The study is devoted to the research of design concepts for the development of decentralized payment systems with its own cryptocurrency based on the blockchain platform Ethereum. The study examines the problems associated with traditional models of decentralized payment systems, analyzes the shortcomings in the industry, and proposes approaches to their solution. During the research, the method of creating digital assets was improved by designing a comprehensive solution consisting of a smart contracts part and a web-client part. Also, the expediency of designing its own exchange platform along with the integration of external cryptocurrency exchanges for the possibility of buying developed cryptocurrency for other popular digital currencies was substantiated and visual schemes of the traditional payment system structure with the method of exchange platform integration were presented. The paper substantiates the use of dynamic block size as a method of optimizing system efficiency, which can increase or decrease the maximum block size at the algorithm level, depending on the number of pending transactions, thus providing the required level of bandwidth. Also, the need for one-to-many transfers is justified, which involves the development of new methods of smart contracts for the cryptocurrency transfer from one user to a large number of addresses within a single transaction in order to reduce the load on the network. The problem of high energy consumption required for the functioning of the consensus system in the traditional model was solved by developing a model based on protocols that do not use the computational power of participants as a parameter to maintain consensus. Thus, the results of this study present improved methods for designing decentralized payment systems. The obtained results and generated recommendations can be used in the design of decentralized payment systems.
Md Shohel Khan, Ajoy Kanti Das, Md. Shohrab Hossain, Husnu S. Narman
The impact of global transformation due to mosquito-borne diseases like dengue is noticeable and according to the World Health Organization, approximately 96 million people are infected by dengue per year. Moreover, the climate of tropical countries, e.g., Bangladesh is highly in favor of dengue. The initiatives taken by different organizations every year are not enough to face the challenges of dengue. To mitigate the effect of dengue, we propose a distributed crowdsourcing framework, the Dengue Tracker System in which the infected patients and the conscious citizen can submit the possible infectious locations. With the submitted data, two separate heatmaps can be generated so that the people and the concerned authority can get ready to face the challenges of dengue. Moreover, the system is deployed on the Ethereum-blockchain to enhance the security of the system. To prevent fake location data, different token generation methods are implemented.
Currently as the widespread use of virtual monetary units (like Bitcoin, Ethereum, Ripple, Litecoin) has begun, people with bad intentions have been attracted to this area and have produced and marketed ransomware in order to obtain virtual currency easily. This ransomware infiltrates the victim's system with smartly-designed methods and encrypts the files found in the system. After the encryption process, the attacker leaves a message demanding a ransom in virtual currency to open access to the encrypted files and warns that otherwise the files will not be accessible. This type of ransomware is becoming more popular over time, so currently it is the largest information technology security threat. In the literature, there are many studies about detection and analysis of this cyber-bullying. In this study, we focused on crypto-ransomware and investigated a forensic analysis of a current attack example in detail. In this example, the attack method and behavior of the crypto-ransomware were analyzed and it was identified that information belonging to the attacker was accessible. With this dimension, we think our study will significantly contribute to the struggle against this threat.
Over the past decade, vast amounts of machine-readable structured information have become available through the automation of research processes as well as the increasing popularity of knowledge graphs and semantic technologies. \nToday, we count more than 10,000 datasets made available online following Semantic Web standards. \nA major and yet unsolved challenge that research faces today is to perform scalable analysis of large-scale knowledge graphs in order to facilitate applications in various domains including life sciences, publishing, and the internet of things. \nThe main objective of this thesis is to lay foundations for efficient algorithms performing analytics, i.e. exploration, quality assessment, and querying over semantic knowledge graphs at a scale that has not been possible before. \nFirst, we propose a novel approach for statistical calculations of large RDF datasets, which scales out to clusters of machines. \nIn particular, we describe the first distributed in-memory approach for computing 32 different statistical criteria for RDF datasets using Apache Spark. \nMany applications such as data integration, search, and interlinking, may take full advantage of the data when having a priori statistical information about its internal structure and coverage. \nHowever, such applications may suffer from low quality and not being able to leverage the full advantage of the data when the size of data goes beyond the capacity of the resources available. \nThus, we introduce a distributed approach of quality assessment of large RDF datasets. \nIt is the first distributed, in-memory approach for computing different quality metrics for large RDF datasets using Apache Spark. We also provide a quality assessment pattern that can be used to generate new scalable metrics that can be applied to big data. \nBased on the knowledge of the internal statistics of a dataset and its quality, users typically want to query and retrieve large amounts of information. \nAs a result, it has become difficult to efficiently process these large RDF datasets. \nIndeed, these processes require, both efficient storage strategies and query-processing engines, to be able to scale in terms of data size. \nTherefore, we propose a scalable approach to evaluate SPARQL queries over distributed RDF datasets by translating SPARQL queries into Spark executable code. \nWe conducted several empirical evaluations to assess the scalability, effectiveness, and efficiency of our proposed approaches. \nMore importantly, various use cases i.e. Ethereum analysis, Mining Big Data Logs, and Scalable Integration of POIs, have been developed and leverages by our approach. \nThe empirical evaluations and concrete applications provide evidence that our methodology and techniques proposed during this thesis help to effectively analyze and process large-scale RDF datasets. \nAll the proposed approaches during this thesis are integrated into the larger SANSA framework.
