Blockchain Papers

Ziang Yuan, Tan Yang, Jiantong Cao

No abstract is available for this record.

Wisnu Uriawan, Youakim Badr, Omar Hasan, Lionel Brunie

International audience

Liang Cai, Qilei Li, Xiubo Liang

No abstract is available for this record.

Akanksha Upadhyay, Gaurav Baranwal

No abstract is available for this record.

Namrata Jain, Kosuke Kaneko, Subodh Sharma

No abstract is available for this record.

Nikolaos Nousias, George Tsakalidis, Sophia Petridou, Kostas Vergidis

No abstract is available for this record.

Lavina Pahuja, Ahmad Kamal

Cryptocurrencies continue to captivate businesses and investors despite market fluctuations. The number of crypto users have risen rapidly in the last few years, and alarmingly, many appear to be unaware of the risks involved. These risks aren't confined to market hazards but include very sophisticated cybercrimes related to cryptocurrencies. As cryptocurrencies have become a breeding ground for a variety of cybercrimes, resulting in enormous financial losses, it hinders user adoption limiting the utility of the blockchain technology. It has become crucial to spot such scams and devise intelligent techniques to make this technology a safer place for investors. This study proposes a classification model to handle fake account problem over Ethereum blockchain, and its contribution is multi-faceted; firstly, available imbalanced Ethereum dataset has been balanced to enhance the accuracy of the classification model. Secondly, correlation-based feature selection technique has been applied to retain best discriminating features. Thirdly, an effective machine learning based model has been presented for the identification of fake accounts over the Ethereum system. A comparative study of ten machine learning techniques has been presented consisting of both individual and ensemble classifiers. Experimental results showed that ensemble classifiers appear to yield better performance measures over individual classifiers and among all, LightGbm-based classification model outperformed with 99.2% accuracy.

Davi Pedro Bauer

The Ethereum Name Service (ENS) allows users to send and receive Ethereum as well as access special websites by using simple names rather than long, complex sequences of letters and numbers.

Liang Cai, Qilei Li, Xiubo Liang

No abstract is available for this record.

Fokke Heikamp, Lei Pan, Rolando Trujillo-Rasúa, Sushmita Ruj · 5 authors

No abstract is available for this record.

P. Shamili, B. Muruganantham

No abstract is available for this record.

Francesco Maria De Collibus, Alberto Partida, Matija Piškorec

We analyse the transaction networks of four representative ERC-20 tokens that run on top of the public blockchain Ethereum and can be used as collateral in DeFi: Ampleforth (AMP), Basic Attention Token (BAT), Dai (DAI) and Uniswap (UNI). We use complex network analysis to characterize structural properties of their transaction networks. We compute their preferential attachment and we investigate how critical code-controlled nodes ( smart contracts , SC) executed on the blockchain are in comparison to human-owned nodes ( externally owned accounts , EOA), which are be controlled by end users with public and private keys or by off-blockchain code. Our findings contribute to characterise these new financial networks. We use three network dismantling strategies on the transaction networks to analyze the criticality of smart contract and known exchanges nodes as opposed to EOA nodes. We conclude that smart contract and known exchanges nodes play a structural role in holding up these networks, theoretically designed to be distributed but in reality tending towards centralisation around hubs. This sheds new light on the structural role that smart contracts and exchanges play in Ethereum and, more specifically, in Decentralized Finance (DeFi) networks and casts a shadow on how much decentralised these networks really are. From the information security viewpoint, our findings highlight the need to protect the availability and integrity of these hubs.

Motoya Ishimaki, Kazumasa Omote

No abstract is available for this record.

Atta ur Rehman Khan, Raja Wasim Ahmad

Recycling of electronic waste is a rapidly growing global issue that requires proper monitoring and tracing of electronic devices and the business transactions between the stakeholders. The majority of current systems that manage electronic devices throughout their supply chain stages are centralized and lack data transparency, immutability, and security. Specifically, such systems are incapable of handling problems like comprehensive coverage of the life cycle of e-products, access control for maintaining data security, reputation-aware selection of stakeholders, and large amounts of data generated during various stages of the supply chain processes. In this paper, we propose a blockchain-based IoT-enabled system for monitoring all post-production business processes, activities, and operations performed on an electronic device. The system is supported by five smart contracts that record the actions of users on the immutable distributed ledger that aid in ensuring that the business processes carried out by the participants are transparent, traceable, and secure. To store large files, such as images of e-waste materials, products, and licenses for stakeholders, we have integrated our system with a distributed storage system. The proposed system is tested on Ethereum blockchain to check the gas consumption of the functions of the smart contracts. The cost and security analysis shows that the proposed system is viable.

