The Problem
Web3 applications increasingly need reputation, risk scoring, and trust signals.
However, current solutions have a major issue:
• Reputation systems expose user data publicly
• Credit/risk models require sending sensitive data to centralized servers
• Wallet analytics often reveal full transaction history
• AI scoring pipelines break user privacy
This creates a trade-off between useful intelligence and data confidentiality.
The goal of this project was to remove that trade-off.
PrivateAI Oracle
A privacy-preserving trust scoring system that:
- Collects wallet or activity signals
- Runs an AI-based trust evaluation off-chain
- Produces a verifiable score
- Stores proof/commitment on Oasis confidential smart contracts
The system allows applications to query a user's trust score without revealing the underlying private data used to compute it.
Why Oasis Network
This architecture relies on Oasis confidential computing features.
Using Sapphire (confidential EVM runtime):
- Sensitive inputs remain encrypted
- Smart contract state is confidential when required
- Only final outputs are selectively revealed
This makes Oasis ideal for AI + blockchain workflows involving private datasets.
Traditional public chains would expose:
- model inputs
- intermediate scoring logic
- sensitive user metadata
Oasis prevents this.
Architecture
The system consists of three main components:
Data Collection Layer
Collects public or permissioned signals such as:
- wallet activity patterns
- GitHub contribution signals
- behavioral indicators
Only necessary features are extracted.
Off-Chain AI Inference Engine
A machine learning model computes:
- a normalized trust score
- optional confidence level
The raw feature data never needs to be publicly stored on-chain.
Oasis Confidential Smart Contract
The contract:
- receives a signed result
- records a commitment hash
- allows verification of authenticity
- exposes only the final trust score
This preserves verifiability while protecting user privacy.
What This Enables
PrivateAI Oracle can be used for:
- DeFi borrower trust scoring without exposing full history
- DAO contributor reputation without revealing private activity
- sybil-resistant onboarding systems
- Privacy-preserving AI reputation layers
- Secure Web3 identity primitives
Technical Takeaways
During development, several important lessons emerged:
- Confidential smart contracts change how data pipelines must be designed
- AI outputs should be treated as attestations, not raw datasets
- Privacy must be built into architecture, not added later
- Hybrid off-chain compute + on-chain proof is extremely powerful
Results
The final prototype demonstrates:
- End-to-end confidential trust scoring
- AI inference integrated with Oasis smart contracts
- Selective transparency (score visible, data hidden)
- Practical architecture for privacy-preserving Web3 intelligence
Future Implementations that could help
- Zero-knowledge proof integration for stronger verification
- Multi-model scoring pipelines
- public demo UI
- Integration with lending or DAO onboarding flows
Web3 does not just need decentralization.
It needs private, verifiable intelligence.
PrivateAI Oracle explores how Oasis confidential computing can enable exactly that.
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