This is a method of data protection that involves encoding information such that it can be reconstructed from a subset of its encoded fragments. The process adds redundant data, allowing the original information to be recovered even if some fragments are lost or corrupted. It is a key technique for ensuring data integrity and availability.
Context
Erasure Coding is a critical technology discussed in the context of decentralized storage networks and data redundancy solutions. Current news often highlights its application in enhancing the robustness of data storage systems against node failures or data loss. Debates frequently touch upon the efficiency of different erasure coding schemes and their impact on storage costs and retrieval speeds within distributed environments.
The Hamster protocol uses coding techniques to reduce Byzantine Fault Tolerance communication to near-optimal O(mn), unlocking linear throughput scaling.
Data Availability Sampling leverages erasure coding to enable light nodes to probabilistically verify block data, fundamentally solving the L2 scaling data bottleneck.
A new sharding consensus pattern achieves provable cross-shard atomicity and optimal intra-shard communication overhead using a jointly managed buffer.
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.
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.
A novel sub-quadratic data availability sampling technique enables asymptotically secure sharding, resolving the critical bottleneck for massive blockchain scaling.
Solana's Alpenglow protocol replaces legacy consensus with Votor and Rotor, achieving sub-second finality and Web2-level responsiveness for decentralized applications.
This research introduces a novel data availability sampling method, enhancing blockchain scalability and security through dynamic, on-the-fly data encoding.
This research introduces novel data availability sampling constructions, enabling robust and efficient data recovery crucial for blockchain scalability.
A novel simulation study dissects Data Availability Sampling parameters, offering critical insights to enhance blockchain scalability and data integrity for decentralized networks.
CrustChain introduces a reputation-weighted Proof-of-Capacity and sharding, achieving unprecedented scalability and security for decentralized storage.
A novel consensus protocol dynamically adjusts data replication and quorum size using erasure coding, enhancing availability and performance in volatile cloud environments.
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