‘Scalable Blockchains’ are distributed ledger technologies designed to process a high volume of transactions efficiently and affordably. Unlike early blockchain architectures that faced limitations in throughput, scalable blockchains employ various mechanisms such as sharding, sidechains, or advanced consensus algorithms. Their capacity to handle increased network activity without significant degradation in performance is fundamental for widespread adoption of decentralized applications. These systems aim to support global-scale usage.
Context
The pursuit of ‘Scalable Blockchains’ remains a primary objective for the entire digital asset ecosystem. Significant advancements are being made in Layer-2 solutions and protocol upgrades designed to boost transaction capacity and reduce latency. Discussions frequently address the trade-offs between decentralization, security, and scalability, as different projects adopt distinct architectural approaches to achieve enhanced performance.
By unifying data availability encoding with multilinear polynomial commitments, this research eliminates a major proving bottleneck, enabling faster verifiable computation.
HyperPlonk introduces a new polynomial commitment scheme, achieving a universal and updatable setup with dramatically faster linear-time proving, enabling mass verifiable computation.
Zero-knowledge proofs are transitioning from theoretical cryptography to practical applications, offering scalable privacy and verifiable computation across decentralized systems.
This research introduces a novel asymmetric gather protocol, enabling DAG-based consensus mechanisms to operate efficiently under diverse, subjective trust assumptions, fostering more resilient and scalable blockchains.
This research introduces novel mechanism design principles to fortify blockchain consensus, ensuring truthful block proposals and mitigating fork-related coordination failures.
HyperNova introduces a recursive zero-knowledge proof system that significantly reduces overhead for high-degree constraint computations, enabling more practical verifiable virtual machines.
A novel Zero-Knowledge Proof of Training (ZKPoT) consensus mechanism uses zk-SNARKs to validate model performance, enabling private, scalable federated learning.
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