Blockchain-Research and Applications最新文献

There is a growing interest in understanding the energy and environmental footprint of digital currencies, specifically in cryptocurrencies such as Bitcoin and Ethereum. These cryptocurrencies are operated by a geographically distributed network of computing nodes, making it hard to estimate their energy consumption accurately. Existing studies, both in academia and industry, attempt to model cryptocurrency energy consumption often based on a number of assumptions, for instance, about the hardware in use or the geographic distribution of the computing nodes. A number of these studies have already been widely criticized for their design choices and subsequent over- or under-estimation of energy use.

In this study, we evaluate the reliability of prior models and estimates by leveraging existing scientific literature from fields cognizant of blockchain, such as social energy sciences and information systems. We first design a quality assessment framework based on existing research, and we then conduct a systematic literature review examining scientific and non-academic literature demonstrating common issues and potential avenues of addressing these issues.

Our goal with this article is to advance the field by promoting scientific rigor in studies focusing on blockchain energy footprint. To that end, we provide a novel set of codes of conduct for the five most widely used research methodologies: Quantitative energy modeling, literature reviews, data analysis and statistics, case studies, and experiments. We envision that this code of conduct would assist in standardizing the design and assessment of studies focusing on blockchain-based systems' energy and environmental footprint.

Decentralized autonomous organizations (DAOs) have found use in the governance of open source software (OSS) projects. However, the governance of an OSS producing DAO should match the particularities of OSS production while also overcoming the existing challenges of decentralized governance. The existing decentralized governance frameworks do not include all the governance activities of OSS projects. Therefore, this study presents a governance framework for DAOs that produce OSS. The framework is built upon a total of 34 articles on DAO and OSS governance. The framework was evaluated in three leading DAOs that produce OSS. The evaluation underscores the significance of the framework and proves the potential of the systematic categorization of governance mechanisms. Finally, we list emerging governance practices in various governance domains in this developing field.

Our aim is to determine the conditions for quantum computing technology to give rise to the security risks associated with quantum Bitcoin mining. Specifically, we determine the speed and energy efficiency a quantum computer needs to offer an advantage over classical mining. We analyze the setting in which the Bitcoin network is entirely classical except for a single quantum miner with a small hash rate compared to the network. We develop a closed-form approximation for the probability that the quantum miner successfully mines a block, with this probability dependent on the number of Grover iterations the quantum miner applies before making a measurement. Next, we show that for a quantum miner that is “peaceful”, this success probability is maximized if the quantum miner applies Grover iterations for 16 ​min before measuring, which is surprising, as the network mines blocks every 10 ​min on average. Using this optimal mining procedure, we show that the quantum miner outperforms a classical computer in efficiency (cost per block) if the condition Q ​< ​Crb is satisfied, where Q is the cost of a Grover iteration, C is the cost of a classical hash, r is the quantum miner's speed in Grover iterations per second, and b is a factor that attains its maximum if the quantum miner uses our optimal mining procedure. This condition lays the foundation for determining when quantum mining and the known security risks associated with it will arise.

The rise in the adoption of blockchain technology has led to increased illegal activities by cybercriminals costing billions of dollars. Many machine learning algorithms are applied to detect such illegal behavior. These algorithms are often trained on the transaction behavior and, in some cases, trained on the vulnerabilities that exist in the system. In our approach, we study the feasibility of using the Domain Name (DN) associated with the account in the blockchain and identify whether an account should be tagged malicious or not. Here, we leverage the temporal aspects attached to the DN. Our approach achieves 89.53% balanced-accuracy in detecting malicious blockchain DNs. While our results identify 73769 blockchain DNs that show malicious behavior at least once, out of these, 34171 blockchain DNs show persistent malicious behavior, resulting in 2479 malicious blockchain DNs over time. Nonetheless, none of these identified malicious DNs were reported in new officially tagged malicious blockchain DNs.

Despite the increase in the number of blockchain-based Decentralized Autonomous Organizations (DAOs), there is no consensus on what constitutes a DAO. This paper provides an in-depth study of DAOs by analyzing their definitions, characteristics, and emerging developments. Existing definitions in the literature hardly recognize common functionalities and intermingle coded DAOs, DAO deployment platforms, and blockchain DAOs. We developed a comprehensive DAO definition by reviewing the literature and empirically analyzing 1,859 DAOs. The findings show that many DAOs were inactive and that a threshold of 20 tokenholders is a tipping point for DAOs to survive over time and maintain sustained levels of activity. Finally, based on an empirical analysis of 9,845 perceived DAOs, we identified the emerging development of off-chain voting. This emerging development challenges the autonomous nature of DAOs. We recommend further research to investigate the effect of governance structures on their long-term sustainability and viability for both on-chain and off-chain DAOs.

