The modern financial technology (âFinTechâ) revolution has two features that distinguish it from previous eras of innovation: (1) Consumers have greater access to financial information and applications using smartphones on high-speed networks; and (2) businesses benefit from dramatically lower costs, improved performance, and enhanced options in data storage, computation, and application development. The once monolithic and proprietary financial services industry is being challenged under the zeitgeist of decentralization, disintermediation, and open protocols. Consequently, households in the United States are witnessing the emergence of new options for investment, credit, insurance, and payments. We illustrate how several influential FinTech trends may help address biases and constraints that hamper households in smoothing intertemporal consumption.
Open banking enables individual customers to own their banking data, which provides fundamental support for the boosting of a new ecosystem of data marketplaces and financial services. In the near future, it is foreseeable to have decentralized data ownership in the finance sector using federated learning. This is a just-in-time technology that can learn intelligent models in a decentralized training manner. The most attractive aspect of federated learning is its ability to decompose model training into a centralized server and distributed nodes without collecting private data. This kind of decomposed learning framework has great potential to protect users' privacy and sensitive data. Therefore, federated learning combines naturally with an open banking data marketplaces. This chapter will discuss the possible challenges for applying federated learning in the context of open banking, and the corresponding solutions have been explored as well.
Mir Hassan, Jincai Chen, Adnan Iftekhar, Xiaohui Cui
The Internet of Things (IoT) has the potential to change the way the world works from home automation to smart cities, from improved healthcare to an efficient management sys-tem in supply chains to industry 4.0 revolution. IoT is increasingly becoming an essential part of the homes and industrial automa-tion; nevertheless, there are still many challenges that need to fix. IoT solutions are costly and complicated, while issues regarding security and privacy must be addressed with a sustainable plan. Support the growing number of connected devices; the IoT is in dire need of a reboot. Blockchain technology might be the answer. Starting as a decentralized financial solution in the form of Bitcoin, Blockchain technology has expanded to diverse areas and Information Technology applications. Blockchain technology and Smart Contracts can address the outstanding security and privacy issues that impede further development of the IoT. Blockchain is a decentralized system with no central governance, facilitates interactions, promotes new and improved transaction models, and allows autonomous coordination of the devices using enhanced encryption techniques. The primary reason for this paper is to showcase the challenges and problems we are facing with the current internet of things solutions and analyze how the use of Blockchain and Smart Contracts can help achieve a new, more robust internet of things system. Finally, we examine some of the many projects using the Internet of Things together with Blockchain and Smart Contracts, to create new solutions that are only possible by integrating these technologies.
Alexandre Pinheiro, Edna Dias Canedo, Rafael T. de Sousa, Robson de Oliveira Albuquerque
The adoption of cloud computing solutions is an established reality in government agencies and in small, medium, and large companies due to procurement easiness and the variety of available services, as well as its low cost compared to the acquisition and management of own infrastructures. Among the most used services is cloud file storage, and the security of this storage has been an essential subject of recent research, particularly customer data integrity. Thus, this article proposes a solution for the monitoring of the integrity of files stored in the cloud, based on the use of smart contracts in Blockchain Networks, symmetric encryption, and computational trust. The proposed solution consists of a protocol that provides confidentiality, decentralization, audit availability, and the secure sharing of file integrity monitoring results, without overloading the services involved, as well as an unabridged reference implementation which was used to validate the proposal. The results obtained during the validation tests have shown that the solution is feasible and faultless in detecting corrupted files. These tests also confirmed that the sharing of integrity monitoring results, coupled with the application of computational trust techniques, significantly increased the efficiency of the proposed solution.
Tonglai Liu, Jigang Wu, Long Chen, Yalan Wu · 5 authors
In a mobile blockchain network, many mobile devices have insufficient computational capacity to execute computation-intensive tasks locally. To tackle this problem, blockchain tasks can be offloaded to edge servers with the aid of auction. However, most auction mechanisms on mobile blockchain ignore the automatic parallel execution and long-term performance. This paper aims to solve the problem of computation offloading in a mobile blockchain network. We transform this problem into a multi-choice multi-dimensional knapsack problem which is NP-hard. To improve the total utility of auction participants, this paper proposes a smart-contract-based double auction mechanism, named long-term auction for mobile blockchain (LAMB). The subtasks can be offloaded from one mobile device to heterogeneous edge servers. Also, LAMB satisfies the economic properties of an auction mechanism. Experimental results demonstrate that, the utility and utilization ratio can be achieved by 130.55% higher and 138.64% higher, respectively, in comparison to the existing auction algorithm WBD. Furthermore, the proposed LAMB can guarantee long-term performance for task offloading, and it can achieve automatic execution in an autonomous and secure environment.
