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  1. Architecture

Identity Embedding Module

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Last updated 8 months ago

To effectively aggregate data around users across various chains, applications, and platforms, we require a standardized and flexible system that uniquely represents and evolves over time. This document outlines the use of multi-dimensional user embeddings within a graph structure to contextualize individuals in the Web3 ecosystem. These embeddings are implemented as Non-Fungible Tokens (NFTs) within a knowledge graph system, collecting identity, engagement, interaction, and holdings data into a single node.

Key Concepts

User Embeddings

  • Multi-dimensional Space: Users are represented in a graph structure that allows for the storage and contextualization of various data points.

  • Non-Fungible Tokens (NFTs): These embeddings exist as NFTs in the graph system, encapsulating user identity, engagement, interactions, and holdings.

Identity Binding

  • Web2 and Web3 Identities: Users' Web2 and Web3 identities are linked to NFTs and Soulbound Tokens (SBTs).

  • Aggregated Identity Information: By binding multiple identities, we can verify comprehensive and composable identity information, which enables enhanced on-chain scenarios such as self-authentication, social overlap, and commercial value generation through user targeting.

Implementation Details

Metadata Schema

  • Custom Schema: The binding process involves adding a custom schema in the metadata.

  • Schema Hash: The schema hash is updated and verified within the contract to complete the binding of NFT and identity information.

Merkle Tree Structure

  • Merkle Tree Configuration: In an enhanced version, identities are structured within a Merkle tree, where each leaf represents a distinct ID.

  • Selective Disclosure: Users can disclose specific IDs as needed, concealing their full identity spectrum from the computation layer. This minimizes data leakage risk and enhances data transfer efficiency.

Benefits

  • Self-Authentication: Users can authenticate themselves on-chain using their aggregated identity information.

  • Social Overlapping: The system facilitates social interactions and connections based on verified identity data.

  • Commercial Value: Aggregated identity data enables targeted user engagement, generating commercial value.

Conclusion

The proposed system for user identity aggregation in Web3 leverages NFTs, SBTs, and Merkle tree structures to create a robust and flexible representation of user data. By standardizing and evolving this system, we can ensure secure and efficient data transfer while enabling new on-chain scenarios that benefit individual users.