> For the complete documentation index, see [llms.txt](https://docs.cournot.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.cournot.ai/architecture/privacy-and-confidential-truth.md).

# Privacy & Confidential Truth

While blockchain thrives on transparency, the real world runs on privacy. A prediction market for corporate mergers or supply chain delays requires analyzing sensitive data that cannot be publicly exposed on-chain.

Cournot introduces a "**Confidential Truth**" paradigm: verifying facts without revealing the underlying raw data.

### **Zero-Knowledge Inputs (zkTLS)**

Traditional oracles require data to be public to be verified. This makes it impossible to resolve markets based on private data such as *"Did I receive a transaction confirmation from PayPal?"* or *"Is my private bank balance above $1M?"*

Through **zkTLS**, the Collector Node acts as a blind notary:

* The user generates a proof locally in their browser (Client-Side Proving).
* The proof confirms: *"This data originated from* [*paypal.com*](http://paypal.com) *via a secure TLS session, and the field amount > 1000."*
* **Privacy Guarantee**: The Collector Node verifies the *proof*, but never sees the *content*. No sensitive PII ever leaves the user's device.

### **Eyes-Off Reasoning (TEE Confidential Computing)**

Even if input is private, sending it to an AI model usually means the model operator can see it. This "Reasoning Leakage" is unacceptable for institutional use cases.

All Fast Path inference occurs within **Hardware Enclaves (TEEs like Intel TDX / NVIDIA H100 Confidential Computing)**:

* **Memory Encryption**: The AI model's weights and user input data are encrypted in RAM. Even the physical server owner cannot inspect the reasoning process.
* **Remote Attestation**: The TEE generates a cryptographic quote proving: *"I am running the official, un-tampered Cournot Auditor code, and I processed this specific encrypted input."*

The result is "**Eyes-Off Reasoning**": the AI judges the case, but no human ever sees the evidence.

### **Use Case: Private Prediction Markets (Dark Pools)**

This privacy architecture unlocks enterprise hedging — a new category of markets:

* *Scenario:* A shipping company wants to hedge against a delay in their specific cargo shipment.
* *Mechanism:* They open a prediction market on Cournot. Resolution relies on their private ERP data.
* *Privacy:* Cournot's TEE nodes ingest the private ERP API feed via zkTLS, verify the delay, and settle the market. **Competitors never see the shipping data; they only see the settlement result.**