Rakshit Agrawal, Luca de Alfaro, Vassilis Polychronopoulos
Many prediction problems can be phrased as inferences over local neighborhoods of graphs. The graph represents the interaction between entities, and the neighborhood of each entity contains information that allows the inferences or predictions. We present an approach for applying machine learning directly to such graph neighborhoods, yielding predicitons for graph nodes on the basis of the structure of their local neighborhood and the features of the nodes in it. Our approach allows predictions to be learned directly from examples, bypassing the step of creating and tuning an inference model or summarizing the neighborhoods via a fixed set of hand-crafted features. The approach is based on a multi-level architecture built from Long Short-Term Memory neural nets (LSTMs); the LSTMs learn how to summarize the neighborhood from data. We demonstrate the effectiveness of the proposed technique on a synthetic example and on real-world data related to crowdsourced grading, Bitcoin transactions, and Wikipedia edit reversions.
Estudio de la evolucion de las criptomonedas, desde el tradicional bitcoin al novedoso MUFG, haciendo especial mencion a la tecnologia “blockchain” y a sus potenciales utilidades.
Adem Efe Gencer, Robbert van Renesse, Emin Gün Sirer
The rise of blockchain-based cryptocurrencies has led to an explosion of services using distributed ledgers as their underlying infrastructure. However, due to inherently single-service oriented blockchain protocols, such services can bloat the existing ledgers, fail to provide sufficient security, or completely forego the property of trustless auditability. Security concerns, trust restrictions, and scalability limits regarding the resource requirements of users hamper the sustainable development of loosely-coupled services on blockchains. This paper introduces Aspen, a sharded blockchain protocol designed to securely scale with increasing number of services. Aspen shares the same trust model as Bitcoin in a peer-to-peer network that is prone to extreme churn containing Byzantine participants. It enables introduction of new services without compromising the security, leveraging the trust assumptions, or flooding users with irrelevant messages.
Today, with the use of Internet, a huge volume of data been generated in the form of transactions, logs etc. As assessed, 90% of total volume of data generated since evaluation of Computers is from last 3 years only. It's because of advancements in Data storage, global connectivity with Internet high speed, mobile applications usage and IoT. BigData Technologies aims at processing the BigData for deriving trend analysis and business usage from its BigData information. This paper highlights some of the security concerns that Hadoop implemented in its current version and need for some of the enhancements along with a new methodology such as Electronic Currency (BitCoin) and BlockChain functionality. And also emphasises on why and how BitCoin and BlockChain can fit in Hadoop Eco-Systems and their possible advantages and disadvantages. Especially, in validating and authorizing business transactions with some mathematical cryptographic techniques like hashcode with the help of BlockChain Miners.
The problem of anomaly detection has been studied for a long time, and many Network Analysis techniques have been proposed as solutions. Although some results appear to be quite promising, no method is clearly to be superior to the rest. In this paper, we particularly consider anomaly detection in the Bitcoin transaction network. Our goal is to detect which users and transactions are the most suspicious; in this case, anomalous behavior is a proxy for suspicious behavior. To this end, we use the laws of power degree and densification and local outlier factor (LOF) method (which is proceeded by k-means clustering method) on two graphs generated by the Bitcoin transaction network: one graph has users as nodes, and the other has transactions as nodes. We remark that the methods used here can be applied to any type of setting with an inherent graph structure, including, but not limited to, computer networks, telecommunications networks, auction networks, security networks, social networks, Web networks, or any financial networks. We use the Bitcoin transaction network in this paper due to the availability, size, and attractiveness of the data set.
The problem of anomaly detection has been studied for a long time. In short, anomalies are abnormal or unlikely things. In financial networks, thieves and illegal activities are often anomalous in nature. Members of a network want to detect anomalies as soon as possible to prevent them from harming the network's community and integrity. Many Machine Learning techniques have been proposed to deal with this problem; some results appear to be quite promising but there is no obvious superior method. In this paper, we consider anomaly detection particular to the Bitcoin transaction network. Our goal is to detect which users and transactions are the most suspicious; in this case, anomalous behavior is a proxy for suspicious behavior. To this end, we use three unsupervised learning methods including k-means clustering, Mahalanobis distance, and Unsupervised Support Vector Machine (SVM) on two graphs generated by the Bitcoin transaction network: one graph has users as nodes, and the other has transactions as nodes.
Many countries manipulate the value of their currency or use some form of capital control, yet the data usually used to detect these manipulations are low frequency, expensive, lagged, and potentially mismeasured. I demonstrate that the price data of the internationally traded cryptocurrency Bitcoin can approximate unocial exchange rates which, in turn, can be used to detect both the existence and the magnitude of the distortion caused by capital controls and exchange rate manipulations. However, I document that bitcoin exchange rates contain problematic bitcoin-market-speci c elements and must be adjusted before being used for this purpose. As bitcoin exchange rates exist at a daily frequency, they reveal transitory interventions that would otherwise go undetected. This result also serves as veri cation that Bitcoin is used to circumvent capital controls and manipulated exchange rates.
