Bitcoin is an established cryptographic digital currency whose value lays in the computational complexity rather than a physical commodity. Bitcoin is an open source software program with three aspects. (i) Peer-to-Peer networklow barrier entry; (ii) Mininginevitable concentration of power; (iii) Software upgrades. The nodes on the network follow a decentralized consensus for establishing the value of ledger and updating the blockchain which serves as a single source of truth for all transactions. As cryptocurrencies are developing more compelling utilities, creating ever faster and safer payment systems they are shifting the "money paradigm". Bitcoins are an evolution in money and provide a unique opportunity to forecast their price unlike the existing fiat currencies. The goal of this paper is to implement, train and evaluate several machine learning models in order to predict the price of the most popular cryptocurrency -Bitcoins. The various machine learning algorithms employed are -Linear Regression, K-Nearest Neighbors, Ridge Regression, Lasso Regression,
As a consequence of the blockchain revolution, a key challenge of our times is to identify the legal boundaries of smart contracts and thus to develop conflict rules for divergences between state law and technology-based code. Even if smart contracts are technologically self-executing, they are not necessarily legally enforceable. Rather, they must satisfy a variety of legal and contract law requirements. Two different levels of such rules can be differentiated, namely rules of recognition and substantive restrictions. At both levels, it emerges that either the lawmaker can intervene and introduce new, specific rules, or the judiciary can develop rules on the basis of existing and more general legal standards. For example, at the European level the Unfair Terms Directive and (in future) the Directive on Contracts for the Supply of Digital Content limit the potential scope of smart contracts. At national level, the rules on self-help constitute a crucial legal boundary. At least some applications of blockchain technology will be subject to these rules, strictly limiting their admissibility. Under German law, for instance, a waiver of the relevant provisions is largely excluded. In the case of cross-border situations, the comparative divergence of self-help rules will create legal uncertainty and may hinder the use of smart contracts.
The prevalence of distributed energy resources encourages the concept of an electricity “Prosumer (Producer and Consumer)”. This paper proposes a distributed electricity trading system to facilitate the peer-to-peer electricity sharing among prosumers. The proposed system includes two layers. In the first layer, a multi-agent system is designed to support the prosumer network, and an agent coalition mechanism is proposed to enable the prosumers to form coalitions and negotiate electricity trading. In the second layer, a Blockchain based transaction settlement mechanism is proposed to enable the trusted and secure settlement of electricity trading transactions formed in the first layer. Simulations are conducted based on the java agent development environment to validate the proposed electricity trading process.
Logistics management plays a crucial role in the execution and success of international trades. Current solutions for logistics management suffer from a number of issues. One of the major areas for improvement is inconsistent data and lack of trust and transparency between multiple participants. Distributed Ledger Technology (DLT) or blockchain has the inherent characteristics to address this issue, and is well suited to be applied to supply chain document and workflow management. This paper demonstrates how to apply DLT to achieve a higher level of efficiency through consistent data store, automated workflow process, and tamper-proof transaction history for provenance in the supply chain.
Sara Saberi, Mahtab Kouhizadeh, Joseph Sarkis, Lejia Shen
Globalisation of supply chains makes their management and control more difficult. Blockchain technology, as a distributed digital ledger technology which ensures transparency, traceability, and security, is showing promise for easing some global supply chain management problems. In this paper, blockchain technology and smart contracts are critically examined with potential application to supply chain management. Local and global government, community, and consumer pressures to meet sustainability goals prompt us to further investigate how blockchain can address and aid supply chain sustainability. Part of this critical examination is how blockchains, a potentially disruptive technology that is early in its evolution, can overcome many potential barriers. Four blockchain technology adoption barriers categories are introduced; inter-organisational, intra-organisational, technical, and external barriers. True blockchain-led transformation of business and supply chain is still in progress and in its early stages; we propose future research propositions and directions that can provide insights into overcoming barriers and adoption of blockchain technology for supply chain management.
