Hanna Danylchuk, Oksana Kovtun, Liubov Kibalnyk, Oleksii Sysoiev
The paper focuses on monitoring and modelling of the cryptocurrency market. The application of the chosen research methods is based on the analysis of existing methods and tools of economic and mathematical modelling of time series research on the example of the cryptocurrency market. It is proved that the use of individual methods is not relevant, as they do not give an adequate assessment of the specified market, so a comprehensive approach is the most acceptable. Therefore, monitoring and modelling of some cryptocurrency pairs with different capitalization degree were implemented by fractal and recurrent methods of the financial markets. The daily values of currency pairs for the period from September 2015 to November 2019 were chosen as information basis for monitoring and modelling. The use of R /S modelling method make it possible to conclude the persistence of time series of the selected cryptocurrencies indicating that the market trends are clearly defined, the currency pair of XRP/USD has the highest level of trend resistance. To compare the obtained results, the comprehensive approach is offered using recurrent diagrams that help to determine the cryptocurrency stability. The results of modelling by the recurrent method show that the most stable cryptocurrencies are the ones with the highest capitalization, namely Bitcoin and Ripple.
This paper investigates the risk and return properties of a trading strategy for the cryptocurrency market. The main predictive power for portfolio formation comes from a simple prospect theory model that only uses price information readily available. The dataset consists of a large body of cryptocurrencies from 2014 to 2020. I find a strong outperformance over the market, even after controlling for known predictors. Factor regressions with a cryptocurrency three-factor model further reveal significant alphas. Robustness test emphasize the legitimacy of the strategy. On average, cryptocurrencies with a high (low) prospect theory value earn low (high) subsequent returns. Interestingly, traders in the cryptocurrency market seem to assess the attractiveness of cryptocurrency in a way described by prospect theory. Mechanical tests of the model show that probability weighting is a main driver behind this assessment. Cryptocurrencies with a high prospect theory value tend to be highly positively skewed. This skewness could be the reason why the cryptocurrency seems attractive to traders, similar to lottery-like gambles.
Bank for International Settlements, Raphael Auer, Stijn Claessens, Bank for International Settlements
Cryptocurrencies are often thought to operate out of the reach of national regulation, but in fact their valuations, transaction volumes and user bases react substantially to news about regulatory actions. The impact depends on the specific regulatory category to which the news relates: events related to general bans on cryptocurrencies or to their treatment under securities law have the greatest adverse effect, followed by news on combating money laundering and the financing of terrorism, and on restricting the interoperability of cryptocurrencies with regulated markets. News pointing to the establishment of specific legal frameworks tailored to cryptocurrencies and initial coin offerings coincides with strong market gains. These results suggest that cryptocurrency markets rely on regulated financial institutions to operate and that these markets are segmented across jurisdictions.
We study the cross-sectional interdependence between returns on cryptocurrency pairs and deviations of Tether USD from its parity to the U.S. dollar. Methodologically, we propose a large-scale Bayesian Vector Autoregressive (BVAR) model which features a global-local shrinkage prior for cross-pairs return correlations. Empirically, we show that deviations from the USDT/USD parity significantly and positively correlate with future returns on cryptocurrency pairs, conditional on both aggregate and asset-specific trading activity. A simple long-only rotational investment strategy which exploits the exposure to the lagged USDT/USD deviations outperforms out-of-sample passive benchmark investments in Bitcoin and a value-weighted market index.
Radosław Michalski, Daria Dziubałtowska, Piotr Macek
The term blockchain has its roots in cryptocurrencies. However, its applications are now more widespread, and in many areas, this technology has become the foundation of the distributed ledger. The blockchain protocol assumes that all the participants of the system are both contributors and safeguards of this ledger, since the lack of a trusted third party requires other security precautions in order to maintain the consistency of transactions. In this work, we investigate whether for the participants of a blockchain-based system that does not require revealing the character explicitly, it can be discovered by other means. In order to verify this, we built and publicly released a dataset of nearly 9,000 addresses of nodes in the most popular cryptocurrency - Bitcoin, and then labelled them. These labels represent the character the nodes have in the network, e.g. miners or exchanges. We then developed a set of features that quantify the behaviour of nodes in the network and used supervised machine learning algorithms to find out whether the character of nodes can be revealed based on these features. Our results demonstrate, due to the F-score reaching over 95% in the best-performing algorithms, that it is hard to hide the role the node has in a blockchain-based network. These results indicate that to build trustworthy blockchain-based systems that fully comply with original blockchain assumptions, specific countermeasures are needed in order to preserve the desired level of anonymity.