Chris Mao

Non-fungible tokens (“NFTs”) are an emerging digital asset that has captured global attention with multi-million-dollar price tags for seemingly basic pixelated JPEG files. In March 2021, British auction house Christie’s sold a digital artwork, ‘Everydays: The First 5,000 Days’, by artist Mike Winkelmann (“Beeple”) for the Ether equivalent of $69.3 million, making it the third-most expensive artwork by a living artist.1 Beeple’s sale was by no means alone—Sotheby's sold an NFT collection of 101 ‘Bored Apes’ for $24.4 million;2 CryptoPunk #7804, one of 10,000 unique ‘CryptoPunk’ NFTs sold for $7.56 million,3 and Twitter founder Jack Dorsey’s first-ever tweet sold for $2.9 million as an NFT.4 NFT sales in the first-half of 2021 have already exceeded $2.5 billion,5 and, as of October 2021, the total value of NFTs on the Ethereum blockchain is estimated to be at least $14.3 billion.6 On one hand, NFTs may be poised to revolutionize creative industries and drastically alter consumer interaction with digital media.7 On the other hand, the NFT market is simultaneously both ripe for speculative investment and vulnerable to criminal activity.8 To date, there appears to be no consensus on the regulation of NFTs, neither from the perspective of generally applicable laws, regulatory capture under existing financial market regulation, nor the implementation of new digital asset laws. This paper attempts to highlight several pertinent dangers of NFTs, from a profound misunderstanding of what an NFT transaction entails, their bubble-like pricing, to various criminal activity concerns. By illustrating how existing laws and regulations may not fully capture nor address these dangers, as well as the potential oversight of NFTs in newly proposed digital asset laws, this paper proposes a categorial approach to regulating NFTs, by reducing the current (and likely future) use-cases of NFTs to their constituent categories and in turn, suggesting the most appropriate regulatory approach to each. Ultimately, given the (potential) wide-ranging use-cases of NFTs, this paper proposes that the NFT’s intended use-case described in broad categorical terms, or more aptly, its underlying reference asset and simultaneous conveyance, or lack thereof, should dictate the regulatory approach.

Zigui Jiang, Zibin Zheng, Kai Chen, Xiapu Luo · 6 authors

Since the development of Blockchain 2.0, the smart contract has become the core of blockchain. However, smart contracts with inaccurate or non-standard codes and settings may cause security vulnerabilities, extra expense cost and wast of computing resource. To avoid these problems and assist users to create new smart contract or apply existing smart contract in a more efficient way, we propose smart contract recommendation by regarding smart contract as a special form of software service in a blockchain system. First, four real-world datasets are obtained from Ethereum and EOSIO for smart contract recommendation. Then, a novel smart contract recommendation framework is proposed and evaluated. In the large-scale experiments, the results validate the feasibility of smart contract recommendation. Additionally, the datasets are publicly released online to other researchers for further studies on smart contract recommendation.

Carlos Trucíos, James W. Taylor

Abstract Several procedures to forecast daily risk measures in cryptocurrency markets have been recently implemented in the literature. Among them, long‐memory processes, procedures taking into account the presence of extreme observations, procedures that include more than a single regime, and quantile regression‐based models have performed substantially better than standard methods in terms of forecasting risk measures. Those procedures are revisited in this paper, and their value at risk and expected shortfall forecasting performance are evaluated using recent Bitcoin and Ethereum data that include periods of turbulence due to the COVID‐19 pandemic, the third halving of Bitcoin, and the Lexia class action. Additionally, in order to mitigate the influence of model misspecification and enhance the forecasting performance obtained by individual models, we evaluate the use of several forecast combining strategies. Our results, based on a comprehensive backtesting exercise, reveal that, for Bitcoin, there is no single procedure outperforming all other models, but for Ethereum, there is evidence showing that the GAS model is a suitable alternative for forecasting both risk measures. We found that the combining methods were not able to outperform the better of the individual models.

João Henrique Faes Battisti, Guilherme Koslovski, Maurício A. Pillon, Charles C. Miers · 5 authors

No abstract is available for this record.

Weijia Zhang, Tej Anand

Bitcoin, introduced in 2008, is considered the first implementation of blockchain. Subsequent implementations of blockchain have made changes to Bitcoin to ease application development, improve scalability, and enhance versatility in terms of the types of applications that can be created.

Jatin Manav Mutharasu, Utshav Pandey, B. Rethick, Bhavika Kulkarni · 5 authors

No abstract is available for this record.

Liang Cai, Qilei Li, Xiubo Liang

No abstract is available for this record.

Franck Cassez, Joanne Fuller, Horacio Mijail Antón Quiles

We present a methodology to develop verified smart contracts. We write smart contracts, their specifications and implementations in the verification-friendly language Dafny. In our methodology the ability to write specifications, implementations and to reason about correctness is a primary concern. We propose a simple, concise yet powerful solution to reasoning about contracts that have external calls. This includes arbitrary re-entrancy which is a major source of bugs and attacks in smart contracts. Although we do not yet have a compiler from Dafny to EVM bytecode, the results we obtain on the Dafny code can reasonably be assumed to hold on Solidity code: the translation of the Dafny code to Solidity is straightforward. As a result our approach can readily be used to develop and deploy safer contracts.

Sang Rae Kim

No abstract is available for this record.

Jiří Kukačka, Ladislav Krištoufek

Abstract The driving forces behind cryptoassets’ price dynamics are often perceived as being dominated by speculative factors and inherent bubble-bust episodes. Fundamental components are believed to have a weak, if any, role in the price-formation process. This study examines five cryptoassets with different backgrounds, namely Bitcoin, Ethereum, Litecoin, XRP, and Dogecoin between 2016 and 2022. It utilizes the cusp catastrophe model to connect the fundamental and speculative drivers with possible price bifurcation characteristics of market collapse events. The findings show that the price and return dynamics of all the studied assets, except for Dogecoin, emerge from complex interactions between fundamental and speculative components, including episodes of price bifurcations. Bitcoin shows the strongest fundamentals, with on-chain activity and economic factors driving the fundamental part of the dynamics. Investor attention and off-chain activity drive the speculative component for all studied assets. Among the fundamental drivers, the analyzed cryptoassets present their coin-specific factors, which can be tracked to their protocol specifics and are economically sound.