2PM.Network
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  • Overview
    • What is 2PM.Network
    • Architecture
    • FAQ
    • Official Social Channels
  • 2PM Data VSIES Service
    • What is Data VSIES and why is it important
    • [V] Data Validation (ZK)
    • [SI] Data Standardization and Index
    • [E] Data Encryption Client (FHE)
    • [S] Data Storage and Access
    • Data VSIES SDK
  • Node Framework
    • Modular Architecture
    • Federated Learning
      • Horizontal Federated Learning Task
      • Logistic Regression Task
      • On-chain Secure Aggregation
      • Typical Scenarios
    • FHE Machine Learning
      • Built-in Models
      • Deep Learning
      • Typical Scenarios
    • Task Submission
    • Running a 2PM Node
      • Installation
      • Chain Connector Configuration
      • Data Preparation
      • Joining a Subnet
  • Security and Verification
    • Node Staking and Slash Mechanism
    • Running Verification Client
      • EigenLayer
      • Mind Network
    • Restaking and Delegation
  • Model Inference
    • 2PM Node Inference API
    • Posting Request to a Subnet Model
    • Getting Inference Results on Chain
      • Oracle Adapters
  • Monetization and Incentives
    • AI Model IP Assets
    • Distribution Algorithm
  • 2PM DAO
    • Build Subnets
      • Establishing New Subnets
      • General Requirements
      • Data Schema Definition
      • Model Selection
      • Task Implementation
    • $DMP Token
  • Deployed Smart Contracts
    • Subnets on Testnets
    • Local Deployment Guideline
  • Ecosystem
    • Partners
    • Use Cases
      • Private Personalized Recommendation
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On this page
  • 1. Validation (V)
  • 2. Standardization (S)
  • 3. Index (I)
  • 4. Encryption (E)
  • 5. Storage (S)
  1. 2PM Data VSIES Service

What is Data VSIES and why is it important

VSIES stands for Validation, Standardization, Index, Encryption, and Storage. It represents a comprehensive approach to handling data, which is a critical asset in the field of artificial intelligence. The importance of VSIES within the context of 2PM.Network can be explained through each of its components:

1. Validation (V)

  • Purpose: To ensure the credibility and reliability of data sources.

  • Implementation: 2PM.Network employs zkPass to verify that the data provided by users originates from trusted sources. This step is crucial for maintaining the integrity of the data used in training AI models, as it helps prevent the incorporation of erroneous or manipulated data.

2. Standardization (S)

  • Purpose: To unify data fields and content across distributed datasets.

  • Implementation: By standardizing datasets, 2PM.Network ensures that data contributed from various sources can be effectively integrated and utilized in AI model training. This process involves aligning data formats and schemas to facilitate seamless data aggregation and processing.

3. Index (I)

  • Purpose: To organize data in a way that enhances accessibility and usability.

  • Implementation: 2PM.Network uses an Index method to summarize and catalog similar forms of data, making it easier to build comprehensive and functional datasets. This indexing is typically performed through Data Standardization and Index contracts that manage and facilitate data retrieval.

4. Encryption (E)

  • Purpose: To protect the privacy of data while it is stored and used for computation.

  • Implementation: 2PM.Network provides a Data Encryption Client that allows users to encrypt their data locally using Fully Homomorphic Encryption (FHE) before uploading it to the blockchain. This encryption ensures that data remains confidential and secure from unauthorized access during storage and processing.

5. Storage (S)

  • Purpose: To securely store data in a way that is accessible for computation needs.

  • Implementation: Encrypted data is stored on the blockchain such as 0G, where it is available for use by computational nodes of 2PM.Network. This method of storage not only secures the data but also ensures its availability for authorized processes and applications.

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Last updated 10 months ago