Education plays a vital role in human life. To encourage higher education, the Indian government has taken many initiatives. These include the disbursement of scholarships to financially help the students belonging to low-income families. Central Sector Scheme of Scholarship for College and University Students (CSS) is one such scheme that financially helps meritorious students in pursuing graduation and post-graduation. This scheme was introduced in 2008. Scholarships from CSS have been helping students to fund their educational expenses. But there have been some difficulties faced by the students in availing this scholarship. These include lack of traceability of application form, loss of application form in transit through Indian Postal Service, lack of transparency between students and their respective Education Boards, and lack of bank account verification. In this paper, we propose a Smart Contract for CSS Scheme that overcomes the above-mentioned drawbacks. It has four entities: Students, Education Boards, Colleges, and Banks. The Blockchain-based Smart Contract proposed in this paper, has been developed to provide a user-friendly environment to a student and maintain a transparent and constant connection between students and their respective Education Boards. Many applications have been developed on the Blockchain platform but to the best of our knowledge, there does not exists any government scholarship scheme on the Blockchain platform in India.
We address the absence of reliable tests on contract analyzers of smart contracts and present a systematic method to diversify test cases by combining smart-contract-specific bugs and static analysis barriers in this paper. Using contract analyzers is the most practical solution for building a secure blockchain service, but they are relatively immature and lacking stable performance metrics. Traditionally, performance reports only compare static contract analyzers with pre-defined test cases, such as the Juliet test suite. However, building such test suites is burdensome for smart contracts, which are frequently change. In this paper, we propose an automated method to assess contract analyzers of smart contracts by diversifying test cases. In the experimental results, we identified nine erroneous alarms in the state-of-the-art contract analyzers with automatically generated test cases on five vulnerabilities.
With the growing popularity of smart contracts, the problem of validation of their correctness becomes more and more relevant, but at the moment there are no universally recognized tools for their testing. In this paper, we propose to apply the mutational analysis to improve reliability of Solidity smart contracts. We identified wide spread errors in the source code of existing contracts and developed a RegularMutator tool for mutation analysis. It has shown its effectiveness in testing a large smart contracts project. As a result of our analysis, we managed to improve the test suites of the project to find the discovered defects and, increase the quality of the test suite.
Access control is one of the most important security concerns, which is critical in resource and information protection over IoT devices. This paper proposes a new scheme that combines attribute-based access control (ABAC) model with blockchain technology and uses smart contracts for access control judgment. This scheme can realize dynamic, distributed and reliable access control in the open IoT environment. The IoT access control system based on this scheme consists of five functional modules. The information registration point registers information for each device that joins the system. Policy enforcement point (PEP) is responsible for managing agent-devices in the system and processing original access requests from access subjects. Policy decision point (PDP) makes access control right decision through smart contracts. Policy administration point (PAP) is used to manage smart contract information. Policy information point (PIP) is used to manage key attribute information of devices used for access control judgment. The scheme also includes three types of smart contracts, one management contract (MC) is used to manage other contracts in the system, one policy decision contract (PDC) is responsible for obtaining attribute information from PIP and making final access control right decision, and a large number of policy contracts (PCs) which composed of a public policy contract (PPC) and a large number of exclusive policy contracts (EPCs). These PCs are used to implement specific attribute-based access control policies. To demonstrate the application of the scheme, we simulated a scenario of access control in a home IoT environment and verified the feasibility of access control decisions using our proposed scheme through three experiments.
The main quality of a smart contract relies on the automation of contractual relationships, as the performance is triggered by an algorithm in turn triggered by the fulfilment of certain events. Most of the benefits arising from smart contracts are based on the âself-executingâ and âself-enforcingâ character, which represent a source of innovation for general contract law. Smart contracts use blockchain to ensure the transparency of the contractual relationship and to create trust in the capacity to execute the contract, which depends on the technology used. The aim of the present essay is to investigate whether and how blockchain technology platforms and smart contracts could be considered a modern form of private authority, which at least partially escapes the application of mandatory rules and traditional enforcement mechanisms. In particular, the authors will devote attention to innovative self-help mechanisms and dispute resolution systems, which can be depicted as âalternativeâ insofar as they present themselves as independent from courts and other national state authorities.
