation Matters
- Trust and Transparency: In a decentralized network, participants need to trust that computations are performed honestly. Verifiable computing provides the proof needed to build this trust.
- Security: It prevents malicious actors from manipulating AI model outputs or interfering with computational processes for personal gain.
- Data Privacy: By combining with privacy-preserving technologies, it allows for computations on sensitive data without exposing the data itself.
- Accountability: It creates a clear and auditable trail for every computation, ensuring that all actions within the network are accountable.
How It Works
The framework leverages a combination of cutting-edge cryptographic techniques to achieve its goals:
Zero-Knowledge Proofs (ZKPs)
ZKPs are a class of cryptographic protocols that allow one party (the prover) to prove to another party (the verifier) that a statement is true, without revealing any information beyond the validity of the statement itself. In LexAI, ZKPs are used to:
- Prove the correct execution of an AI model without revealing the model's proprietary architecture or weights.
- Verify computations on private data without exposing the underlying data.
Secure Multi-Party Computation (MPC)
MPC enables multiple parties to jointly compute a function over their inputs while keeping those inputs private. This is crucial for training and running AI models on distributed datasets owned by different individuals or organizations.
Trusted Execution Environments (TEEs)
TEEs are secure areas inside a main processor that guarantee code and data loaded inside are protected with respect to confidentiality and integrity. The framework can utilize TEEs as an additional layer of security for off-chain computations.
Benefits of the Verified Computing Framework
- Decentralized Trust: Removes the need for a central trusted authority to oversee AI computations.
- Enhanced Security: Protects against a wide range of attacks, including data tampering and malicious computations.
- Economic Incentives: Creates opportunities for users to earn rewards by providing computational resources and verifying the work of others.
- Interoperability: Provides a standardized way to verify computations across different AI models and applications within the LexAI ecosystem.