This paper is discusses the problems of the short-term forecasting of financial time series using supervised machine learning (ML) approach. For this goal, we applied several the most powerful methods including Support Vector Machine (SVM), Multilayer Perceptron (MLP), Random Forests (RF) and Stochastic Gradient Boosting Machine (SGBM). As dataset were selected the daily close prices of two stock index: SP 500 and NASDAQ, two the most capitalized cryptocurrencies: Bitcoin (BTC), Ethereum (ETH), and exchange rate of EUR-USD. As features we used only the past price information. To check the efficiency of these models we made out-of-sample forecast for selected time series by using one step ahead technique. The accuracy rates of the forecasted prices by using ML models were calculated. The results verify the applicability of the ML approach for the forecasting of financial time series. The best out of sample accuracy of short-term prediction daily close prices for selected time series obtained by SGBM and MLP in terms of Mean Absolute Percentage Error (MAPE) was within 0.46-3.71 %. Our results are comparable with accuracy obtained by Deep learning approaches.
Current research has led to a rejection of the hypothesis of a normal distribution of financial assets returns. Under these conditions, portfolio variance cannot serve as a good risk measure. In this paper analyzed the daily returns of the most common cryptocurrencies: Bitcoin, Ethereum, XRP, USDT, Bitcoin Cash, Litecoin. It is shown that the asset returns are not normally distributed, but with good precision follow the Cauchy distribution and Laplace distribution. The analytical expressions for risk measure were obtained using the distribution function and the VaR technique. However, the risk assessment of the return obtained on the basis of the Cauchy distribution is twice as high as the risk assessment obtained on the basis of the Laplace distribution. Therefore, the question arises: what distribution law to use to measurement the cryptocurrency risk? The paper shows that the Laplace distribution is the most adequate basis for measuring of cryptocurrencies risk.
Communication security between IoT devices is a major concern in this area, and the blockchain has raised hopes that this concern will be addressed. In the blockchain concept, the majority or even all network nodes check the validity and accuracy of exchanged data before accepting and recording them, whether this data is related to financial transactions or measurements of a sensor or an authentication message. In evaluating the validity of an exchanged data, nodes must reach a consensus in order to perform a special action, in which case the opportunity to enter and record transactions and unreliable interactions with the system is significantly reduced. Recently, in order to share and access management of IoT devices information with distributed attitude a new authentication protocol based on blockchain is proposed and it is claimed that this protocol satisfies user privacy preserving and security. However, in this paper, we show that this protocol has security vulnerabilities against secret disclosure, replay, traceability, and <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" id="M1"> <a:mtext>Token</a:mtext> </a:math> reuse attacks with the success probability of 1 and constant complexity of also 1. We also proposed an improved blockchain-based authentication protocol (IBCbAP) that has security properties such as secure access management and anonymity. We implemented IBCbAP using JavaScript programming language and Ethereum local blockchain. We also proved IBCbAP’s security both informally and formally through the Scyther tool. Our comparisons showed that IBCbAP could provide suitable security along with reasonable cost.
