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.
A new vector commitment scheme achieves constant verification time with logarithmic proof size, fundamentally enabling efficient stateless clients and scalable data availability.
Data Availability Sampling leverages erasure coding to enable light nodes to probabilistically verify block data, fundamentally solving the L2 scaling data bottleneck.
This new modular paradigm uses Random Linear Network Coding on uncoded data, yielding dramatically stronger data availability assurances for light nodes.
A new Probabilistically Verifiable Vector Commitment scheme secures Data Availability Sampling, decoupling execution from data and enabling massive asynchronous scalability.
Partition Vector Commitments introduce a novel data structure to drastically reduce proof size and communication overhead, securing data availability for scalable decentralized architectures.
Introducing a novel vector commitment scheme that reduces data availability proof size from linear to logarithmic, fundamentally unlocking scalable decentralized rollups.
PeerDAS radically optimizes data availability for rollups, enabling an eightfold increase in blob throughput to redefine the L2 cost curve and user experience.
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.
A new k-dimensional polynomial commitment scheme drastically reduces data availability overhead, unlocking massive throughput for decentralized rollups.
PANDAS uses direct communication and a two-phase seeding/consolidation model to meet the 4-second DAS deadline, ensuring data availability despite malicious nodes.
A new Data Availability Sampling paradigm commits to uncoded data, enabling on-the-fly coding for verification, which drastically strengthens light client security guarantees.
A novel recursive folding of polynomial commitments into Inner Product Arguments yields universal, transparent proof systems for highly scalable verifiable computation.
By unifying data availability encoding with multilinear polynomial commitments, this research eliminates a major proving bottleneck, enabling faster verifiable computation.
This research introduces Robust Distributed Arrays, a novel distributed data structure that secures the DAS networking layer against malicious actors without relying on an honest majority assumption.
PANDAS, a novel two-phase network protocol, leverages direct communication and PBS to meet the stringent 4-second deadline for large-scale data availability sampling.
BaseFold generalizes FRI, introducing foldable codes to create a field-agnostic polynomial commitment scheme with superior prover and verifier efficiency.
A novel sub-quadratic data availability sampling technique enables asymptotically secure sharding, resolving the critical bottleneck for massive blockchain scaling.
This research introduces a novel data availability sampling method, enhancing blockchain scalability and security through dynamic, on-the-fly data encoding.
The Fusaka upgrade, centered on PeerDAS, rearchitects data availability verification to significantly amplify Layer 2 throughput and reduce operational overhead for nodes.
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