Roman Matzutt, Oliver Hohlfeld, Martin Henze, Robin Rawiel · 6 authors
As transaction fees skyrocket today, blockchains become increasingly expensive, hurting their adoption in broader applications. This work tackles the saving of transaction fees for economic blockchain applications. The key insight is that other than the existing "default'' mode to execute application logic fully on-chain, i.e., in smart contracts, and in fine granularity, i.e., user request per transaction, there are alternative execution modes with advantages in cost-effectiveness. On Ethereum, we propose a holistic middleware platform supporting flexible and secure transaction executions, including off-chain states and batching of user requests. Furthermore, we propose control-plane schemes to adapt the execution mode to the current workload for optimal runtime cost. We present a case study on the institutional accounts (e.g., coinbase.com) intensively sending Ether on Ethereum blockchains. By collecting real-life transactions, we construct workload benchmarks and show that our work saves 18%\sim 47%18%-47% per invocation than the default baseline while introducing 1.81%\sim 16.59%1.81%-16.59% blocks delay.
Bitcoin (BTC) is often cited as Libertarian. However, the technology underpinning Bitcoin, blockchain, has properties that make it ideally suited to Socialist paradigms. Current literature supports the Libertarian viewpoint by focusing on the ability of Bitcoin to bypass central authority and provide anonymity; rarely is there an examination of blockchain technology’s capacity for decentralised transparency and auditability in support of a Socialist model. This paper conducts a review of the blockchain, Libertarianism, and Socialist philosophies. It then explores Socialist models of public ownership and looks at the unique cooperative properties of blockchain that make the technology ideal for supporting Socialist societies. In summary, this paper argues that blockchain technologies are not just a Libertarian tool, they also enhance Socialist forms of governance.
Hermann Elendner, Simon Trimborn, Bobby Ong, Teik Ming Lee
Crypto-currencies have developed a vibrant market since bitcoin, the first crypto-currency, was created in 2009. We look at the properties of cryptocurrencies as financial assets in a broad cross-section. We discuss approaches of altcoins to generate value and their trading and information platforms. Then we investigate crypto-currencies as alternative investment assets, studying their returns and the co-movements of altcoin prices with bitcoin and against each other. We evaluate their addition to investors' portfolios and document they are indeed able to enhance the diversification of portfolios due to their little co-movements with established assets, as well as with each other. Furthermore, we evaluate pure portfolios of crypto-currencies: an equallyweighted one, a value-weighted one, and one based on the CRypto-currency IndeX (CRIX). The CRIX portfolio displays lower risk than any individual of the liquid crypto-currencies. We also document the changing characteristics of the crypto-currency market. Deepening liquidity is accompanied by a rise in market value, and a growing number of altcoins is contributing larger amounts to aggregate crypto-currency market capitalization.
Cryptocurrencies are a type of digital currencies based on cryptography principles. Cryptocurrencies are a unique combination of three characteristics: they provide anonymity, they are independent of central authority and they provide protection from double spending attack. The aim of this paper is to capture trends in the area of significant cryptocurrencies price developments and to explain their causes. The current research in this area is exclusively limited to an analysis of the price developments of the most important Bitcoin cryptocurrency; our research is the first to focus on other cryptocurrencies too. The economic perspective on cryptocurrencies is based on IT knowledge regarding the principles of their functioning. We have created a database of prices of 1278 cryptocurrencies from 2013 to 2016. This database is publicly available. To analyse the data, SQL query language was used.
Blockchain is a decentralized transaction and data management technology developed first for Bitcoin cryptocurrency. The interest in Blockchain technology has been increasing since the idea was coined in 2008. The reason for the interest in Blockchain is its central attributes that provide security, anonymity and data integrity without any third party organization in control of the transactions, and therefore it creates interesting research areas, especially from the perspective of technical challenges and limitations. In this research, we have conducted a systematic mapping study with the goal of collecting all relevant research on Blockchain technology. Our objective is to understand the current research topics, challenges and future directions regarding Blockchain technology from the technical perspective. We have extracted 41 primary papers from scientific databases. The results show that focus in over 80% of the papers is on Bitcoin system and less than 20% deals with other Blockchain applications including e.g. smart contracts and licensing. The majority of research is focusing on revealing and improving limitations of Blockchain from privacy and security perspectives, but many of the proposed solutions lack concrete evaluation on their effectiveness. Many other Blockchain scalability related challenges including throughput and latency have been left unstudied. On the basis of this study, recommendations on future research directions are provided for researchers.
