基于熵值理论的区块链网络去中心化程度量化评估方法

去中心化是区块链网络的重要优势。然而,尽管人们普遍认识到这一性质的重要性,但大多数关于这一主题的研究缺乏量化,而且没有对它们在实践中实现的去中心化程度进行计算。熵是随机变量不确定性的度量,也就是数据集的随机性度量,正好可以用来衡量区块链网络的去中心化程度。本文以比特币和以太坊为例,提出一种基于信息论中熵值理论来量化计算这些区块链网络去中心化程度的方法。利用信息熵计算出比特币和以太坊的出块数量和地址余额的随机程度,以此作为量化比特币和以太坊去中心化程度的指标。计算结果表明,基于出块数量的全样本统计,比特币比以太坊要高出大约12%的去中心化程度,并且基于10000个地址余额的样本统计,比特币也比以太坊要高出大约9.2%的去中心化程度。本文提出的方法具有普适性,可以用来量化评估任何区块链网络的去中心化程度。     

Smart contract applications within blockchain technology: A systematic mapping study

With the advent of blockchain, smart contracts have become one of the most sought-after technologies because of the high customisability they add to transactions. This has given rise to many smart contract applications in areas ranging from financial services, life sciences and healthcare to energy resources and voting. However, due to their infancy, smart contracts still pose many challenges that encumber the stakeholders who interact with them: users, developers and the organisations that are built on top of smart contracts. This study aims to contribute to the body of knowledge of smart contracts within blockchain technology. Based on a systematic mapping study, we offer a broad perspective on their problems and corresponding solutions, present the research trends within the area and compile the 64 papers identified, grouped by top publication sources, channels, methods and approaches. We conclude that, since 2016, there has been an increasing trend towards the publication of blockchain-based smart contract articles at conferences and journals, mainly reflecting experiments and presenting methods, tools and models. According to the results, the most commonly discussed problems and solutions in the literature are related to the security, privacy and scalability of blockchain and the programmability of smart contracts.     

A systematic literature review of blockchain cyber security

Since the publication of Satoshi Nakamoto's white paper on Bitcoin in 2008, blockchain has (slowly) become one of the most frequently discussed methods for securing data storage and transfer through decentralized, trustless, peer-to-peer systems. This research identifies peer-reviewed literature that seeks to utilize blockchain for cyber security purposes and presents a systematic analysis of the most frequently adopted blockchain security applications. Our findings show that the Internet of Things (IoT) lends itself well to novel blockchain applications, as do networks and machine visualization, public-key cryptography, web applications, certification schemes and the secure storage of Personally Identifiable Information (PII). This timely systematic review also sheds light on future directions of research, education and practices in the blockchain and cyber security space, such as security of blockchain in IoT, security of blockchain for AI data, and sidechain security.     

A game-theoretic approach of mixing different qualities of coins

Perpetrators leverage the untraceable feature to conduct illegal behaviors leading security issues with respect to mixing coins. Generally, bad coins are blocked based on a common blacklist. However, the blacklist may not be updated in time, which results in that bad coins escape the blocking. Consequently, perpetrators can still conduct illicit behaviors such as money laundering. In this paper, we apply game theory under imperfect information to study how coins' quality restrain these illicit behaviors under the incomplete scenario. More specifically, we propose a strategy for participants to submit deposits if they hope to mix coins with others even if they are not in blacklist at this time. The deposits will not be refunded when participants are included in the blacklist after mixing. Therefore, no participants have incentives to mix with bad coins. At the last part of this paper, we also simulate the incomes for participants, which indicates that deposits strategy is effective to prevent illicit behaviors.     

The effect of propagation delay on the dynamic evolution of the Bitcoin blockchain

This paper analyzes the selfish-mine strategy in the Bitcoin blockchain introduced in 2013 by I. Eyal and E. G. Sirer. This strategy could be used by a colluding pool of miners to earn more than their fair share of the mining revenue and in consequence to force other honest miners to join them to decrease the variance of their revenues and make their monthly revenues more predictable. It is a very dangerous dynamic that could allow the rogue pool of miners to go toward a majority by accumulating powers of news adherents and control the entire network. Considering that the propagation delay of information between any two miners in the network, which is not negligible and follows a normal distribution with mean proportional to the physical distance between the two miners, and a constant variance independent of others' delays, we prove that no guarantee can be given about the success or failure of the selfish-mine attack because of the variability of information propagation in the network.     

