PrivateInsight: TEE-Powered AI Analytics for Confidential Data

kamalbuilds · June 15, 2025, 11:35am

Project Name

Problem Statement

Organizations and individuals possess valuable private data (medical records, financial transactions, business analytics) but cannot leverage AI insights without exposing sensitive information. Traditional AI analytics require data to be exposed to third parties, creating privacy risks and compliance issues. Current solutions either compromise on privacy or significantly limit analytical capabilities.

Solution Overview

PrivateInsight uses Alith’s TEE (Trusted Execution Environment) capabilities combined with Hyperion’s on-chain AI inference to create a privacy-preserving analytics platform. Users can submit encrypted datasets, receive AI-powered insights, and maintain complete data sovereignty while benefiting from advanced analytics powered by MetisVM’s AI optimization features.

Project Description

PrivateInsight revolutionizes private data analytics through a sophisticated architecture that combines several cutting-edge technologies:

Core Technical Architecture:

Alith TEE Integration: Utilizes Alith’s secure enclave capabilities to process sensitive data without exposure
On-Chain AI Inference: Leverages MetisVM’s AI coprocessor acceleration and specialized opcodes for privacy-preserving machine learning
LazAI Data Anchoring: Implements the Data Anchoring Token (DAT) system to create verifiable, tokenized AI insights with provenance tracking
Encryption-First Design: Uses Alith’s native encryption/decryption capabilities with RSA and AES for data protection

Key Features:

Confidential Analytics Engine: Processes encrypted healthcare, financial, or business data using Alith agents with specialized tools for statistical analysis, pattern recognition, and predictive modeling
Verifiable AI Insights: All AI computations are anchored on-chain through LazAI’s proof system, ensuring insights are tamper-proof and auditable
Data Sovereignty: Users retain complete control over their data - it never leaves the TEE unencrypted
Multi-Model Support: Supports various AI models (regression, classification, clustering, NLP) through MetisVM’s hybrid development environment
Reward Distribution: Contributors of valuable datasets earn DAT tokens through LazAI’s reward mechanism

Technical Implementation:

Built using Alith Node SDK with privacy data handling and LazAI integration
Utilizes MetisDB’s MVCC for concurrent processing of multiple analytics jobs
Implements parallel execution through Metis SDK’s Block-STM for high-throughput analysis
Integrates IPFS for decentralized storage of encrypted datasets

Users can submit datasets through a simple interface, specify analytical requirements in natural language, and receive comprehensive insights while maintaining complete privacy.

Community Engagement Features

Testable Features & Gamification:

Privacy Score Challenge (30 points): Submit sample datasets and verify they remain encrypted throughout analysis
AI Insight Accuracy Test (50 points): Compare AI predictions against known results using anonymized public datasets
TEE Verification Challenge (75 points): Validate that computations actually run in secure enclaves by checking proof signatures
Multi-Model Benchmark (100 points): Test different AI models on the same dataset and compare performance
Data Contribution Rewards (150 points): Contribute valuable encrypted datasets and earn DAT tokens
Privacy-Preserving DAO (200 points): Participate in governance decisions about analytics models using zero-knowledge voting

Leaderboard System: Users earn “Privacy Pioneer” badges based on successful analytics jobs, data contributions, and security testing. Top contributors get early access to new AI models and premium analytics features.

Getting Involved

Join our Telegram community (Telegram: Join Group Chat) where privacy advocates, data scientists, and blockchain developers collaborate. We’re seeking contributors with expertise in cryptography, TEE development, and AI/ML. Regular workshops demonstrate privacy-preserving analytics techniques and LazAI integration patterns.

Vidhika · June 15, 2025, 5:16pm

amazing project idea

han · June 16, 2025, 6:32am

Looks really interesting! Here are five questions about PrivateInsight:

1- How does the Trusted Execution Environment (TEE) ensure that sensitive data remains completely private during AI processing?

2- In what ways does the LazAI Data Anchoring Token (DAT) system verify and track the provenance of AI-generated insights?

3-Can users choose which AI models (e.g., regression, NLP) to apply to their encrypted datasets, and how flexible is this selection?

4- How are contributors rewarded with DAT tokens, and what incentives exist for sharing valuable datasets?

5-What measures are in place to ensure that the AI computations anchored on-chain remain tamper-proof and auditable over time?

priyankg3 · June 16, 2025, 6:42pm

Hello @kamalbuilds , how are you?

When someone contributes encrypted datasets and earns DAT tokens, how do you ensure data uniqueness and prevent duplicate dataset submissions gaming the reward system?
With on-chain anchoring of AI outputs via LazAI , is there a way to ensure that those insights don’t unintentionally leak sensitive patterns, especially when multiple datasets are analyzed together?

kamalbuilds · June 20, 2025, 6:39am

Hey @han too many good questions you asked , I will try my best to answer each of them one by one. As these might be helpful to ones that are curious about PrivateInsight.

Q1 How does the Trusted Execution Environment (TEE) ensure that sensitive data remains completely private during AI processing?