The ability to predict financial markets has tremendous potential to limit exposure\nto risk and provide better assurances of annualized gains. In this thesis, a\nmodel for predicting the future daily price of Bitcoin is proposed and evaluated\nin comparison to that of a purely random model. Bitcoin is a novel digital currency\nthat relies on cryptography instead of a central authority to verify transactions.\nWithout a central authority, Bitcoin requires a complete list of all transactions\nto be made public so that they can be verified by all users. This unique\nfeature of Bitcoin, where all transactions are public, is exploited by the model\nto predict the future price directions based on the actions of Bitcoin users. The\ndaily activity of the markets, aggregate network features, and the actions of major\nnetwork influencers are all used as features for the predictive model. Where major\nnetwork influencers are defined as users that accumulate a disproportionate\namount of wealth within the Bitcoin network compared to others. The information\nabout the actions of all Bitcoin users are extracted from the blockchain and\nstored in a relational database for ease of use. Two metrics were created to identify\nthe major network influencers based on the history of their actions recorded\non the blockchain. The first metric, based on the concept of an h-index, often\nused in academia to rank authors by their citations, is used to rank users by the\namount of wealth they accumulate monthly. The second metric is based on the\noptimization of multiple objectives, the maximum increase in wealth with the\nleast amount of activity using Pareto optimization. All of the major network influencers\nidentified were then used as features, in combination with aggregate\nnetwork features, and market data, to test and evaluate several predictive models.\nThe models created were based on non-linear equations, support vector machines,\ndecision trees, and XGBoost; all evaluated and compared using the same\ndata. The XGBoost model consistently proved to be much more accurate than all\nother models and was used for the final set of experiments. The XGBoost model\nwas compared to that of a purely random Monte Carlo model using the entire\nhistory of data for the period of 2013???2016. The first set of experiments were conducted\nusing various sizes of training and testing data, in each case the XGBoost\nmodel had an accuracy 20% greater than that of the Monte Carlo model. For\nthe final experiments, the model was tested in a realistic scenario, predicting the\nprice direction for each future day, while also being re-trained using the results\nof each new day. The XGBoost model achieved a much better performance in\ncomparison to the Monte Carlo model, which had approximately 50% accuracy,\nwhereas the XGBoost model had 70%???79% accuracy.
Emmanuelle Anceaume, Thibaut Lajoie-Mazenc, Romaric Ludinard, Bruno Sericola
Decentralized cryptocurrency systems offer a medium of exchange secured by cryptography, without the need of a centralized banking authority. Among others, Bitcoin is considered as the most mature one. Its popularity lies on the introduction of the concept of the blockchain, a public distributed ledger shared by all participants of the system. Double spending attacks and blockchain forks are two main issues in blockchain-based protocols. The first one refers to the ability of an adversary to use the very same bitcoin more than once, while blockchain forks cause transient inconsistencies in the blockchain. We show through probabilistic analysis that the reliability of recent solutions that exclusively rely on a particular type of Bitcoin actors, called miners, to guarantee the consistency of Bitcoin operations, drastically decreases with the size of the blockchain.
The value of bitcoin depends upon self‐fulfilling beliefs that are hard to pin down. We demonstrate this for the case where bitcoin is the only form of money in the economy and then generalize the message to the case of multiple bitcoin clones and/or a competing fiat currency. Some aspects of the indeterminacy we describe would no longer hold if bitcoin were an interest‐bearing object. ( JEL D50, E42)
A trustless technology, Bitcoin tries to solve issues of social coordination and economic exchange by relying exclusively on technological means. Is technology alone able to resolve the social and political concerns affecting the Bitcoin network?
Elie Bouri, Péter Molnár, Georges Azzi, David Roubaud · 5 authors
This paper uses a dynamic conditional correlation model to examine whether Bitcoin can act as a hedge and safe haven for major world stock indices, bonds, oil, gold, the general commodity index and the US dollar index. Daily and weekly data span from July 2011 to December 2015. Overall, the empirical results indicate that Bitcoin is a poor hedge and is suitable for diversification purposes only. However, Bitcoin can only serve as a strong safe haven against weekly extreme down movements in Asian stocks. We also show that Bitcoin hedging and safe haven properties vary between horizons.