Janez Bartol, Andrej Souvent, Nermin Suljanović, Matej Zajc
This paper investigates a secure data exchange between many small distributed consumers/prosumers and the aggregator in the process of energy balancing. It addresses the challenges of ensuring data exchange in a simple, scalable, and affordable way. The communication platform for data exchange is using Ethereum Blockchain technology. It provides a distributed ledger database across a distributed network, supports simple connectivity for new stakeholders, and enables many small entities to contribute with their flexible energy to the system balancing. The architecture of a simulation/emulation environment provides a direct connection of a relational database to the Ethereum network, thus enabling dynamic data management. In addition, it extends security of the environment with security mechanisms of relational databases. Proof-of-concept setup with the simulation of system balancing processes, confirms the suitability of the solution for secure data exchange in the market, operation, and measurement area. For the most intensive and space-consuming measurement data exchange, we have investigated data aggregation to ensure performance optimisation of required computation and space usage.
U ovom radu će biti predstavljene mogućnosti primene blokčejn tehnologije za podršku sistemima za glasanje i opisan razvoj jednog takvog sistema u vidu aplikacije na Ethereum blokčejn platformi. Aplikacija je implementirana na distribuiran i decentralizovan način kako bi se ispitale prednosti i mane softverskog rešenja ovog tipa.
Efe Bozkir, Shahram Eivazi, Mete Akgün, Enkelejda Kasneci
Eye tracking data collection in the virtual reality context is typically carried out in laboratory settings, which usually limits the number of participants or consumes at least several months of research time. In addition, under laboratory settings, subjects may not behave naturally due to being recorded in an uncomfortable environment. In this work, we propose a proof-of-concept eye tracking data collection protocol and its implementation to collect eye tracking data from remotely located subjects, particularly for virtual reality using Ethereum blockchain and smart contracts. With the proposed protocol, data collectors can collect high quality eye tracking data from a large number of human subjects with heterogeneous socio-demographic characteristics. The quality and the amount of data can be helpful for various tasks in data-driven human-computer interaction and artificial intelligence.
Flash Loan, as an emerging service in the decentralized finance ecosystem, allows traders to request a non-collateral loan as long as the debt is repaid within the transaction. While providing convenience, it brings considerable challenges that Flash Loan allows speculative traders to leverage vulnerability of deployed protocols with vast capital and few risks and responsibilities. Most recently, attackers have gained over $15M profits from Eminence Finance via exploiting Flash Loans to repeatedly swap tokens (i.e., EMN and DAI). To be aware of foxy actions, we should understand what is the behavior running with the Flash Loan by traders. In this work, we propose ThunderStorm, a 3-phase transaction-based analysis framework, to systematically study Flash Loan on the Ethereum. Specifically, ThunderStorm first identifies Flash Loan transactions by applying observed transaction patterns, and then understands the semantics of the transactions based on primitive behaviors, and finally recovers the intentions of transactions according to advanced behaviors. To perform the evaluation, we apply ThunderStorm to existing transactions and investigate 11 well-known platforms. As the result, 22,244 transactions are determined to launch Flash Loan(s), and those Flash Loan transactions are further classified into 7 categories. Lastly, the measurement of financial behaviors based on Flash Loans is present to help further understand and explore the speculative usage of Flash Loan. The evaluation results demonstrate the capability of the proposed system.
Claudia Cristina Bozza, Marcelo Cabús Klötzle, Antônio Carlos Figueiredo Pinto, Paulo Vítor Jordão da Gama Silva
Este trabalho buscou avaliar a existência do efeito de feedback trading para as criptomoedas Bitcoin, Ethereum, Litecoin e Dash usando o modelo VAR proposto por Hasbrouck (1991). Este efeito busca avaliar a utilização de dados passados para tomar decisões futuras, utilizando para tanto, dados de alta frequência, divididos em quatro períodos (dia, hora, minuto e segundo) para captar a existência do efeito de feedback trading nas criptomoedas, visando contribuir para a linha de finanças comportamentais, uma vez que há poucos estudos que avaliam o investimento em mercados digitais seguindo uma perspectiva comportamental. O resultado do modelo indica a existência de feedback trading negativo para todas as criptomoedas nas granularidades de tempo segundo e minuto. O estudo também aponta como resultado do modelo a existência de feedback trading negativo para a granularidade de tempo hora a hora para Litecoin e Dash.
