Data Availability Committee (DAC)

A DAC introduces a trusted committee model, a significant departure from the trustless security of the underlying blockchain. Users must trust that a supermajority of committee members (e.g., 7 of 10) are honest and will not collude to withhold data. This creates a liveness assumption—if the committee fails, users cannot reconstruct the chain state and prove fraud, potentially leading to frozen funds. This is a key trade-off for scalability.

The primary security risk with a DAC is a data withholding attack. If malicious committee members refuse to provide the data needed to reconstruct a state transition, fraud proofs cannot be submitted. This can allow an invalid state root to be finalized on Layer 1. Mitigations include:

• Committee slashing: Financial penalties for provable non-cooperation.
• Multi-signature schemes: Requiring signatures from a threshold of members to attest to data availability.
• Reputation systems: Relying on established, reputable entities to form the committee.

The security of a DAC hinges on its member selection and incentives. Members are typically known, reputable entities like exchanges, foundations, or infrastructure providers. Their incentive to behave honestly is reputational capital and often financial stakes or bonds that can be slashed. The security model is weaker than economic consensus (Proof-of-Stake) as it relies on social and legal accountability rather than purely cryptoeconomic penalties.

DACs are often contrasted with Data Availability Sampling (DAS), used by validiums and zkPorter. DAS allows light clients to probabilistically verify data availability without trusting a committee, moving towards a trust-minimized model. A DAC is a simpler, more pragmatic solution with higher throughput but introduces explicit trust assumptions, while DAS is more complex but aims for cryptographic security without trusted parties.

Many projects treat a DAC as an interim, training-wheels solution on the path to full decentralization. The roadmap typically involves:

• Increasing committee size and diversity.
• Implementing DAS to reduce reliance on the committee.
• Transitioning to a permissionless validator set for data availability. The end goal is to replace the trusted committee with a cryptoeconomically secure network, eliminating the social trust assumption entirely.

A trust-minimized cryptographic technique that allows light nodes to probabilistically verify data availability by downloading small, random samples of block data. Key aspects include:

• Erasure Coding: Data is encoded so the full block can be reconstructed from a subset of samples.
• Random Sampling: Nodes query for random chunks; if unavailable, the block is rejected.
• Scalability: Enables nodes to verify large data blocks without downloading them entirely, a core innovation of validiums and zk-rollups.

A Layer 2 scaling solution that uses zero-knowledge proofs (ZKPs) for execution validity but relies on an external data availability solution, often a DAC, instead of posting all data to Layer 1. Characteristics:

• High Throughput & Low Cost: No L1 data posting fees.
• Trust Assumption: Security depends on the DAC's honesty for data availability.
• Use Case: Ideal for applications requiring high transaction privacy and volume where users accept a committee-based trust model.

A dedicated blockchain or network layer whose primary function is to guarantee the publication and accessibility of transaction data for other execution layers. Examples and types:

• Celestia: A modular blockchain network designed specifically as a data availability layer.
• EigenDA: A data availability service built on Ethereum using restaking.
• Purpose: Decouples data availability from execution, allowing rollups and other L2s to post data cost-effectively while maintaining security.

The fundamental security trade-off in data availability solutions. Committee-Based (DAC) relies on a known, permissioned set of honest actors. Cryptoeconomic systems (like DAS on L1) use economic incentives and slashing to secure data.

• DAC Model: Faster, cheaper, but introduces trust in specific entities.
• Cryptoeconomic Model: More decentralized and trust-minimized, but can be more expensive and complex.
• Spectrum: Solutions like validiums (DAC) and zk-rollups (L1 posting) represent different points on this spectrum.

A critical data encoding method used in Data Availability Sampling (DAS). It transforms the original data block into an extended set of coded chunks with redundancy.

• Function: Allows reconstruction of the entire data set from any sufficient subset of chunks (e.g., 50% out of 100%).
• Requirement for DAS: Makes random sampling feasible; if any significant portion of data is withheld, samples will fail with high probability.
• Implementation: Commonly uses algorithms like Reed-Solomon encoding.

A hybrid architecture, pioneered by StarkWare, that gives users a choice per transaction between two data availability modes.

• On-Chain Data Availability: Data posted to Ethereum L1 (like a zk-rollup), offering higher security.
• Off-Chain Data Availability: Data held by a DAC (like a validium), offering lower cost.
• User Sovereignty: Allows applications and users to select the appropriate security-cost trade-off for each asset or transaction.