Private AI Engine
Private AI Engine
Overview
The Private AI Engine is zkShine’s privacy-preserving artificial intelligence framework. It enables AI models to perform inference, training, and analytics on encrypted data using zero-knowledge proofs, ensuring that no private data, model weights, or intermediate outputs are ever exposed.
This module integrates zkML (Zero-Knowledge Machine Learning) into Solana, turning private computation into verifiable on-chain proofs.
Core Capabilities
Confidential Inference Execution Execute AI models on private data (e.g., financial metrics, user profiles) where only the result is revealed, not the data itself. Example: Proving that a risk score > 700 without showing the dataset.
zkML Proof Verification on Solana Each inference task generates a cryptographic proof validated through a Solana program. This provides verifiable correctness of AI outputs while maintaining confidentiality.
Encrypted Dataset & Model Handling All AI inputs, parameters, and weights are encrypted during runtime, ensuring that neither nodes nor operators can view sensitive information.
Private AI-as-a-Service (zkAIaaS) Developers and enterprises can deploy AI workloads to zkShine Compute Nodes for private execution, billed via $ZKSHN tokens.
Architecture
Encrypted Data → zkAI Engine → Proof Generation → On-Chain VerificationFlow Example:
User submits encrypted dataset and model parameters.
zkShine executes the AI model privately inside zkCompute nodes.
The engine produces a ZK proof of inference correctness.
Solana validates the proof and records the result.
Use Cases
Private DeFi risk scoring
zk-powered credit and identity analytics
Confidential medical or biometric AI
Encrypted data marketplaces
zkVerified AI results for on-chain automation
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