通过使用基于学习的声誉和可验证的随机函数来保护区块链分片

Ahmet Bugday, Adnan Ozsoy, H. Sever
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引用次数: 6

摘要

为了满足区块链日益增长的需求,它需要找到可扩展性问题的解决方案。它最近一直专注于分片,以解决可伸缩性问题。在分片方法中,区块链被分成几部分。取而代之的是更广泛的网络,取而代之的是节点更少的网络。因此,网络中每个节点的可靠性变得更加重要。本文对使用分片方法的研究进行了探讨,并提出了将节点分配给分片的方法。在此过程中使用基于学习的自适应方法将有助于安全可靠地使用分片。分片恶化和影响整个区块链的概率将会降低。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Securing Blockchain Shards By Using Learning Based Reputation and Verifiable Random Functions
In order to meet the increasing demand of the blockchain, it needs to find a solution to the scalability problem. It has been focused on sharding recently to address the scalability problem. In the sharding method, the blockchain is divided into pieces. Instead of a more extensive network, networks with fewer nodes are created. As a result, it becomes more important that each node in the network is reliable. In this study, studies using sharding method have been investigated, and methods for the assigning nodes to shards are proposed. The use of learning-based adaptive methods for this process will contribute to the safe and reliable use of shards. The probability of the shards to deteriorate and influence the whole blockchain will be reduced.
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