Data AI
  • Getting Started
    • Introduction
  • Overview
    • Data AI Protocol Overview
    • Data Integrators
    • On-Device LLMs
    • Guardian Models
  • Architecture
    • Data AI Infrastructure Overview
    • Secure Computing Infrastructure in Data AI
    • TEE Nodes
  • Tokenomics
    • $DAI Tokenomics
    • $DAI Token Distribution
    • $DAI Utility in the Platform
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  • Benifits of Data Integrators
  • How Data Integrators Operate
  • Advantages of Using Data Integrators
  1. Overview

Data Integrators

Data Integrators are software modules that securely retrieve, process, and organize personal data within Trusted Execution Environments (TEEs). They allow users to connect to and gather information from major Web2 and Web3 platforms—such as Google, Apple, Meta, Amazon, MetaMask, and Phantom—while ensuring data privacy and user control.

Additionally, Data Integrators structure your data into a personal knowledge graph, making it easily accessible and optimized for use by your Personal AI.

Benifits of Data Integrators

  • Selective Data Retrieval: Choose specific sources to fetch only the data you need.

  • Secure Storage: Store retrieved data in a trusted location of your choice.

  • Controlled Access: Manage your Personal AI’s access and usage of your data.

Note: A TEE is a secure area within a device’s main processor that prevents external processes from accessing or altering the data inside.

Key Features of Data Integrators:

  • Secure and Private Data Handling: All data retrieval and processing occur within TEEs, ensuring that sensitive information remains confidential and protected from unauthorized access.

  • Verifiable Data Processing: Data Integrators provide cryptographic proofs confirming that data has been handled securely and in compliance with user permissions.

How Data Integrators Operate

Users of the Data AI app can specify which data sources they wish to connect with, granting Data Integrators permission to fetch and store their information securely. The operation involves the following steps:

  1. Data Collection: The integrator establishes a secure connection to the specified data source. Upon user authorization, it utilizes appropriate API calls with valid access tokens to retrieve the authorized data.

  2. Data Processing: Retrieved data is cleaned, formatted, and structured into a personal knowledge graph, optimizing it for use by on-device LLMs within the Data AI framework.

  3. On-Chain Verification: The integrator submits hardware-generated attestation reports to the Data AI Network, providing verifiable proof that data handling and processing occurred securely within a TEE. These reports confirm:

    • Completion of the assigned data retrieval task by the TEE node.

    • Integrity of the code running inside the TEE, ensuring it matches the certified version approved by the Data AI Protocol.

  4. Data Storage: After verification, the processed data is securely stored in the user’s chosen trusted location—such as their local device, personal cloud storage, or a dedicated secure storage solution. This approach ensures that users retain full control over their data, maintaining privacy and facilitating direct access for their Personal AI.

Advantages of Using Data Integrators

  • Privacy-Preserving Data Handling: All data fetching and processing occur exclusively within a TEE node, preventing unauthorized access or data leakage unless explicitly authorized by the user.

  • User-Controlled Access: Users have complete control over which Data Integrators can access their applications and under what conditions. Data retrieval can be customized based on user-defined parameters, such as scheduled updates or on-demand fetching.

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Last updated 1 month ago