Ujan Mukhopadhyay, Anthony Skjellum, Oluwakemi Hambolu, Jon Oakley · 6 authors
Cryptocurrencies have emerged as important financial software systems. They rely on a secure distributed ledger data structure; mining is an integral part of such systems. Mining adds records of past transactions to the distributed ledger known as Blockchain, allowing users to reach secure, robust consensus for each transaction. Mining also introduces wealth in the form of new units of currency. Cryptocurrencies lack a central authority to mediate transactions because they were designed as peer-to-peer systems. They rely on miners to validate transactions. Cryptocurrencies require strong, secure mining algorithms. In this paper we survey and compare and contrast current mining techniques as used by major Cryptocurrencies. We evaluate the strengths, weaknesses, and possible threats to each mining strategy. Overall, a perspective on how Cryptocurrencies mine, where they have comparable performance and assurance, and where they have unique threats and strengths are outlined.
Bitcoin system, when more than 51% computing power is controlled by a single node, the block chain can be distorted maliciously. This is called 51% attack which is a well-known potential risk that could destroy the Bitcoin system. The paper proves that under the current proof-of-work mechanism, computing power eventually will be centralized at a single node if miners are rational enough. The paper propose a new proof-of-work mechanism that improves decentralization and reduces the risk of 51% attack without increasing the risk of Sybil attack. This new mechanism introduces a series of principles such as Career open to all talents, without distinction of birth, Distribution according to labor and All Men are created equal.
Open access
Blockchain Technology Applications and Security
Spam and Phishing Detection
Advanced Steganography and Watermarking Techniques
Bitcoin is a digital currency in which the need for a trusted third party is avoided. Instead, this digital currency is based on the concept of ‘proof of work’ allowing users to execute payments by digitally signing their transactions. Since electronic files can be duplicated, fraudulent transactions in the form of double-spend attacks – where users spend the same money at least twice – can happen. This paper is about attack models that can assign possible time advantage to attacker agents in the Bitcoin network. In particular, this paper presents: (i) two attack models in which partial advancement towards block production can be influenced by time and not only by the hashpower used to produce blocks of hashes, and (ii) algorithmic experimentation comparing these models against existing well-known hashrate-based attack models that do not consider time advantage. As a conclusion, this paper presents evidence on the fact that advantages are not negligible for cases in which an attacker has had enough time for secretly mining fraudulent blocks or significant control over the network. Also, the models presented in this paper help in supporting previous claims in the literature about how to correctly model and detect double-spend attacks in the Bitcoin network.
David C. Mills, Kathy Wang, Brendan Malone, Anjana Ravi · 14 authors
Digital innovations in finance, loosely known as fintech, have garnered a great deal of attention across the financial industry. Distributed ledger technology (DLT) is one such innovation that has been cited as a means of transforming payment, clearing, and settlement (PCS) processes, including how funds are transferred and how securities, commodities, and derivatives are cleared and settled. DLT is a term that has been used by the industry in a variety of ways and so does not have a single definition. Because there is a wide spectrum of possible deployments of DLT, this paper will refer to the technology as some combination of components including peer-to-peer networking, distributed data storage, and cryptography that, among other things, can potentially change the way in which the storage, recordkeeping, and transfer of a digital asset is done.
The reputation system has been designed as an effective mechanism to reduce risks associated with online shopping for customers. However, it is vulnerable to rating fraud. Some raters may inject unfairly high or low ratings to the system so as to promote their own products or demote their competitors. This study explores the rating fraud by differentiating the subjective fraud from objective fraud. Then it discusses the effectiveness of blockchain technology in objective fraud and its limitation in subjective fraud, especially the rating fraud. Lastly, it systematically analyzes the robustness of blockchain-based reputation systems in each type of rating fraud. The detection of fraudulent raters is not easy since they can behave strategically to camouflage themselves. We explore the potential strengths and limitations of blockchain-based reputation systems under two attack goals: ballot-stuffing and bad-mouthing, and various attack models including constant attack, camouflage attack, whitewashing attack and sybil attack. Blockchain-based reputation systems are more robust against bad-mouthing than ballot-stuffing fraud. Blockchain technology provides new opportunities for redesigning the reputation system. Blockchain systems are very effective in preventing objective information fraud, such as loan application fraud, where fraudulent information is fact-based. However, their effectiveness is limited in subjective information fraud, such as rating fraud, where the ground-truth is not easily validated. Blockchain systems are effective in preventing bad mouthing and whitewashing attack, but they are limited in detecting ballot-stuffing under sybil attack, constant attacks and camouflage attack.