Alexander Ivanov, Yevhenii Babichenko, Hlib Kanunnikov, Paul Karpus · 8 authors
Blockchain as a technology is rapidly developing, finding more and more new entry points into everyday life. This is one of the elements of the technical Revolution 4.0, and it is used in the field of supply, maintenance of various types of registers, access to software products, combating DDOS attacks, distributed storage, fundraising for projects, IoT, etc.Nowadays, there are many blockchainplatforms in the world. They have one technological root but different applications. There are many prerequisites to the fact that in the future the number of new decentralized applications will increase. Therefore, it is important to develop a methodology for determining the optimal blockchainbased platform to solve a specific problem. As an example, consider the worldfamous platforms Ethereum, Nem, and Stellar. Each of them allows to develop decentralized applications, issue tokens, and execute transactions. At the same time, the key features of these blockchainbased platforms are not similar to one another. These very features will be considered in the article.Purpose. Identify the key parameters that characterize the blockchainbased platforms. This will provide an opportunity to present a complex blockchain technology in the form of a simple and understandable architecture. Based on these parameters and using the expertise of the article’s authors, we will be able to develop a methodology to be used to solve the problems of choosing the optimal blockchainbased platform for solving the problem of developing smart contracts and issuing tokens.Methods. Analysis of the complexity of using blockchainbased platforms. Implementation of token issuance, use of test and public networks, execution of transactions, analysis of the development team and the community, analysis of the user interface and the developer interface.Discussion. By developing a platform comparison methodology to determine optimal characteristics, we can take the development process to a new level. This will allow to quickly and effectively solve the tasks.Results. Creation of a methodology for comparison blockchainbased platforms.
his article examines the risks and opportunities associated with distributed ledgers technologies. A novel methodology is developed and presented in order to diagnose and manage their impacts using a strategic risk management perspective. Possible responses to the identified challenges are suggested.
This article examines the labor power of digital miners. Though an obscure and still incipient facet of the digital economy, crypto‐mining powers and secures transactions across blockchains, or public distributed digital ledgers. Drawing from interviews with cryptocurrency enthusiasts, blockchain advocates, and developers; participation in online and offline discussions; and a survey with small‐scale crypto‐miners, this article takes on the material and technoscientific valuation of crypto‐mining to understand how a future of open, decentralized accountability implicates human labor alongside automated processes. The work of digital mining, performed in the work of inscribing, registering, and politically organizing mining operations, enables the formation of democratic communities in the digital economy and remains inevitably embedded in social relations as a mode of productive, meaningful action.
Distributed ledger technology is attracting the attention of the financial sector, both owing to its use in transactions with crypto-assets and to the proliferation of initiatives which have the potential to increase the efficiency, transparency, speed and resilience of processes underlying financial transactions. This article aims to introduce this technology, describing a series of basic issues surrounding it and attempting to identify opportunities and intrinsic limitations. Additionally, it addresses possible applications in the financial sector and outlines some of the main challenges which its use poses for the authorities.
Alan T. Sherman, Farid Javani, Haibin Zhang, Enis Golaszewski
We explore the origins of blockchain technologies to better understand the enduring needs they address. We identify the five key elements of a blockchain, show embodiments of these elements, and examine how these elements come together to yield important properties in selected systems. To facilitate comparing the many variations of blockchains, we also describe the four crucial roles of blockchain participants common to all blockchains. Our historical exploration highlights the 1979 work of David Chaum whose vault system embodies many of the elements of blockchains.
The chapter sets out to show a chain between the many aspects of Bitcoin as a regime of exchange and contract-based transactions, and to lay a basic groundwork of understanding for Bitcoin as a framework for transactions. Relevant aspects are Bitcoin’ s status as money, the different and functional qualities that it shares with other types of money, its distributed technical nature and structural integrity, as well as its ideological roots incorporated in technology. An exciting addition onto the open final link of this chain is the advent of new decentralised Bitcoinbased marketplaces where the goal is to include the transactions themselves inside Bitcoin's ‘blockchain’ – the foundation of this digital regime of exchange. Historical lines are drawn between Bitcoin and the ancient cultures of Mesopotamia in 3000 BC. While the attributes of the monetary system used in the Cradle of Civilisation are the antithesis of Bitcoin when it comes to the dichotomy of geographical centralisation/decentralisation, it evinces strong similarities concerning the relative unimportance of material tokens. A further shared trait is traced, one that is likely to prove revolutionary in the future: the intimate connection between the monetary system itself and the transactions performed using it. The Silk Road was launched in 2011. That is to say, this was when an online marketplace that called itself the ‘Silk Road’ was launched. The principal items on sale at this digital market were, unsurprisingly, various illicit substances, and the parties frequenting this marketplace settled their transactions by a direct transfer of something called ‘Bitcoin’. In this online version of buying drugs for cash in dark back-alleys, there were no banks or bank-backed payment systems involved, only this money-like thing called ‘Bitcoin’, which purported to sustain its integrity upon a technology without a central point of control. Now, years after the new Silk Road was shut down, Bitcoin is being used by wholly legal online stores as well as some banks and even state authorities. HIGH PRIEST NAKAMOTO The key realization was that there’ s no difference between modern culture and Sumerian. We have a huge workforce that is illiterate or alliterate and relies on TV – which is a form of oral tradition. And we have a small, extremely literate power elite … who control society because they have this semimystical ability to speak magic computer languages.