Haya R. Hasan, Khaled Salah, Raja Jayaraman, Raja Wasim Ahmad · 6 authors
Spare parts are essential assets to maintain productivity and operations. A spare part is used for the repair or replacement of failed units. Tracing and tracking of spare parts ownership can help to ensure reliable outcomes in terms of manufacturing operations and service delivery. However, today's spare part inventory systems fall short of providing reliable tracing and tracking of spare parts ownership which poses serious threats to their authenticity. Also, current approaches and systems leveraged for spare part inventory management are vulnerable to the single point of failures. In this paper, we propose a blockchain-based smart contract to trace and track the spare parts ownership details from the original equipment manufacturer to the supplier and end-users. We exploit blockchain features to build a secure and trusted spare part inventory system that is tamper-proof, traceable, trackable, accessible immutable, resilient, and reliable. The proposed system integrates decentralized storage of interplanetary file systems (IPFS) to store and share spare parts data. We present algorithms along with their full implementation details. We perform testing and validation of the smart contract. Furthermore, we provide security and cost analysis, and show how the proposed system ensures reliable tracing and tracking of spare parts ownership. We make the smart contract source code publicly available on Github.
Roman Mühlberger, Stefan Bachhofner, Eduardo Castelló Ferrer, Claudio Di Ciccio · 7 authors
Blockchain has evolved into a platform for decentralized applications, with beneficial properties like high integrity, transparency, and resilience against censorship and tampering. However, blockchains are closed-world systems which do not have access to external state. To overcome this limitation, oracles have been introduced in various forms and for different purposes. However so far common oracle best practices have not been dissected, classified, and studied in their fundamental aspects. In this paper, we address this gap by studying foundational blockchain oracle patterns in two foundational dimensions characterising the oracles: (i) the data flow direction, i.e., inbound and outbound data flow, from the viewpoint of the blockchain; and (ii) the initiator of the data flow, i.e., whether it is push or pull-based communication. We provide a structured description of the four patterns in detail, and discuss an implementation of these patterns based on use cases. On this basis we conduct a quantitative analysis, which results in the insight that the four different patterns are characterized by distinct performance and costs profiles.
Due to the long train marshaling and complex line conditions, the operating modes in heavy haul rail systems frequently change when trains travel. Improper traction or braking operation made by drivers will increase the longitudinal impact force to trains and causes the train decoupling, severely affecting the safe operations of trains. It is quite desirable to replace the manual control with intelligent control in heavy haul rail systems. Traditional machine learning-based intelligent control methods suffer from insufficient data. Due to lacking effective incentives and trust, data from different rail lines or operators cannot be shared directly. In this paper, we propose an approach on blockchain-based federated learning to implement asynchronous collaborative machine learning between distributed agents that own data. This method performs distributed machine learning without a trusted central server. The blockchain smart contract is used to realize the management of the entire federated learning. Using the historical driving data collected from real heavy haul rail systems, the learning agent in the federated learning method adopts a support vector machine (SVM) based intelligent control model. To deal with the imbalanced traction and braking data, we optimize the classic SVM model via assigning different penalty factors to the majority and minority classes. The data set are mapped to a high dimension using kernel functions to make it linearly separable. We construct a mixing kernel function composed of polynomial and radial basis function (RBF) kernel functions, which uses a dynamic weight factor changing with train speeds to improve the model accuracy. The simulation results demonstrate the efficiency and accuracy of our proposed intelligent control method.
With the exponential increase in the complexity of network management and configuration, Software Defined Networking (SDN) has emerged as a promising network paradigm. SDN aims to efficiently transform network architecture and operations to be agile, and effectively enrich the functionality of underlying network elements, such as routers and switches, by decoupling the control plane from the data plane. In SDN, the network intelligence is centralized in a software entity so-called SDN controller, which enables network administrators to dynamically manage, secure, and optimize network resources and programmatically shape all entire network traffic pattern. Despite the impressive benefits SDN has brought to network architecture, it introduces new security challenges and prompts different implementation strategies to spread attack vectors. This paper comprehensively describes the utilization of Blockchain technology to secure and protect SDN architecture and discusses the feasibility of integrating the revolutionary technologies of SDN and Blockchain to provide confidentiality, integrity, and availability to network infrastructure.
