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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→Partitioning Data Structure

→Data Availability Sampling

→Large Scale Data

Partition Vector Commitments Optimize Data Availability and Communication Overhead

Partition Vector Commitments introduce a novel data structure to drastically reduce proof size and communication overhead, securing data availability for scalable decentralized architectures.

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→Cryptographic Commitment

→Layer Two Scalability

→Network Coding

On-The-Fly Coding Dramatically Improves Data Availability Security Assurance

Modularizing data availability by committing to uncoded data and using Random Linear Network Coding for stronger sampling assurance.

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→Cryptographic Primitive

→Sublinear Complexity

→Scalability Trilemma

Logarithmic-Cost Data Availability Sampling Vector Commitments

Introducing a novel vector commitment scheme that reduces data availability proof size from linear to logarithmic, fundamentally unlocking scalable decentralized rollups.

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→Developer Experience

→Gas Cap

→Parallel Execution

Ethereum Fusaka Upgrade Secures Layer Two Scalability with PeerDAS Innovation

PeerDAS radically optimizes data availability for rollups, enabling an eightfold increase in blob throughput to redefine the L2 cost curve and user experience.

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→On-The-Fly Coding

→Data Availability Sampling

→Erasure Code Modularity

New Data Availability Sampling Paradigm Uses On-The-Fly Coding for Stronger Security

The new DAS paradigm commits to uncoded data and uses on-the-fly coding, fundamentally strengthening light node verification assurance.

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→Opening Consistency

→Data Availability Sampling

→Code Binding

Opening-Consistent IOPs Enable Trustless Erasure Code Commitments

This research introduces Erasure Code Commitments, a new primitive constructed via a novel IOP compiler, solving data availability without a trusted setup or high overhead.

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→$k$-Dimensional Encoding

→Tensor Commitment

→Block Data Availability

Hyper-Dimensional Commitment Secures Data Availability Sampling Efficiency and Scalability

A new $k$-dimensional polynomial commitment scheme drastically reduces data availability overhead, unlocking massive throughput for decentralized rollups.

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→P2P Communication

→Block Availability

→Layer Two Scaling

Direct Communication Protocol Secures Data Availability Sampling Efficiency

PANDAS uses direct communication and a two-phase seeding/consolidation model to meet the 4-second DAS deadline, ensuring data availability despite malicious nodes.

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→Data Withholding Attack

→Cryptographic Commitment

→On-The-Fly Coding

Decoupling Data Commitment from Coding Enhances Sampling Security

A new Data Availability Sampling paradigm commits to uncoded data, enabling on-the-fly coding for verification, which drastically strengthens light client security guarantees.

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→Fast Reed-Solomon

→Reed-Solomon Code

→Succinct Non-Interactive Argument

FRI-IOP Establishes Quantum-Resistant Polynomial Commitments for Scalable Proofs

FRI-based polynomial commitments replace pairing-based cryptography with hash-based, quantum-resistant security, enabling transparent, scalable ZK-SNARKs and data availability.