Bitcoin is a decentralised currency and payment system that seeks to eliminate the need for trusted authorities. It relies on a peer-to-peer network and cryptographic protocols to perform the functions of traditional financial intermediaries, such as verifying transactions and preserving the integrity of the system. This article examines the political economy of Bitcoin, in light of a recent dispute that divided the Bitcoin community with regard to a seemingly simple technical issue: whether or not to increase the block size of the Bitcoin blockchain. By looking at the socio-technical constructs of Bitcoin, the article distinguishes between two distinct coordination mechanisms: governance by the infrastructure (achieved via the Bitcoin protocol) and governance of the infrastructure (managed by the community of developers and other stakeholders). It then analyses the invisible politics inherent in these two mechanisms, which together display a highly technocratic power structure. On the one hand, as an attempt to be self-governing and self-sustaining, the Bitcoin network exhibits a strong market-driven approach to social trust and coordination, which has been embedded directly into the technical protocol. On the other hand, despite being an open source project, the development and maintenance of the Bitcoin code ultimately relies on a small core of highly skilled developers who play a key role in the design of the platform.
Motivated by the great success and adoption of Bitcoin, a number of cryptocurrencies such as Litecoin, Dogecoin, and Ethereum are becoming increasingly popular. Although existing blockchain-based cryptocurrency schemes can ensure reasonable security for transactions, they do not consider any notion of fairness. Fair exchange allows two players to exchange digital items, such as digital signatures, over insecure networks fairly, so that either each player gets the other's item, or neither player does. Given that blockchain participants typically do not trust each other, enabling fairness in existing cryptocurrencies is an essential but insufficiently explored problem. In this paper, we explore the solution space for enabling the fair exchange of a cryptocurrency payment for a receipt. We identify the timeliness of an exchange as an important property especially when one of the parties involved in the exchange is resource-constrained. We introduce the notion of strong timeliness for a fair exchange protocol and propose two fair payment-for-receipt protocol instantiations that leverage functionality of the blockchain to achieve strong timeliness. We implement both and compare their security and efficiency.
Motivated by the great success and adoption of Bitcoin, a number of cryptocurrencies such as Litecoin, Dogecoin, and Ethereum are becoming increasingly popular. Although existing blockchain-based cryptocurrency schemes can ensure reasonable security for transactions, they do not consider any notion of fairness. Fair exchange allows two players to exchange digital "items", such as digital signatures, over insecure networks fairly, so that either each player gets the other's item, or neither player does. Given that blockchain participants typically do not trust each other, enabling fairness in existing cryptocurrencies is an essential but insufficiently explored problem. In this paper, we explore the solution space for enabling the fair exchange of a cryptocurrency payment for a receipt. We identify the timeliness of an exchange as an important property especially when one of the parties involved in the exchange is resource-constrained. We introduce the notion of strong timeliness for a fair exchange protocol and propose two fair payment-for-receipt protocol instantiations that leverage functionality of the blockchain to achieve strong timeliness. We implement both and compare their security and efficiency.
Financial technologies embody and shape notions of social, as well as financial, worth. New digital ‘alt-finance’ systems, including the blockchain technology underlying Bitcoin and similar ‘cryptocurrencies,’ are no exception: technology, rhetoric, imagined users and non-users, and a long history of sociotechnical, political, and cultural relations are all elements in a dynamic assemblage with wide-ranging consequences. This paper examines the rise and fall of one alt-finance system: MazaCoin, a Bitcoin variant intended to benefit the Oglala Lakota of the Pine Ridge Indian Reservation. The story of MazaCoin is one of an attempt to unite two apparently divergent sociotechnical assemblages: (1) a libertarian, elite technology of cryptocurrency, and (2) a richly traditional indigenous community with a deep desire for cultural survivance, bound up in a precarious economy left behind in the wake of more than a century of genocide.
We provide an extreme value analysis of the returns of Bitcoin. A particular focus is on the tail risk characteristics and we will provide an in-depth univariate extreme value analysis. Those properties will be compared to the traditional exchange rates of the G10 currencies versus the US dollar. For investors - especially institutional ones - an understanding of the risk characteristics is of utmost importance. So for bitcoin to become a mainstream investable asset class, studying these properties is necessary. Our findings show that the bitcoin return distribution not only exhibits higher volatility than traditional G10 currencies, but also stronger non-normal characteristics and heavier tails. This has implications for risk management, financial engineering (such as bitcoin derivatives) - both from an investor's as well as from a regulator's point of view. To our knowledge, this is the first detailed study looking at the extreme value behaviour of the cryptocurrency Bitcoin.
Different versions of peer-to-peer electronic cash exist as data represented by separate blockchains. Payments between such systems cannot be sent directly from one party to another without going through a financial institution. Bitcoin provided part of the solution but its utility is limited to intra-blockchain transactions. The benefits are lost if a trusted third party is required to execute inter-blockchain transactions. We propose a solution to the inter-blockchain transaction problem using the same fundamental principles of Bitcoin. The protocol is described by the Uberledger framework, a hierarchical meta-blockchain layer that encapsulates information regarding the fidelity of peer-to-peer transaction facilitators.