Data Availability Sampling

Definition ∞ Data availability sampling is a technique used in blockchain scalability solutions, particularly rollups, to ensure that transaction data is accessible without requiring every node to download the entire dataset. It allows nodes to probabilistically verify that data has been published, significantly reducing the resource burden on validators. This mechanism is vital for the security and decentralization of layer-2 networks.
Context ∞ The current discussion around data availability sampling is primarily centered on its implementation in optimistic and zero-knowledge rollups, aiming to enhance their security guarantees and decentralization. Key debates involve optimizing the sampling process to minimize overhead while maintaining robust assurances of data availability for all participants.

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→Modular Blockchain

→Code Word Commitment

→Data Availability Sampling

Erasure Code Commitments Achieve Poly-Logarithmic Data Availability Sampling Efficiency

A new compiler translates Interactive Oracle Proofs into erasure code commitments, enabling trustless, poly-logarithmic data availability for modular architectures.

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→Cryptographic Commitment Scheme

→Layer Two Scalability

→Decentralized Data Storage

Network Coding Paradigm Secures Data Availability Sampling beyond Fixed Erasure Codes

Committing to uncoded data with on-the-fly Random Linear Network Coding radically strengthens light node data availability assurances.

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→Stateless Client Verification

→Data Availability Sampling

→Logarithmic Proof Size

Logarithmic Vector Commitment Enables Truly Stateless Verification and Data Availability

Merkle Forest Commitment achieves constant-time verification for massive data sets, fundamentally solving the stateless client and data availability bottleneck.

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→Succinct Data Commitment

→Cryptographic Primitive

→Network Coding

Random Linear Coding Decouples Data Availability Sampling and Commitment

The RLNC-DAS paradigm commits to uncoded data, enabling on-demand, highly efficient sample generation that drastically improves data availability certainty.

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→Blob Data Space

→Layer One Upgrade

→EIP-7594

Ethereum Fusaka Upgrade Unlocks 8x Data Space for Layer Two Rollups

PeerDAS implementation fundamentally scales Ethereum's data availability, creating a massive, permanent cost reduction for the entire Layer Two ecosystem.

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→Network Layer Security

→Data Availability Sampling

→Peer to Peer Networking

Robust Distributed Arrays Secure Data Availability Sampling Networking

Robust Distributed Arrays formally secure the peer-to-peer networking layer of Data Availability Sampling, enabling provably robust blockchain scaling.

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→Cryptographic Primitive

→Multilinear Commitment

→Polynomial Commitment Scheme

Data Availability Encoding Becomes Zero-Overhead Polynomial Commitment Scheme

This work unifies data availability and polynomial commitment schemes, achieving zero prover overhead by cryptographically repurposing data encoding.

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→Reed-Solomon Code

→Optimal Prover Time

→List Decoding Radius

DeepFold Optimizes Zero-Knowledge Proofs with Efficient Multilinear Commitments

DeepFold, a new Reed-Solomon-based polynomial commitment scheme, achieves optimal prover time and concise proofs, unlocking practical, large-scale verifiable computation.

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→Cryptographic Primitive

→Data Integrity

→Commitment Scheme

Sublinear Vector Commitments Enable Trustless Stateless Data Availability

A new vector commitment scheme allows light clients to verify massive datasets with logarithmic communication, fundamentally solving the stateless data availability problem.

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→Succinct Arguments

→Ring SIS Problem

→Data Availability Sampling

Lattice Polynomial Commitments Unlock Concretely Efficient Post-Quantum Zero-Knowledge Arguments

A new lattice-based polynomial commitment scheme drastically shrinks proof size, providing the essential, quantum-safe primitive for future scalable blockchain privacy.