Conflicts between supply chain members emerge because individual strategic actions may not be jointly optimal. Efforts to forecast consumer demand represent a source of conflict. The coordination of forecasts requires a powerful incentive alignment approach. This work proposes a smart contract equipped consortium blockchain system that creates an incentive structure that makes coordination with respect to forecasts economically appealing. Distortions of demand information due to uncoordinated forecasting are captured by a bullwhip measure that factors both forecast error and variance. Cooperation under the system is shown to help minimize this bullwhip measure, thus generating new outcomes for the participants that allow for a higher reward. Under a fixed payout structure, the system achieves credibility of continued cooperation, thus promoting an optimally coordinated equilibrium between the retailer and supplier. Blockchain technology represents a novel information system and consensus formation mechanism that can intermediate the behavior of a supply chain network.

As an advantageous technique and service, the blockchain has shown great development and application prospects. However, its security has also met great challenges, and many security vulnerabilities and attack issues in blockchain-based services have emerged. Recently, security issues of blockchain have attracted extensive attention. However, there is still a lack of blockchain security research from a full-stack architecture perspective, as well as representative quantitative experimental reproduction and analysis. We aim to provide a security architecture to solve security risks in blockchain services from a full-stack architecture perspective. Meanwhile, we propose a formal definition of the full-stack security architecture for blockchain-based services, and we also propose a formal expression of security issues and defense solutions from a full-stack security perspective. We use ConCert to conduct a smart contract formal verification experiment by property-based testing. The security vulnerabilities of blockchain services in the Common Vulnerabilities and Exposures (CVE) and China Nation Vulnerability Database (CNVD) are selected and enumerated. Additionally, three real contract-layer real attack events are reproduced by an experimental approach. Using Alibaba's blockchain services and Identity Mixer in Hyperledger Fabric as a case study, the security problems and defense techniques are analyzed and researched. At last, the future research directions are proposed.

Intellectual property rights (IPR) management needs to evolve in a digital world where not only companies but also many independent content creators contribute to our culture with their art, music, and videos. In this respect, blockchain has recently emerged as a promising infrastructure, providing a trustworthy and immutable environment through the use of smart contracts, which may enable more agile management of digital rights and streamline royalty payments. However, no widespread consensus has been reached on the ability of this technology to adequately manage and transfer IPR. This paper presents an innovative approach to digital rights management developed within the scope of an international research endeavour co-financed by the European Commission named MediaVerse. The approach proposes the combined usage of smart legal contracts and blockchain smart contracts to take care of the legally binding contractual aspects of IPR and, at the same time, the need for notarization, rights transfer, and royalty payments. The work being conducted represents a contribution to advancing the current literature on IPR management that may lead to an improved and fairer monetization process for content creators as a means of individual empowerment.

In order to preserve privacy in a blockchain ecosystem, the main objective is to keep a transaction's data private, such as the sender, the receiver, and the amount transferred. The current work studies the cryptographic tools commonly used to achieve this type of privacy, primarily focusing on the Ethereum blockchain. Such tools usually require many computational and storage resources, leading to additional fees. An anonymous auction protocol was developed as a case study to explore these costs, where hiding the identity and the amount of the bids utilizes a variety of cryptographic primitives. The proposed implementation was compared against three sealed-bid auction protocols, which utilize similar cryptographic tools for preserving privacy throughout the auction process. The results show that providing an additional level of anonymity, such as hiding someone's identity, can increase the gas cost significantly, up to 2.5 times, depending on the choice of the cryptographic tools, which determine the usage of the blockchain's storage and computational resources. By adjusting the level of decentralization on the application level by moving some operations off-chain and maintaining the role of the auctioneer, we show that we can maintain anonymity while reducing the gas cost by 40%.

Smart contracts are the building blocks of blockchain systems that enable automated peer-to-peer transactions and decentralized services. Smart contracts certainly provide a powerful functional surplus for maintaining the consistency of transactions in applications governed by blockchain technology. Smart contracts have become lucrative and profitable targets for attackers because they can hold a large amount of money. Formal verification and symbolic analysis have been employed to combat these destructive scams by analysing the codes and function calls, yet each scam's vulnerability should be discreetly predefined. In this work, we introduce ADEFGuard, a new anomaly detection framework based on the behaviour of smart contracts, as a new feature. We design a learning and monitoring module to determine fraudulent smart contract behaviours.

Our framework is advantageous over basic algorithms in three aspects. First, ADEFGuard provides a unified solution to different genres of scams, relieving the need for code analysis skills. Second, ADEFGuard's inference is orders of magnitude faster than code analysis. Third, the experimental results show that ADEFGuard achieves high accuracy (85%), precision (75%), and recall (90%) for malicious contracts and is potentially useful in detecting new malicious behaviours of smart contracts.