Özet— Tarihin ilk yıllarından itibaren alım-satım gibi güvene dayalı ekonomik işlemler merkezi otoriteler tarafından kayıt altına alınmaktadır. Kağıdın icadı ile kayıt işlemleri daha düzenli ve detaylı bir şekilde yapılmaya başlanmıştır. Son yıllarda da bilgisayarların yaygınlaşması ile birlikte kayıt işlemleri dijital olarak yapılarak kayıtların yönetimi kolaylaştırılmıştır. Yaşanan her teknolojik gelişme veri kayıt işlemlerinde kolaylık ve hız sağlamıştır. Gelişen teknoloji ile birlikte işlemler hızlanmış ancak kayıtların yönetim merkezi otoritelerin kontrolünde kalmıştır. Dağıtık defter teknolojisi kayıtların merkezi otoritelerin kontrolünden çıktığı yeni bir yaklaşım sunmaktadır. Dağıtık defter teknolojisi merkezi bir veritabanı veya merkezi yönetimi olmayan verilerin katılımcılarda dağıtık olarak tutuldğu eşler arası ağdır. İlk dağıtık defter uygulaması 2008 yılındaki ekonomik krizin ardından ortaya çıkan Bitcoin ödeme sistemi ve kripto parasıdır. Bitcoin’in ortaya çıkmasında merkezi otoritelerin verdiği kötü ekonomik kararlar etkili olmuştur. Bitcoin ile amaç mevcut finansal sistemleri merkezi otoritelerin kontrolünden çıkarmaktır. Bitcoin’in popüler hale gelmesiyle birlikte arkasındaki blok zinciri teknolojisi dikkat çekmeye başlamıştır. Başlangıçta Bitcoin nedeniyle blok zinciri teknolojisi finans sektörü ile özdeşleştirilse de tedarik zinciri, sağlık, tarım, enerji gibi farklı sektörde kullanımına yönelik çalışmalar yapılmaya başlanmıştır. Blok zinciri teknolojisinin farklı sektörlerde kullanılabilmesi için Hyperledger, Ethereum ve Corda gibi blok zinciri altyapıları geliştirilmiştir. Ancak yapılan geliştirme çalışmalarında blok zinciri teknolojisinin hız ve ölçeklenebilirlik açısından eksiklikleri olduğu görülmüştür. Bu nedenle hız ve ölçeklenebilirlik açısından gelişmiş yeni dağıtık defter teknolojileri geliştirilmiştir. Yeni geliştirilen popüler dağıtık defter teknolojisi türleri Directed Acyclic Graph, Hashgraph, Tempo ve Holochain’dir. Yeni nesil dağıtık defter teknolojilerinin öne çıkan farklı özellikleri bulunmaktadır. Yapılan çalışmada blok zinciri ile yeni nesil dağıtık defter teknolojileri karşılaştırılmıştır.
In current healthcare systems, electronic medical records (EMRs) are always located in different hospitals and controlled by a centralized cloud provider. However, it leads to single point of failure as patients being the real owner lose track of their private and sensitive EMRs. Hence, this article aims to build an access control framework based on smart contract, which is built on the top of distributed ledger (blockchain), to secure the sharing of EMRs among different entities involved in the smart healthcare system. For this, we propose four forms of smart contracts for user verification, access authorization, misbehavior detection, and access revocation, respectively. In this framework, considering the block size of ledger and huge amount of patient data, the EMRs are stored in cloud after being encrypted through the cryptographic functions of elliptic curve cryptography (ECC) and Edwards-curve digital signature algorithm (EdDSA), while their corresponding hashes are packed into blockchain. The performance evaluation based on a private Ethereum system is used to verify the efficiency of proposed access control framework in the real-time smart healthcare system.
Blockchain is a technology for decentralized transactions that has been widely used with cryptocurrencies such as Bitcoin. Many studies have been conducted in the last decades, approaching cryptocurrencies, and blockchain technology, more strongly in the context of financial transactions. However, little has been done to provide a panoramic view of the current literature; as a consequence, a careful understanding of the state-of-the-art papers remains limited and inconclusive. This study, therefore, aims to classify and provide a thematic analysis of studies on the use of blockchain in the context of financial area, thus allowing to create a clear systematic map of the current literature, and identify challenges and research opportunities. To achieve these objectives a systematic mapping study (SMS) approach was performed to answer 6 research questions. In total, 1884 studies were reviewed from 6 data sources, being 23 studies selected after a careful filtering process. The main findings were: (1) Over 65% of the selected studies concentrated on adopting two blockchain platforms named Hyperledger Fabric and Ethereum; (2) Over 60% used blockchain technology to increase security; (3) Most studies did not reveal the consensus algorithm used; (4) Fintech (or Bank) and loan were the most explored application areas; (5) Over 60% focused on producing prototype rather than frameworks; and (6) most studies were published in conferences. This study can benefit research community by generating a map of the literature, serving as a starting point for future researches. Moreover, this study reports some challenges worth investigating.
