Trusted AI
Run AI models where inputs, outputs, and user intent remain private while preserving model integrity and verifiable execution.
What Is
Trusted AI?
Trusted AI uses MPC to compute on sensitive inputs without exposing data to the model or its operator.
Practical
Implementations
Enable AI decision trees, healthcare analysis, sensitive queries, regulated AI systems, and accountable models using private data safely.
Trusted AI Use Cases
Privacy-Based AI Decision Trees
Privacy-based AI decision trees enable AI inference on sensitive data while keeping both the inputs and the model logic confidential.
Problem: Traditional AI decision tree models require users to submit data in clear form for inference. This exposes sensitive inputs to the model operator and can also reveal aspects of the model’s logic through repeated queries, creating privacy risks for users and intellectual property concerns for model owners.
Solution: Partisia Blockchain enables AI decision tree inference using decentralised MPC, where both input data and intermediate computations remain private. Users receive results without exposing their data, while model owners protect their proprietary logic. This allows sensitive data to be used safely in publicly accessible AI services.
Trust Stamped AI Models
TrustStamped AI models enable accountable AI decisions by proving which criteria were used, without revealing the model’s internal logic or the identity of its owner.
Problem: Many AI systems operate without accountability or transparency around how decisions are made. In regulated or sensitive contexts, such as hiring or credit assessment, there is often no verifiable way to prove whether illegal or biased criteria were used, creating compliance risks and eroding trust.
Solution: Partisia Blockchain enables AI models to be linked to a transparent, MPC-based decentralised identity. The model can cryptographically attest to the criteria used during inference without exposing proprietary logic. This provides verifiable accountability, supports audits, and enables identification of the model owner without revealing sensitive personal information.