Predicting the direction,maximum, minimum and closing prices of daily Bitcoin exchange rate using machine learning techniques

Bitcoin is the most accepted cryptocurrency in the world, which makes it attractive for investors and traders. However, the challenge in predicting the Bitcoin exchange rate is its high volatility. Therefore, the prediction of its behavior is of great importance for financial markets. In this way, recent studies have been carried out on what internal and/or external Bitcoin information is relevant to its prediction. The increased use of machine learning techniques to predict time series and the acceptance of cryptocurrencies as financial instruments motivated the present study to seek more accurate predictions for the Bitcoin exchange rate. In this way, in a first stage of the proposed methodology, different feature selection techniques were evaluated in order to obtain the most relevant attributes for the predictions. In the sequence, it was analyzed the behavior of Artificial Neural Networks (ANN), Support Vector Machines (SVM) and Ensemble algorithms (based on Recurrent Neural Networks and the k-Means clustering method) for price direction predictions. Likewise, the ANN and SVM were employed for regression of the maximum, minimum and closing prices of the Bitcoin. Moreover, the regression results were also used as inputs to try to improve the price direction predictions. The results showed that the selected attributes and the best machine learning model achieved an improvement of more than 10%, in accuracy, for the price direction predictions, with respect to the state-of-the-art papers, using the same period of information. In relation to the maximum, minimum and closing Bitcoin prices regressions, it was possible to obtain Mean Absolute Percentage Errors between 1% and 2%. Based on these results, it was possible to demonstrate the efficacy of the proposed methodology when compared to other studies.     

区块链中双重花费的研究与分析

双重花费攻击又被称为“双花”攻击，即利用比特币的数字特性用“同一笔钱”完成两次或者多次支付。首先总结了双重花费的类型，并对现有的双重花费模型进行对比，分析其具有的优缺点，然后构造了一个预挖掘双重花费模型，考虑节点在进行攻击之前就已经挖掘了一段时间，从而在进行双重花费攻击时具有块数优势。对模型的适用条件进行设置，并对模型进行求解。基于实验得到的数据，与现有模型进行对比验证所构造模型的正确性。最后基于对区块链中双重花费的研究，给出了两个抵御区块链中双重花费攻击的方法，为基于区块链的虚拟货币抵御双重花费攻击提供了一定的参考价值。     

The digital traces of bubbles: feedback cycles between socio-economic signals in the Bitcoin economy

What is the role of social interactions in the creation of price bubbles? Answering this question requires obtaining collective behavioural traces generated by the activity of a large number of actors. Digital currencies offer a unique possibility to measure socio-economic signals from such digital traces. Here, we focus on Bitcoin, the most popular cryptocurrency. Bitcoin has experienced periods of rapid increase in exchange rates (price) followed by sharp decline; we hypothesize that these fluctuations are largely driven by the interplay between different social phenomena. We thus quantify four socio-economic signals about Bitcoin from large datasets: price on online exchanges, volume of word-of-mouth communication in online social media, volume of information search and user base growth. By using vector autoregression, we identify two positive feedback loops that lead to price bubbles in the absence of exogenous stimuli: one driven by word of mouth, and the other by new Bitcoin adopters. We also observe that spikes in information search, presumably linked to external events, precede drastic price declines. Understanding the interplay between the socio-economic signals we measured can lead to applications beyond cryptocurrencies to other phenomena that leave digital footprints, such as online social network usage.     

比特币区块链扩容技术研究

比特币是中本聪(Nakamoto)于2008年提出的数字货币,它具有去中心化、跨国界和发行总量固定等特性,现在已经成为使用最广泛的数字货币之一.然而,比特币设计初期的一些人为限制,导致现有网络处理交易的速率十分有限,最近交易处理能力已经接近上限,交易确认时间显著增加.这不仅严重影响比特币的使用体验,进而限制使用范围,而且对比特币的设计提出更高的要求.针对比特币所面临的交易处理性能挑战,以提升区块链容量为目标对比特币展开深入研究.首先,分析比特币当前网络状态,根据比特币交易数据,统计交易延迟情况;其次,针对链上扩容方案,分析可行性,研究扩容效果;再次,针对链下扩容方案,分析作用原理,研究扩容效果;最后,分析链上/链下扩容方案优缺点,提出适应社区的可行的比特币扩容路线方案.最新比特币扩容的进展进一步证明了我们结论的正确性.     

区块链技术对人工智能的影响

区块链是比特币的支撑技术,具有去中心化、不可篡改、可追溯的特点,在金融、数字版权、公证、物联网、文档存储等领域开始逐步应用并取得了较大成果,已成为与人工智能、大数据、云计算等比肩的热门技术。人工智能建立在海量数据和强大计算力的基础上,区块链技术的特点可以很好地融入到人工智能应用中,从而推动人工智能的进一步发展。文中在介绍了区块链基本概念与工作机制的基础上,重点介绍区块链技术的发展对人工智能的影响,分析了区块链技术应用于人工智能领域的可行性,最后提出展望。