Luh Putu Mahyuni, Richard Adrian, Gede Sri Darma, Ngakan Nyoman Kutha Krisnawijaya · 6 authors
This paper aims at reviewing and systematically mapping research on blockchain potentials in improving supply chain performance. Articles were retrieved from several prominent databases, selected, reviewed, grouped into several themes and synthesized. This paper suggests that applying blockchain in the supply chain could improve its performance in terms of transparency, traceability, sustainability, trust, and cost-efficiency. As a cutting-edge technology, blockchain has not been widely implemented in supply chain industries. Research on blockchain application in the supply chain is also relatively limited. This paper contributes to the literature by offering a comprehensive map of research on blockchain potentials in improving supply chain performance. The findings of this study will also be beneficial for managers who seek for a comprehensive understanding of how blockchain technology affects their companies particularly in supply chain management.
Matteo Signorini, Matteo Pontecorvi, Waël Kanoun, Roberto Di Pietro
Anomaly detection tools play a role of paramount importance in protecting networks and systems from unforeseen attacks, usually by automatically recognizing and filtering out anomalous activities. Over the years, different approaches have been designed, all focused on lowering the false positive rate. However, no proposal has addressed attacks specifically targeting blockchain-based systems. In this paper, we present BAD: Blockchain Anomaly Detection. This is the first solution, to the best of our knowledge, that is tailored to detect anomalies in blockchain-based systems. BAD is a complete framework, relying on several components leveraging, at its core, blockchain meta-data in order to collect potentially malicious activities. BAD enjoys some unique features: (i) it is distributed (thus avoiding any central point of failure); (ii) it is tamper-proof (making it impossible for a malicious software to remove or to alter its own traces); (iii) it is trusted (any behavioral data is collected and verified by the majority of the network); and, (iv) it is private (avoiding any third party to collect/analyze/store sensitive information). Our proposal is described in detail and validated via both experimental results and analysis, that highlight the quality and viability of our Blockchain Anomaly Detection solution.
Since their introduction, Online Social Networks (OSNs) have transformed the way people interact with each other. Lately, a new trend is rising in the development of OSNs, fueled by an increasing interest of the blockchain technology and the benefits it can bring to the world of OSNs. Blockchain Online Social Media (BOSMs) are Social Media applications that are supported by the blockchain technology. Thanks to a blockchain, BOSMs either try to enforce the privacy of the users or try to redistribute with their users the economic wealth generated by the platform through a rewarding system. There are countless BOSMs available which incorporate a rewarding system. Among them, Steemit can be considered the most well-known platform exceeding 1 million registered users. Steemit is supported by the blockchain Steem, which is a blockchain that natively supports the development of social applications by the usage of transactions that model social activity. Even if other important blockchains, such as Ethereum has been widely analysed, at the best of our knowledge, no study exists concerning the topology of the transactions graph of Steem. The main goal of this paper is to study the structure of the Steem transaction graph to understand its characteristics and unveil crucial knowledge concerning their users. More in detail, we build the Interactions Graph and, after its study, we evaluate three subgraphs that capture its social and monetary aspects. The degree distributions of the graphs follow a power-law. Additionally, we detect a substantial number of bots that offer paid services on the platform among the most active users. Lastly, the investigation of the four analysed graphs through a bow-tie structure, suggesting that half of the users have a passive social behaviour and that 80% of the users tend to accrue economic value
ABSTRACT Blockchain transactions are recorded in a shared and append-only repository that multiple parties verify, validate, and agree upon. While initially used to keep track of digital assets, blockchains now track the ownership and provenance of tangible assets. An inherent challenge in using blockchain for this task involves keeping the status of a tangible asset in the physical world in sync with its non-fungible token on a blockchain. While several blockchains are already being used in this manner, specific implementation details are fragmented. In response, this study examines four stages of tracking tangible assets using a consortium's permissioned blockchain, including: design and governance of a blockchain, asset creation, asset transfer, and asset retirement. Based on this analysis, this study proposes a framework of risk considerations and control objectives to evaluate the extent to which a unique blockchain serves as a reliable transaction repository for tracking the ownership and provenance of tangible assets.
