OmniTensor
  • Welcome
  • Introduction
    • What is OmniTensor?
    • Vision & Mission
    • Key Features
    • Why OmniTensor?
      • Current Challenges in AI
      • How OmniTensor Addresses These Challenges
  • Core Concepts
    • AI Grid as a Service (AI-GaaS)
      • Overview of AI-GaaS
      • Benefits of AI-GaaS
      • Use Cases of AI-GaaS
    • Decentralized Physical Infrastructure Network (DePIN)
      • GPU Sharing Model
      • Incentive Mechanisms
    • AI OmniChain
      • Layer 1 and Layer 2 Integration
      • AI Model Marketplace and Interoperability
    • DualProof Consensus Mechanism
      • Proof-of-Work (PoW) for AI Compute
      • Proof-of-Stake (PoS) for Validation
    • OMNIT Token
      • Overview
      • Utility
      • Governance
  • Tokenomics
    • Token Allocations
    • Token Locks
    • ERC20 Token
    • Audit
  • OmniTensor Infrastructure
    • L1 EVM Chain
      • Overview & Benefits
      • Development Tools & API
    • AI OmniChain
      • Interoperability
      • Scalability
      • Decentralized Data & Model Management
    • Nodes & Network Management
      • AI Consensus Validator Nodes
      • AI Compute Nodes (GPUs)
  • Roadmap & Updates
    • Roadmap
    • Future Features
  • PRODUCTS
    • AI Model Marketplace
    • dApp Store
    • Data Layer
    • Customizable Solutions
    • AI Inference Network
  • For the Community
    • Contributing to OmniTensor
      • Sharing Your GPU
      • Data Collection & Validation
    • Earning OMNIT Tokens
      • Computation Rewards
      • Data Processing & Validation Rewards
    • Community Incentives & Gamification
      • Participation Rewards
      • Leaderboards & Competitions
  • For Developers
    • Building on OmniTensor
      • dApp Development Overview
      • Using Pre-trained AI Models
    • SDK & Tools
      • OmniTensor SDK Overview
      • API Documentation
    • AI Model Training & Deployment
      • Training Custom Models
      • Deploying Models on OmniTensor
    • Decentralized Inference Network
      • Running AI Inference
      • Managing and Scaling Inference Tasks
    • Advanced Topics
      • Cross-Chain Interoperability
      • Custom AI Model Fine-Tuning
  • For Businesses
    • AI Solutions for Businesses
      • Ready-Made AI dApps
      • Custom AI Solution Development
    • Integrating OmniTensor with Existing Systems
      • Web2 & Web3 Integration
      • API Usage & Examples
    • Privacy & Security
      • Data Encryption & Privacy Measures
      • Secure AI Model Hosting
  • Getting Started
    • Setting Up Your Account
    • Installing SDK & CLI Tools
  • Tutorials & Examples
    • Building AI dApps Step by Step
    • Integrating AI Models with OmniTensor
    • Case Studies
      • AI dApp Implementations
      • Real-World Applications
  • FAQ
    • Common Questions & Issues
    • Troubleshooting
  • Glossary
    • Definitions of Key Terms & Concepts
  • Community and Support
    • Official Links
    • Community Channels
  • Legal
    • Terms of Service
    • Privacy Policy
    • Licensing Information
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  1. Legal

Privacy Policy

The Privacy Policy governs the collection, processing, and usage of data within the OmniTensor ecosystem. Built on a foundation of blockchain's transparency and user control, OmniTensor strives to ensure that privacy is maintained without compromising the decentralized nature of the network.

Data Collection

OmniTensor operates under a community-powered model, and while some personal information may be gathered for account creation or transactional verification, the platform is designed to minimize personal data collection. Most operations occur via anonymized interactions facilitated by blockchain addresses.

Data Encryption

All data exchanged or stored within OmniTensor, including AI models, training datasets, and transaction records, is encrypted using industry-standard algorithms to ensure privacy during transmission and storage. Decentralized encryption protocols are implemented for models or data requiring higher security.

Decentralized Storage

OmniTensor utilizes a decentralized storage layer, where data is distributed across multiple nodes to reduce the risk of single-point breaches. User data, when applicable, is only accessible through cryptographic keys held by the data owner.

User Control

Users retain control over the data they submit for AI model training or inference. OmniTensor ensures that no proprietary data is shared or utilized without explicit user consent.

Third-Party Access

OmniTensor does not share user data with third parties unless required for specific integrations, and even then, such sharing is conducted in a fully transparent manner, governed by smart contracts.

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Last updated 8 months ago