In recent years, blockchain technology has attracted considerable attention. It records cryptographic transactions in a public ledger that is difficult to alter and compromise because of the distributed consensus. As a result, blockchain is believed to resist fraud and hacking. This work explores the types of fraud and malicious activities that can be prevented by blockchain technology and identifies attacks to which blockchain remains vulnerable. This study recommends appropriate defensive measures and calls for further research into the techniques for fighting malicious activities related to blockchains.
The notion of smart city has grown popular over the past few years. It embraces several dimensions depending on the meaning of the word “smart” and benefits from innovative applications of new kinds of information and communications technology to support communal sharing. By relying on prior literature, this paper proposes a conceptual framework with three dimensions: (1) human, (2) technology, and (3) organization, and explores a set of fundamental factors that make a city smart from a sharing economy perspective. Using this triangle framework, we discuss what emerging blockchain technology may contribute to these factors and how its elements can help smart cities develop sharing services. This study discusses how blockchain-based sharing services can contribute to smart cities based on a conceptual framework. We hope it can stimulate interest in theory and practice to foster discussions in this area.
Blockchain technology is a core, underlying technology with promising application prospects in the banking industry. On one hand, the banking industry in China is facing the impact of interest rate liberalization and profit decline caused by the narrowing interest-rate spread. On the other hand, it is also affected by economic transformation, Internet development, and financial innovations. Hence, the banking industry requires urgent transformation and is seeking new growth avenues. As such, blockchains could revolutionize the underlying technology of the payment clearing and credit information systems in banks, thus upgrading and transforming them. Blockchain applications also promote the formation of “multi-center, weakly intermediated” scenarios, which will enhance the efficiency of the banking industry. However, despite the permissionless and self-governing nature of blockchains, the regulation and actual implementation of a decentralized system are problems that remain to be resolved. Therefore, we propose the urgent establishment of a “regulatory sandbox” and the development of industry standards.
Provable data possession (PDP) is a technique for ensuring the integrity of data in storage outsourcing. In this paper, we address the construction of an efficient PDP scheme for distributed cloud storage to support the scalability of service and data migration, in which we consider the existence of multiple cloud service providers to cooperatively store and maintain the clients' data. We present a cooperative PDP (CPDP) scheme based on homomorphic verifiable response and hash index hierarchy. We prove the security of our scheme based on multi-prover zero-knowledge proof system, which can satisfy completeness, knowledge soundness, and zero-knowledge properties. In addition, we also propose a fuzzy clustering system for analyzing the high dimensional Data bases in cloud Environments. This paper proposes novel effective fuzzy soft clustering systems with the combination of possibilistic c-means.
In a global economic landscape of hyper-commodification and financialisation, efforts to assimilate digital art into the high-stakes commercial art market have so far been rather unsuccessful, presumably because digital artworks cannot easily assume the status of precious object worthy of collection. This essay explores the use of blockchain technologies in attempts to create proprietary digital art markets in which uncommodifiable digital artworks are financialised as artificially scarce commodities. Using the decentralisation techniques and distributed database protocols underlying current cryptocurrency technologies, such efforts, exemplified here by the platform Monegraph, tend to be presented as concerns with the interest of digital artists and with shifting ontologies of the contemporary work of art. I challenge this characterisation, and argue, in a discussion that combines aesthetic theory, legal and philosophical theories of intellectual property, rhetorical analysis and research in the political economy of new media, that the formation of proprietary digital art markets by emerging commercial platforms such as Monegraph constitutes a worrisome amplification of long-established, on-going efforts to fence in creative expression as private property. As I argue, the combination of blockchain-based protocols with established ambitions of intellectual property policy yields hybrid conceptual-computational financial technologies (such as self-enforcing smart contracts attached to digital artefacts) that are unlikely to empower artists but which serve to financialise digital creative practices as a whole, curtailing the critical potential of the digital as an inherently dynamic and potentially uncommodifiable mode of production and artistic expression.
