Overview

Vibin’ is building a privacy-first Data Rollup infrastructure focused on music listening data. It securely transforms encrypted Spotify history into validated, high-quality datasets that fuel the decentralized music data economy.

Vibin’ Data Rollup Architecture

User Node (Vibin’ Client)

Users run the Vibin’ client to securely sync and encrypt their Spotify listening history. This data never leaves the user’s device unencrypted, ensuring privacy while contributing to the network. The client also manages wallet connection and referral tracking.

Trusted Execution Environments (TEEs)

Vibin’ leverages TEEs, secure, isolated hardware environments to process encrypted Spotify data safely. TEEs enable the network to validate and analyze user data without ever exposing raw listening histories. This guarantees privacy while ensuring the authenticity and quality of contributed data.

Router

Vibin’ Routers act as intermediaries between user nodes and Validators. They relay encrypted data and metadata while maintaining network accountability. Routers are incentivized through rewards proportional to the volume of validated data they help transmit.

Validator

Validators receive encrypted data batches and use zk-SNARK proofs combined with TEE attestation to verify data validity. They generate cryptographic proofs submitted on-chain to guarantee data provenance and integrity. The validator set will evolve from a centralized model to a decentralized committee.

ZK Processor

The Zero-Knowledge Processor batches validation proofs and submits succinct zk-SNARK proofs to the Ethereum blockchain, creating a permanent, tamper-proof record of validated listening data contributions.

Vibin’ Data Ledger

An immutable ledger that stores validated datasets linked to their corresponding on-chain proofs and TEE attestations. This ensures transparency, auditability and trust in the data powering the Vibin’ ecosystem.

Edge Embedding Models

These models convert validated, encrypted listening data into structured insights such as listening volume, artist diversity and history depth. This processing prepares the data for AI analytics, DAO governance and application development.