In our PrivateInsight platform, I implemented TEEs as hardware-based secure enclaves that create an isolated, encrypted environment for all AI computations. When users submit their data to our platform, the TEE ensures complete privacy through multiple layers of protection:

Hardware-Level Isolation: Our TEEs use Intel TDX (Trusted Domain Extensions) and AMD SEV-SNP (Secure Encrypted Virtualization) to create cryptographically isolated virtual machines where data is encrypted even during processing. This means that even I, as the platform operator, or the node operators running the infrastructure, cannot access the raw data being processed.

Memory Encryption: All data in memory is encrypted using hardware-protected keys that are inaccessible to any software outside the TEE. This protects against physical memory attacks and ensures that sensitive information never appears in plaintext in system memory.

Attestation-Based Trust: Before any data processing begins, our platform performs remote attestation to cryptographically verify the integrity of the TEE environment. Users can independently verify that their data is being processed in a genuine, uncompromised secure environment through our on-chain attestation system integrated with services like Automata Network.

End-to-End Encryption Workflow: When users submit data, it’s encrypted using the TEE’s public key before transmission. Only the specific TEE instance assigned to process the data can decrypt it using its private key generated within the secure hardware. The AI model inference happens entirely within this encrypted environment, and results are re-encrypted before being returned to the user.

This approach ensures that sensitive healthcare data, financial information, or proprietary business intelligence remains completely private throughout the entire AI processing pipeline, enabling us to provide enterprise-grade privacy guarantees for our decentralized AI platform.

kamalbuilds · June 20, 2025, 6:41am

TEE wasn’t implemented in the Alith SDK yet , so yesterday I made a PR that Implements Phala TEE in the Alith Node SDK

github.com/0xLazAI/alith

🔒 Phala TEE Integration into the Alith Node SDK

main ← kamalbuilds:tee

opened 12:28PM - 19 Jun 25 UTC

kamalbuilds

+4987 -3179

This PR aims to Implement 🔒 **Phala TEE in the Alith Node SDK** ### **1. Adde…d Phala Dependency** - Added `@phala/dstack-sdk` to the Alith Node SDK dependencies - This provides access to Phala's TappdClient for TEE operations ### **2. Created TEE Module (`/src/tee.ts`)** The new TEE module provides comprehensive TEE functionality: **Core Classes:** - **`TEEClient`** - Main Phala TEE integration client - **`TEEAgent`** - TEE-enabled agent wrapper - **`TappdClient`** - Direct export from Phala SDK **Key Features:** - **Remote Attestation**: Generate cryptographic proofs of TEE execution - **Key Derivation**: Secure key generation within TEE environment - **Secure Execution**: Execute operations with hardware-level security - **Signature Verification**: Sign and verify TEE operation results - **Secure Channels**: Create encrypted communication channels ### **3. Enhanced Agent Class** Updated the main `Agent` class to support TEE operations: **New TEE Features:** - **`teeConfig`** option in `AgentOptions` - **`promptSecure()`** - Explicit TEE-secured prompt processing - **`generateAttestation()`** - Generate agent-specific attestation - **`getTEEStatus()`** - Check TEE health and capabilities - **`isTEEEnabled()`** - Check if TEE is configured **Automatic TEE Integration:** - When `teeConfig` is provided, all `prompt()` calls automatically use TEE - Maintains backward compatibility for non-TEE deployments ### **4. Integration Architecture** The implementation follows the pattern mentioned in the Rust crate: ```typescript // Similar to Rust: use phala_tee_sdk::TappdClient; import { TappdClient } from '@phala/dstack-sdk' // Create TEE-enabled agent const agent = new Agent({ name: 'SecureAgent', model: 'gpt-4', teeConfig: { endpoint: 'http://localhost:8090', enableAttestation: true, enableKeyDerivation: true, enableSignatures: true } }) // All operations now have TEE security guarantees const result = await agent.prompt('Analyze sensitive data') ``` ### **5. Security Features Implemented** **Hardware-Level Security:** - Intel TDX integration through Phala's SDK - Remote attestation with cryptographic verification - Tamper-proof execution environment **Privacy Preservation:** - End-to-end encryption within TEE - Secure key derivation that never leaves TEE - Private ML inference with verifiable results **Verification & Audit:** - Cryptographic signatures on all TEE operations - Attestation proofs for regulatory compliance - Verifiable computation results ## 🚀 **Usage Examples** ### **Basic TEE Agent:** ```typescript import { Agent, TEEConfig } from 'alith' const teeConfig: TEEConfig = { endpoint: 'http://localhost:8090', enableAttestation: true, enableKeyDerivation: true } const agent = new Agent({ name: 'PrivateInsightAgent', model: 'gpt-4', teeConfig, preamble: 'You are a privacy-preserving AI agent running in TEE' }) ``` ### **Secure Operations:** ```typescript // Automatic TEE security const result = await agent.prompt('Analyze encrypted user data') // Explicit TEE with full verification const secureResult = await agent.promptSecure('Process sensitive data', { attestUserData: 'user-data-analysis', signResult: true, includeAttestation: true }) ``` ### **Attestation & Verification:** ```typescript // Generate attestation proof const attestation = await agent.generateAttestation() // Check TEE status const status = await agent.getTEEStatus() console.log('TEE Healthy:', status.healthy) ```

han · June 20, 2025, 8:01am

Thank you for the thorough explanation that clears it up well. It’s reassuring to see such a strong privacy-first approach built into PrivateInsight. Appreciate the detailed breakdown!