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.
Early 1990s governments across the South have embarked on democratic decentralization reforms aimed at introducing and strengthening local governance because of its assumed potential to improve the delivery of public services and alleviate poverty. To comply with that international practice, in early 2000 Tanzania government decide embarked on an ambitious Local Government Reform Program that addressed Political decentralization. Political decentralization signaled the government’s commitment to enhance the decision making authority of local government councils on matters affecting local development including determining priorities for local development, land use, finance, service delivery and human resource management. This paper sought to find out whether the selected local government council led by councilors enjoys the development planning, and service delivery authority as established in the local government law. The findings have confirmed that the case study council enjoyed modest decision making authority in the areas of local development planning, selecting local development strategy, and enjoyed even greater authority over service delivery powers.
The blockchain is a distributed network that records digital transactions on a publicly-accessible ledger. This paper explores whether blockchain technology is a suitable platform for the preservation of digital signatures and key pairs (public and private keys). Conventional infrastructures use digital certificates, issued by certification authorities, to declare the authentication of key pairs and digital signatures. However, this paper suggests that the blockchain’s hash functions can replace those certificates on the grounds of better privacy, that the nature of the network removes the problem of a single point of failure and that hashing is a form of authentication that does not require trust in a third-party authority. This article was an appendix to the research paper, Blockchain Technology for Recordkeeping which is available in the Reports section at http://www.blockchainubc.ca/main/dissemination.
The well-being of the society depends on a number of metals, including base metals, precious metals and increasingly rare earth elements (REE). The usage of these metals increased in numerous applications, including electrical and electronic equipment (EEE), and their interrupted supply is at stake. There is an increasing interest in the secondary sources of these metals, particularly waste electrical and electronic equipment (WEEE) in order to compensate their potential supply deficit. This PhD thesis demonstrates the advantages and bottlenecks of biological and chemical approaches, as well as the advances and perspectives in the development of sustainable processes for metal recovery from WEEE. Furthermore, a novel process for the recovery of metals from WEEE is described, and a techno-economic assessment is given.\nDiscarded printed circuit boards (PCB) from personal computers (PC), laptops, mobile phones and telecom servers were studied. Following an extensive literature review, a novel characterization and total metal assay method was introduced and applied to waste board materials. Discarded PCB contained metals in the range of (%, by weight): copper (Cu) 17.6 - 39.0, iron (Fe) 0.7 - 7.5, aluminum (Al) 1.0 - 5.5, nickel (Ni) 0.2 - 1.1, zinc (Zn) 0.3 - 1.2, as well as gold (Au) (in ppm) 21 - 320. In addition, multi-criteria analysis (MCA) using the analytical hierarchical process (AHP) methodology was applied for selection of the best-suited technology. A proof-of-concept for a two-step bioleaching extraction is given, in which 98.4% and 44.0% of the Cu and Au, respectively, were extracted. The two-step extraction procedure was applied to the chemical leaching of metals from PCB. Cu leaching was carried in an acidic oxidative mixture of H2SO4 and H2O2, whereas Au was leached by S2O32− in a NH4+medium, catalyzed by CuSO4. Under the optimized conditions, 99.2% and 92.2% of Cu and Au, respectively, were extracted from the board material. Selective recovery of Cu from the bioleaching leachate using sulfidic precipitation and electrowinning is studied. Cu was selectively recovered on the cathode electrode at a 50 mA current density in 50 minutes, with a 97.8% efficiency and 65.0% purity. The techno-economic analysis and environmental sustainability assessment of the new technology at an early stage of development was investigated.
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.