Rosario Gennaro, Michele Minelli, Anca Nitulescu, Michele Orrù
Zero-knowledge SNARKs (zk-SNARKs) are non-interactive proof systems with short and efficiently verifiable proofs. They elegantly resolve the juxtaposition of individual privacy and public trust, by providing an efficient way of demonstrating knowledge of secret information without actually revealing it. To this day, zk-SNARKs are being used for delegating computation, electronic cryptocurrencies, and anonymous credentials. However, all current SNARKs implementations rely on pre-quantum assumptions and, for this reason, are not expected to withstand cryptanalitic efforts over the next few decades. In this work, we introduce the first designated-verifier zk-SNARK based on lattice assumptions, which are believed to be post-quantum secure. We provide a generalization in the spirit of Gennaro et al. (Eurocrypt'13) to the SNARK of Danezis et al. (Asiacrypt'14) that is based on Square Span Programs (SSPs) and relies on weaker computational assumptions. We focus on designated-verifier proofs and propose a protocol in which a proof consists of just 5 LWE encodings. We provide a concrete choice of parameters as well as extensive benchmarks on a C implementation, showing that our construction is practically instantiable.
Geng Hong, Zhemin Yang, Sen Yang, Lei Zhang · 10 authors
As a new mechanism to monetize web content, cryptocurrency mining is becoming increasingly popular. The idea is simple: a webpage delivers extra workload (JavaScript) that consumes computational resources on the client machine to solve cryptographic puzzles, typically without notifying users or having explicit user consent. This new mechanism, often heavily abused and thus considered a threat termed "cryptojacking", is estimated to affect over 10 million web users every month; however, only a few anecdotal reports exist so far and little is known about its severeness, infrastructure, and technical characteristics behind the scene. This is likely due to the lack of effective approaches to detect cryptojacking at a large-scale (e.g., VirusTotal). In this paper, we take a first step towards an in-depth study over cryptojacking. By leveraging a set of inherent characteristics of cryptojacking scripts, we build CMTracker, a behavior-based detector with two runtime profilers for automatically tracking Cryptocurrency Mining scripts and their related domains. Surprisingly, our approach successfully discovered 2,770 unique cryptojacking samples from 853,936 popular web pages, including 868 among top 100K in Alexa list. Leveraging these samples, we gain a more comprehensive picture of the cryptojacking attacks, including their impact, distribution mechanisms, obfuscation, and attempts to evade detection. For instance, a diverse set of organizations benefit from cryptojacking based on the unique wallet ids. In addition, to stay under the radar, they frequently update their attack domains (fastflux) on the order of days. Many attackers also apply evasion techniques, including limiting the CPU usage, obfuscating the code, etc.
A wave of alternative coins that can be effectively mined without specialized hardware, and a surge in cryptocurrencies' market value has led to the development of cryptocurrency mining ( cryptomining ) services, such as Coinhive, which can be easily integrated into websites to monetize the computational power of their visitors. While legitimate website operators are exploring these services as an alternative to advertisements, they have also drawn the attention of cybercriminals: drive-by mining (also known as cryptojacking ) is a new web-based attack, in which an infected website secretly executes JavaScript code and/or a WebAssembly module in the user's browser to mine cryptocurrencies without her consent. In this paper, we perform a comprehensive analysis on Alexa's Top 1 Million websites to shed light on the prevalence and profitability of this attack. We study the websites affected by drive-by mining to understand the techniques being used to evade detection, and the latest web technologies being exploited to efficiently mine cryptocurrency. As a result of our study, which covers 28 Coinhive-like services that are widely being used by drive-by mining websites, we identified 20 active cryptomining campaigns. Motivated by our findings, we investigate possible countermeasures against this type of attack. We discuss how current blacklisting approaches and heuristics based on CPU usage are insufficient, and present MineSweeper, a novel detection technique that is based on the intrinsic characteristics of cryptomining code, and, thus, is resilient to obfuscation. Our approach could be integrated into browsers to warn users about silent cryptomining when visiting websites that do not ask for their consent.