We assess how the cost structure of cryptocurrency mining affects the response of miners to exchange rate fluctuations and the immutability of cryptocurrency ledgers that rely on proof-of-work. We show that the amount of mining power supplied to currencies that rely on specialized hardware, such as Bitcoin, responds less to adverse exchange rate shocks than other currencies respond to such shocks, a fact that is instrumental to avoiding double-spending attacks. The results may change if mining equipment used for one cryptocurrency can be transferred to another. For smaller currencies with low exchange rate correlation, transferability eliminates the protection that fixed costs provide. Our results weaken doomsday predictions for Bitcoin and other cryptocurrencies with declining block rewards. This paper was accepted by Bruno Biais, Special Section of Management Science: Blockchains and Crypto Economics. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.4901 .
Cryptocurrency history begins in 2008 as a means of payment proposal. However, cryptocurrencies evolved into a complex ecosystem of high yield speculative assets. Contrary to traditional financial instruments, they are not (mostly) traded in organized, law-abiding venues, but on online platforms, where anonymity reigns. This paper examines the long term memory in return and volatility, using high frequency time series of seven important coins. Our study covers the pre-Covid-19 and the subsequent pandemic period. We use a recently developed method, based on the wavelet transform, which provides more robust estimators of the Hurst exponent. We detect that, during the peak of Covid-19 pandemic (around March 2020), the long memory of returns was only mildly affected. However, volatility suffered a temporary impact in its long range correlation structure. Our results could be of interest for both academics and practitioners.
Selin Sezer, Clemens Eyhoff, Wolfgang Prinz, Thomas Rose
Due to the increase in smart contracts in Ethereum, a need for proper classification has emerged. Although the smart contracts are accessible due to the open nature of the Blockchain, readability is still an issue with respect to the smart contract bytecode. We propose an automated approach for classifying smart contracts that utilize popular text classification methods on the opcode translation of the smart contract bytecode in order to overcome this limitation. Our experiments indicate that the decision-tree-based techniques like Random Forest and Xgboost outmatch the traditional classification tools like NaĂÂŻve Bayes, Logistic Regression, and SVM once the opcode input is presented as n-gram tf-idf vectors.
Ethereum smart contracts are distributed programs running on top of the Ethereum blockchain. Since program flaws can cause significant monetary losses and can hardly be fixed due to the immutable nature of the blockchain, there is a strong need of automated analysis tools which provide formal security guarantees. Designing such analyzers, however, proved to be challenging and error-prone. We review the existing approaches to automated, sound, static analysis of Ethereum smart contracts and highlight prevalent issues in the state of the art. Finally, we overview eThor, a recent static analysis tool that we developed following a principled design and implementation approach based on rigorous semantic foundations to overcome the problems of past works.
Yuze Li, Shangrong Jiang, Xuerong Li, Shouyang Wang
Abstract In recent years, Bitcoin has received substantial attention as potentially high-earning investment. However, its volatile price movement exhibits great financial risks. Therefore, how to accurately predict and capture changing trends in the Bitcoin market is of substantial importance to investors and policy makers. However, empirical works in the Bitcoin forecasting and trading support systems are at an early stage. To fill this void, this study proposes a novel data decomposition-based hybrid bidirectional deep-learning model in forecasting the daily price change in the Bitcoin market and conducting algorithmic trading on the market. Two primary steps are involved in our methodology framework, namely, data decomposition for inner factors extraction and bidirectional deep learning for forecasting the Bitcoin price. Results demonstrate that the proposed model outperforms other benchmark models, including econometric models, machine-learning models, and deep-learning models. Furthermore, the proposed model achieved higher investment returns than all benchmark models and the buy-and-hold strategy in a trading simulation. The robustness of the model is verified through multiple forecasting periods and testing intervals.
Bitcoin is a current popular cryptocurrency with a promising future. Itâs like a stock market with time series, the series of indexed data points. We looked at different deep learning networks and methods of improving the accuracy, including min-max normalization, Adam optimizer and windows min-max normalization. We gathered data on the Bitcoin price per minute, and we rearranged them to reflect Bitcoin price in hours, a total of 56,832 points. We took 24 hours of data as input and output the Bitcoin price of the next hour. We compared the different models and found that the lack of memory means that Multi-Layer Perceptron (MLP) is ill-suited for the case of predicting price based on current trend. Long Short-Term Memory (LSTM) provides relatively the best prediction when past memory and Gated Recurrent Network (GRU) is included in the model.
The goal of this paper is to compare the accuracy of bitcoin price in USD prediction based on two different model, Long Short term Memory (LSTM) network and ARIMA model. Real-time price data is collected by Pycurl from Bitfine. LSTM model is implemented by Keras and TensorFlow. ARIMA model used in this paper is mainly to present a classical comparison of time series forecasting, as expected, it could make efficient prediction limited in short-time interval, and the outcome depends on the time period. The LSTM could reach a better performance, with extra, indispensable time for model training, especially via CPU.
