datahaven: a verifiable decentralized storage network for AI training data and ML models secured by EigenLayer

What it solves

DataHaven provides a verifiable, decentralized storage network specifically designed for production-scale AI training data, machine learning models, and Web3 applications. It solves the problem of trusting third-party storage providers by using cryptographic proofs to ensure data has not been tampered with and remains available.

How it works

DataHaven separates storage from verification. Files are chunked and hashed into Merkle trees, with the roots anchored on-chain for tamper-evident verification. The network uses a two-tier provider model:

• Main Storage Providers (MSPs): Handle user uploads, bucket management, and data retrieval.
• Backup Storage Providers (BSPs): Ensure redundancy through decentralized replication and are subject to periodic proof challenges to prove they are still holding the data.

Security is provided by Ethereum restaking via EigenLayer, where validators are slashed for misbehavior. The system integrates with Ethereum via Snowbridge for trustless cross-chain messaging and supports EVM compatibility through Frontier pallets.

Who it’s for

• AI/ML Researchers and Developers: Those needing to store training datasets and model weights with cryptographic proofs of integrity for verifiable AI pipelines.
• DePIN Projects: Developers building decentralized physical infrastructure that requires provable data lineage for IoT sensor logs.
• Web3 Application Developers: Users requiring immutable, tamper-proof storage for asset documentation and compliance records.

Highlights

• Verifiable Storage: Uses Merkle proofs to verify data integrity without trusting intermediaries.
• EigenLayer Security: Leverages Ethereum restaking for economic security and validator slashing.
• Two-Tier Provider Model: Balances performance (MSPs) and reliability (BSPs).
• Cross-chain Integration: Trustless bridging with Ethereum via Snowbridge and full EVM compatibility.