We introduce BitML, a domain-specific language for specifying contracts that regulate transfers of bitcoins among participants, without relying on trusted intermediaries. We define a symbolic and a computational model for reasoning about BitML security. In the symbolic model, participants act according to the semantics of BitML, while in the computational model they exchange bitstrings, and read/append transactions on the Bitcoin blockchain. A compiler is provided to translate contracts into standard Bitcoin transactions. Participants can execute a contract by appending these transactions on the Bitcoin blockchain, according to their strategies. We prove the correctness of our compiler, showing that computational attacks on compiled contracts are also observable in the symbolic model.
Karan Ganju, Qi Wang, Wei Yang, Carl A. Gunter · 5 authors
With the growing adoption of machine learning, sharing of learned models is becoming popular. However, in addition to the prediction properties the model producer aims to share, there is also a risk that the model consumer can infer other properties of the training data the model producer did not intend to share. In this paper, we focus on the inference of global properties of the training data, such as the environment in which the data was produced, or the fraction of the data that comes from a certain class, as applied to white-box Fully Connected Neural Networks (FCNNs). Because of their complexity and inscrutability, FCNNs have a particularly high risk of leaking unexpected information about their training sets; at the same time, this complexity makes extracting this information challenging. We develop techniques that reduce this complexity by noting that FCNNs are invariant under permutation of nodes in each layer. We develop our techniques using representations that capture this invariance and simplify the information extraction task. We evaluate our techniques on several synthetic and standard benchmark datasets and show that they are very effective at inferring various data properties. We also perform two case studies to demonstrate the impact of our attack. In the first case study we show that a classifier that recognizes smiling faces also leaks information about the relative attractiveness of the individuals in its training set. In the second case study we show that a classifier that recognizes Bitcoin mining from performance counters also leaks information about whether the classifier was trained on logs from machines that were patched for the Meltdown and Spectre attacks.
Frode Kjærland, Aras Khazal, Erlend Aune Krogstad, Frans B. Gyllenhammar Nordstrøm · 5 authors
This paper aims to enhance the understanding of which factors affect the price development of Bitcoin in order for investors to make sound investment decisions. Previous literature has covered only a small extent of the highly volatile period during the last months of 2017 and the beginning of 2018. To examine the potential price drivers, we use the Autoregressive Distributed Lag and Generalized Autoregressive Conditional Heteroscedasticity approach. Our study identifies the technological factor Hashrate as irrelevant for modeling Bitcoin price dynamics. This irrelevance is due to the underlying code that makes the supply of Bitcoins deterministic, and it stands in contrast to previous literature that has included Hashrate as a crucial independent variable. Moreover, the empirical findings indicate that the price of Bitcoin is affected by returns on the S&P 500 and Google searches, showing consistency with results from previous literature. In contrast to previous literature, we find the CBOE volatility index (VIX), oil, gold, and Bitcoin transaction volume to be insignificant.
Blockchain has emerged as a decentralized and trustable ledger for recording and storing digital transactions. The mining process of Blockchain, however, incurs a heavy computational workload for miners to solve the proof-of-work puzzle (i.e., a series of the hashing computation), which is prohibitive from the perspective of the mobile terminals (MTs). The advanced multi-access mobile edge computing (MEC), which enables the MTs to offload part of the computational workloads (for solving the proof-of-work) to the nearby edge-servers (ESs), provides a promising approach to address this issue. By offloading the computational workloads via multi-access MEC, the MTs can effectively increase their successful probabilities when participating in the mining game and gain the consequent reward (i.e., winning the bitcoin). However, as a compensation to the ESs which provide the computational resources to the MTs, the MTs need to pay the ESs for the corresponding resource-acquisition costs. Thus, to investigate the trade-off between obtaining the computational resources from the ESs (for solving the proof-of-work) and paying for the consequent cost, we formulate an optimization problem in which the MTs determine their acquired computational resources from different ESs, with the objective of maximizing the MTs' social net-reward in the mining process while keeping the fairness among the MTs. In spite of the non-convexity of the formulated problem, we exploit its layered structure and propose efficient distributed algorithms for the MTs to individually determine their optimal computational resources acquired from different ESs. Numerical results are provided to validate the effectiveness of our proposed algorithms and the performance of our proposed multi-access MEC for Blockchain.