【Author】
Caldarola, Fabio; D'Atri, Gianfranco; Zanardo, Enrico
【Abstract】To protect participants' confidentiality, blockchains can be outfitted with anonymization methods. Observations of the underlying network traffic can identify the author of a transaction request, although these mechanisms often only consider the abstraction layer of blockchains. Previous systems either give topological confidentiality that may be compromised by an attacker in control of a large number of nodes, or provide strong cryptographic confidentiality but are so inefficient as to be practically unusable. In addition, there is no flexible mechanism to swap confidentiality for efficiency in order to accommodate practical demands. We propose a novel approach, the neural fairness protocol, which is a blockchain-based distributed ledger secured using neural networks and machine learning algorithms, enabling permissionless participation in the process of transition validation while concurrently providing strong assurance about the correct functioning of the entire network. Using cryptography and a custom implementation of elliptic curves, the protocol is designed to ensure the confidentiality of each transaction phase and peer-to-peer data exchange.
【Keywords】blockchain; distributed consensus; neural networks; elliptic cryptographic curves; decentralized applications; tokens; machine learning; confidentiality preserving; cryptocurrencies
【摘要】为了保护参与者的机密性，区块链可以配备匿名化方法。对底层网络流量的观察可以识别交易请求的作者，尽管这些机制通常只考虑区块链的抽象层。以前的系统要么提供拓扑机密性，可能会被控制大量节点的攻击者破坏，要么提供强大的密码机密性，但效率低下以至于实际上无法使用。此外，没有灵活的机制来以保密换效率，以适应实际需求。我们提出了一种新颖的方法，即神经公平协议，它是一种基于区块链的分布式账本，使用神经网络和机器学习算法进行保护，允许无权参与转换验证过程，同时为整个网络的正确运行提供强有力的保证.该协议使用密码学和椭圆曲线的自定义实现，旨在确保每个交易阶段和点对点数据交换的机密性。
【关键词】区块链；分布式共识；神经网络;椭圆密码曲线；去中心化应用程序；代币；机器学习；保密；加密货币
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