The underlying transparency of the Bitcoin blockchain allows transactions in the network to be tracked in near real-time. When someone transfers a large number of Bitcoins, the market receives this information and traders can adjust their expectations based on the new information. This paper investigates trading volume and its relation to asymmetric information around transfers on the Bitcoin blockchain. We collect data on 2132 large transactions on the Bitcoin blockchain between September 2018 and November 2019, where 500 or more Bitcoins were transferred. Using event study methodology, we identify significant positive abnormal trading volume for the 15-minute window before a large Bitcoin transaction as well as during and after the event. Using public information about Bitcoin addresses of cryptocurrency exchanges as proxies for information asymmetry, we find that transactions with high levels of information asymmetry negatively affect abnormal trading volume once the event becomes public knowledge, while some effects are even opposite for transactions with lower information asymmetry. The results show that blockchain transaction activity is a relevant aspect of Bitcoinns microstructure, as informed traders make use of the information in general and adjust their expectations based on the degree of information asymmetry.
The paper presents a novel analysis specifically investigating as to whether stocks associated with leading blockchain patent-developments influence the price volatility of Bitcoin across multiple time frequencies. It is important to further develop our understanding of the inter-dynamics between this relatively youthful financial product and pricing sensitivities associated with corporate technological advancement. Several interesting results are presented. First, Bitcoin is identified as a volatility receiver instead of a transmitter across all of the time frequencies considered during periods of patent development. Secondly, Microsoft, Mastercard, Intel and Visa contribute the largest volatility spillovers to the Bitcoin market due to patent development. Finally, for most of the companies considered, the calculated spillover effects towards Bitcoin markets are found to increase from the short-term to the long-term. These results suggest the existence of an avenue through which large corporations can influence cryptocurrency prices through their announcements of future technological intentions. The inherent risks incorporated with blockchain and cryptocurrency patent-development should be studied in detail, with particular warnings presented to those companies with no evidence of prior exposure and market knowledge.
The subjects of this dissertation are distributed consensus systems (DCS). These systems gained prominence with the implementation of cryptocurrencies, such as Bitcoin. This work aims at understanding the drivers and motives behind the adoption of this class of technologies, and to â consequently â evaluate the social and normative implications of blockchains and distributed ledgers. To do so, a phenomenological account of the field of distributed consensus systems is offered, then the core claims for the adoption of systems are taken into consideration. Accordingly, the relevance of these technologies on trust and governance is examined. It will be argued that the effects on these two elements do not justify the adoption of distributed consensus systems satisfactorily. Against this backdrop, it will be held that blockchains and similar technologies are being adopted because they are regarded as having a valid claim to authority as specified by Max Weber, i.e., herrschaft. Consequently, it will be discussed whether current implementations fall â and to what extent â within the legitimate types of traditional, charismatic, and rational-legal authority. The conclusion is that the conceptualization developed by Weber does not capture the core ideas that appear to establish the belief in the legitimacy of distributed consensus systems. Therefore, this dissertation describes the herrschaft of systems such as blockchains by conceptualizing a computational extension of the pure type of rational-legal authority, qualified as algorithmic authority. The foundational elements of algorithmic authority are then discussed. Particular attention is focused on the idea of normativity cultivated in systems of algorithmic rules as well as the concept of decentralization. Practical suggestions conclude the following dissertation.
High availability of data is responsible for the current trends in Artificial Intelligence (AI) and Machine Learning (ML). However, high-grade datasets are reluctantly shared between actors because of lacking trust and fear of losing control. Provenance tracing systems are a possible measure to build trust by improving transparency. Especially the tracing of AI assets along complete AI value chains bears various challenges such as trust, privacy, confidentiality, traceability, and fair remuneration. In this paper we design a graph-based provenance model for AI assets and their relations within an AI value chain. Moreover, we propose a protocol to exchange AI assets securely to selected parties. The provenance model and exchange protocol are then combined and implemented as a smart contract on a permission-less blockchain. We show how the smart contract enables the tracing of AI assets in an existing industry use case while solving all challenges. Consequently, our smart contract helps to increase traceability and transparency, encourages trust between actors and thus fosters collaboration between them.
Blockchain technology has been emerged in the last decade and has gained a lot of interests from several sectors such as finance, government, energy, health, etc. This paper gives a broad ranging survey of the application of blockchain in healthcare domain. In fact, the ongoing research in this area is evolving rapidly. Therefore, we have identified several use cases in the state of art applying the blockchain technology, for instance for sharing electronic medical records, for remote patient monitoring, for drug supply chain, etc. We have focused also on identifying limitations of studied approaches and finally we have discussed some open research issues and the areas of future research.