This study aims to determine the factors that determine the Local Government Finance Accountability Reporting. Variabel Government Accountability Financial Reporting independently used in this study is the Fiscal Decentralization measured using Local Self-Reliance, Reliance Regions, the performance is measured using the ratio Effectiveness, Operational Expenditure Ratio, Ratio Shopping Capital and Regional Status. The dependent variable used in this study is the Local Government Finance Accountability Reporting measured using BPK Audit Report on the Financial Statements of Local Government in the form of an audit opinion. The samples were all over the city / regency in Central Java province with the study period between 2011-2013. Data were collected using purposive sampling. Based on these criteria, the total of 35 city / county elected as the population in this study. The analytical tool used is path analysis with SPSS version 16.0 Results of the study found that there is a significant positive effect between the Regional Autonomy and Accountability Financial Reporting Regions. Dependence area does not have a significant impact on Regions Financial Reporting Accountability. The effectiveness of significant negative effect on the Local Government Finance Accountability Reporting. Operating expenditure does not have a significant impact on Regions Financial Reporting Accountability. Capital spending has a significant positive effect on Regions Financial Reporting Accountability. Regional Status significant negative effect on Regions Financial Reporting Accountability. From the findings, we can conclude that the Regional Addiction and capital expenditures are a significant positive effect on Regions Financial Reporting Accountability. Regional and Operational Expenditure dependence there is no significant effect on the Local Government Financial Reporting Accountability however Effectiveness and Regional Status has a significant negative effect on Regions Financial Reporting Accountability.
“Code is law” refers to the idea that, with the advent of digital technology, code has progressively established itself as the predominant way to regulate the behavior of Internet users. Yet, while computer code can enforce rules more efficiently than legal code, it also comes with a series of limitations, mostly because it is difficult to transpose the ambiguity and flexibility of legal rules into a formalized language which can be interpreted by a machine. With the advent of blockchain technology and associated smart contracts, code is assuming an even stronger role in regulating people’s interactions over the Internet, as many contractual transactions get transposed into smart contract code. In this paper, we describe the shift from the traditional notion of “code is law” (i.e., code having the effect of law) to the new conception of “law is code” (i.e., law being defined as code).
According to the World Economic Forum, by 2025 10% of global GDP will be stored on blockchains, a type of decentralised database and distributed shared ledger. Smart contracts are automated computable contracts that are executed in blockchains, with the benefit of removing intermediaries and reducing costs. The use cases in finance include: in cross-border payments, to capture obligations, minimize operational errors and expedite transfers; for property and casualty claims in insurance, to automate claims processing through third-party data sources and codification of business rules; for deposits and lending in syndicated loans, to facilitate real-time loan funding and automated servicing activities without intermediaries; for deposits and lending in trade finance, to automate the creation and management of credit facilities ultimately eliminating correspondent banks; for contingent convertible bonds in capital raising, to alert regulators when loan absorption needs to be activated, minimizing need for point-in-time stress tests; for compliance in investment management, to execute reporting and facilitate the automated creation of periodic filings; for proxy voting in investment management, to automate end-to-end confirmation by the validation of votes, increasing transparency; for asset rehypothecation in market provisioning, to enable the real-time reporting of asset history and the enforcement of regulatory constraints, including facilitating clearing and settlement to eliminate need for intermediaries and reduce settlement time; for equity post-trade in market provisioning, to simultaneously transfer equity and cash in real time, reducing the likelihood of errors impacting settlement.The policy implications introduced by decentralization require that economists and lawyers understand this technological shift, and more importantly, the risks related to tangible (e.g consensus selection as a security choice) and intangible (e.g contract incompleteness/code errors) factors. We demonstrate a decision making method where utility is measured by “levels of trust” using artifacts from fields finance applied to a portfolio of institutional smart contract companies. Expected utility is measured by mapping a demand vector field (the attention level), and funding by plotting a scalar field (the investment level); the associated risk exposure is implicit in the consensus mechanism tradeoffs, according to the progression of firms represented in the system of coordinates. The goal is to provide a device for portfolio analysis and construction. The data comes from a panel of 200 million internet users, and investment databases. The result is a comprehensive and scalable view of decentralised portfolios, inspired in the methods of behavioural finance.
The rapid development of the blockchain technology and its various applications has rendered it important to understand the guidelines for adopting it. The comparative analysis method is used to analyze different dimensions of the maturity model, which is mainly based on the commonly used capability maturity model. The blockchain maturity model and its adoption process have been discussed and presented. This study serves as a guide to institutions to make blockchain adoption decisions more systematically.
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.