Blockchain has been a new technology that involves an emergent evolution of many areas, particularly in finance, healthcare and supply chain. Due to its remarkable features including immutability, decentralization, peer-to-peer networking and capability to corporate with data protection and privacy, it is attractive to system integration. In traditional system integration architecture, a central mediator with a silo to store transactions stands in the center of integration to provide trust and transparency for participated systems. Hence existing central mediator could become a possible target of Distributed Denial of Service (DDoS) cyberattack and single point of failure. This paper presents a blockchain-base system architecture and its implementation for system integration which doesn't need a central mediator. The blockchain-based system has been implemented using Ethereum Blockchain in compliance with the General Data Protection Regulation (GDPR) standard to support the integration of DNA profiles information systems.
The reality is that we live in a society where a small group of people thinks they know better than we do and how we should live our lives. They don’t ever seem to realize that the power and wealth they surround themselves with is only possible because of the quiet acquiescence of the majority. They say employment is a record high, but fail to say wages have been going down in real terms for decades. This wall street elites and big head of governments and pharmaceutical businesses constantly keep telling the general population how great everything is but deep down we all know it’s not true as Recent estimates for global poverty are that 8.6 percent of the world, or 736 million people, live in extreme poverty on 1.90 dollars or less a day, according to the World Bank but we know in our bones it’s not true. But it could be and through technology, it will be. As the only answer to political and financial problems that assail us is to step outside the circus. That’s where cryptocurrencies come in, as they offer a secure form of transferring or recording ownership of our assets and the most important part is they function completely independently of governments. Whether you are with or against them cryptocurrencies represent one of the biggest bull markets in the history of finance and it’s the only boom that comes close to the California gold rush. So as a future Muslim Moroccan scientific researcher in the field of Cybersecurity and block-chain technology I couldn’t help but wonder how could we use this technology in my country to revolutionize the banking and Financial sector in it and especially after I learned that Morocco prohibited the use of bitcoin back in 2017. I then found myself asking the following questions: how does cryptocurrency conform to sharia’s Islamic teaching especially in Morocco? And how could the use of cryptocurrencies send shock waves across the Middle East?
The outbreak of the respiratory disease caused by the new coronavirus (COVID-19) has caused the world to face an existential health crisis. To contain the infectious disease, many countries have quarantined their citizens for several weeks to months and even suspended most economic activities. To track the movements of residents, the governments of many states have adopted various novel technologies. Connecting billions of sensors and devices over the Internet, the so-called Internet of Things (IoT), has been used for outbreak control. However, these technologies also pose serious privacy risks and security concerns with regards to data transmission and storage. In this paper, we propose a blockchain-based system to provide the secure management of home quarantine. The privacy and security attributes for various events are based on advanced cryptographic primitives. To demonstrate the application of the system, we provide a case study in an IoT system with a desktop computer, laptop, Raspberry Pi single-board computer, and the Ethereum smart contract platform. The obtained results prove its ability to satisfy security, efficiency, and low-cost requirements.
Social networks such as Twitter contain billions of data of users, and in every second, a large number of tweets trade through Twitter. Sentiment analysis is the way toward deciding the emotional tone behind a series of words that users utilize to understand the attitudes, thoughts, and emotions that are enunciated in online references on Twitter. This chapter aims to determine the user preference of Bitcoin and Ethereum, which are the two most popular cryptocurrencies in the world by using the Twitter sentiment analysis. It proposes a powerful and fundamental approach to identify emotions on Twitter by considering the tweets of these two distinctive cryptocurrencies. One hundred twenty thousand (120,000) tweets were extracted separately from Twitter for each keyword Bitcoin/BTC and Bitcoin/ETC between the period from 12/09/2018 to 22/09/2018 (10 days).