Michael Henke, Christoph Besenfelder, Sandra Kaczmarek, Michael Fiolka
Digitalization requires a new form of management to master the transformation process of corporations and companies. The Dortmund Management Model structures the focus areas of the digital transformation along the management tasks goal, planning, decision, realization and monitoring as well as the common socio-technical subsystems technological, organizational and personnel - enriched by a fourth dimension: information. Additionally, the acceleration factors transformation, migration and change management are taken into account. This paper embraces a vision for a persistent management of production and supply chain networks in order to achieve a holistic Management 4.0. The emerging developments of technology, methods, tools and models in production and supply chain research are connected and merged into a big picture of digital supply chain management and logistics. The interfaces between management tasks show specific characteristics of digital business processes in particular, which are hereinafter exemplarily outlined: New business models and value-creation networks are based on adaption intelligent production systems, which are interconnected with digital models for continuous planning and reconfiguration. At the shop floor and between sites orders are completed by autonomous guided vehicles (AGV) with intelligent load carriers. Decentralized negotiations and decisions across company boundaries concluded with smart contracts are enabling reasonable and sustainable distribution of the value creation processes. Humans are still in the center of action – abilities are developed by integrated competence management, new learning approaches and human-centered assistance systems coupled with AI-based decision-making support. New types of organizations allow a synergetic collaboration of humans and machines. The benefit of integrating new production and transport technologies becomes assessable and accelerates the ongoing renewal of existing networks. This paper provides an overview of possible potential and connecting factors by linking different technological developments towards supply chain, logistics, production and management research and shows further research demands.
In a recent seminal work, Bitansky and Shmueli (STOC '20) gave the first construction of a constant round zero-knowledge argument for NP secure against quantum attacks. However, their construction has several drawbacks compared to the classical counterparts. Specifically, their construction only achieves computational soundness, requires strong assumptions of quantum hardness of learning with errors (QLWE assumption) and the existence of quantum fully homomorphic encryption (QFHE), and relies on non-black-box simulation. In this paper, we resolve these issues at the cost of weakening the notion of zero-knowledge to what is called $\epsilon$-zero-knowledge. Concretely, we construct the following protocols:
- We construct a constant round interactive proof for NP that satisfies statistical soundness and black-box $\epsilon$-zero-knowledge against quantum attacks assuming the existence of collapsing hash functions, which is a quantum counterpart of collision-resistant hash functions. Interestingly, this construction is just an adapted version of the classical protocol by Goldreich and Kahan (JoC '96) though the proof of $\epsilon$-zero-knowledge property against quantum adversaries requires novel ideas.
- We construct a constant round interactive argument for NP that satisfies computational soundness and black-box $\epsilon$-zero-knowledge against quantum attacks only assuming the existence of post-quantum one-way functions.
At the heart of our results is a new quantum rewinding technique that enables a simulator to extract a committed message of a malicious verifier while simulating verifier's internal state in an appropriate sense.
We put forth a new framework for building pairing-based non-interactive zero-knowledge (\(\mathsf {NIZK}\)) arguments for a wide class of algebraic languages, which are an extension of linear languages, containing disjunctions of linear languages and more. Our approach differs from the Groth-Sahai methodology, in that we rely on pairings to compile a \(\varSigma \)-protocol into a \(\mathsf {NIZK}\). Our framework enjoys a number of interesting features:
conceptual simplicity, parameters derive from the \(\varSigma \)-protocol;
proofs as short as resulting from the Fiat-Shamir heuristic applied to the underlying \(\varSigma \)-protocol;
fully adaptive soundness and perfect zero-knowledge in the common random string model with a single random group element as CRS;
yields simple and efficient two-round, public coin, publicly-verifiable perfect witness-indistinguishable (WI) arguments(ZAPs) in the plain model. To our knowledge, this is the first construction of two-rounds statistical witness-indistinguishable arguments from pairing assumptions.