While transactions with cryptocurrencies such as Ethereum are becoming more prevalent, fraud and other criminal transactions are not uncommon. Graph analysis algorithms and machine learning techniques detect suspicious transactions that lead to phishing in large transaction networks. Many graph neural network (GNN) models have been proposed to apply deep learning techniques to graph structures. Although there is research on phishing detection using GNN models in the Ethereum transaction network, models that address the scale of the number of vertices and edges and the imbalance of labels have not yet been studied. In this paper, we compared the model performance of GNN models on the actual Ethereum transaction network dataset and phishing reported label data to exhaustively compare and verify which GNN models and hyperparameters produce the best accuracy. Specifically, we evaluated the model performance of representative homogeneous GNN models which consider single-type nodes and edges and heterogeneous GNN models which support different types of nodes and edges. We showed that heterogeneous models had better model performance than homogeneous models. In particular, the RGCN model achieved the best performance in the overall metrics.
User transactions on Ethereum's peer-to-peer network are at risk of being attacked. The smart contracts building decentralized finance (DeFi) have introduced a new transaction ordering dependency to the Ethereum blockchain. As a result, attackers can profit from front- and back-running transactions. Multiple approaches to mitigate transaction reordering manipulations have surfaced recently. However, the success of individual approaches in mitigating such attacks and their impact on the entire blockchain remains largely unstudied. In this systematization of knowledge (SoK), we categorize and analyze state-of-the-art transaction reordering manipulation mitigation schemes. Instead of restricting our analysis to a scheme's success at preventing transaction reordering attacks, we evaluate its full impact on the blockchain. Therefore, we are able to provide a complete picture of the strengths and weaknesses of current mitigation schemes. We find that currently no scheme fully meets all the demands of the blockchain ecosystem. In fact, all approaches demonstrate unsatisfactory performance in at least one area relevant to the blockchain ecosystem.
Our study empirically predicts the bubble of non-fungible tokens (NFTs): transferable and unique digital assets on public blockchains. This topic is important because, despite their strong market growth in 2021, NFTs on a project basis have not been investigated in terms of bubble prediction. Specifically, we applied the logarithmic periodic power law (LPPL) model to time-series price data associated with four major NFT projects. The results indicate that, as of December 20, 2021, (i) NFTs, in general, are in a small bubble (a price decline is predicted), (ii) the Decentraland project is in a medium bubble (a price decline is predicted), and (iii) the Ethereum Name Service and ArtBlocks projects are in a small negative bubble (a price increase is predicted). A future work will involve a prediction refinement considering the heterogeneity of NFTs, comparison with other methods, and the use of more enriched data.
Renan Gomes Mendes Diniz, Diogo de Prince, Leandro Maciel
Purpose The aim of this paper is to test the existence of bubbles for the daily prices of cryptocurrencies Bitcoin and Ethereum and verify if there is a relationship between bubbles and volatility regimes. Design/methodology/approach The authors test the presence of bubbles with the generalized supremum augmented Dickey–Fuller (GSADF) test using critical values simulated by the bootstrap procedures of Gutierrez (2011), Harvey et al. (2016) and Pedersen and Schütte (2020). Also, the authors estimate Markov regime switching generalized autoregressive conditional heteroskedasticity model for these cryptocurrencies. Findings The GSADF test result indicates the presence of bubbles for both cryptocurrencies. Simulating critical values by wild-bootstrap, which is robust to non-stationary volatility, leads to the highest number of bubbles in both cryptocurrencies. In addition, based on the estimates of conditional variance models with regime changes, the authors find that the bubbles identified are associated with a regime of low returns volatility, indicating a change in the trade-off between risk and return when the prices of cryptocurrencies differ from their fundamental values. Originality/value To the best of the authors knowledge, there are no studies that test the explosive behavior for cryptocurrencies by the GSADF test using the bootstrap method to simulate critical values from the procedures of Harvey et al. (2016) or Pedersen and Schütte (2020). These bootstrapping procedures are robust to heteroscedasticity and avoid the detection of false bubbles. Further, the advantage of Harvey et al. (2016) procedure is the robustness to non-stationary volatility.
Internet backboned crowdsourcing utilizes network-wide resources to solve complicated and large-scale tasks, which are not accomplishable for independent individuals. Existing crowdsourcing platforms are mostly centralized solutions with reliability and trustworthiness fragile to single-point failures on the central servers. The innovation of distributed ledgers as blockchain inspires us to optimize the traditional crowdsourcing procedure with distributed sustainability. We propose a blockchain-based design of the distributed secure crowdsourcing scheme for task distribution and result verification without relying on any third trusted institution. A preference-based task distribution (PTD) mechanism is presented which guarantees the percentage of task distribution and the satisfaction of the chosen workers. Task works are continuously assessed for reputations based on their historical behaviors. Task completion correctness is verified by blockchain consensus in two different scenarios after workers submit their results with reputations. We implement a prototype system based on the Ethereum chain with PTD and solution verification components. With various tasks and scenarios evaluated in the system, the proposed distributed crowdsourcing framework shows system reliability, data security, and scenario feasibility.
Because of its many advantages, big data has been extending to various domains of science, health, education, and commerce. Despite its many applications, big data sharing typically suffers from some key issues, such as user control, lack of incentives, cost, and the right of data. This paper proposes a decentralized big data sharing prototype to improve the applications and services of big data. The method makes use of Ethereum blockchain and related technologies to systematically recommend the implementation guidelines. The research provides a detailed description of the design and implementation of each sublayer of a big data system. As the method is based on blockchain technology, the key technical points are properly addressed in each of the layers. For evaluation, relevant data were collected, and functional testing was performed. A comparison was performed about the sharing frequency and blockchain consensus performance of similar platforms. The dual mining node of the proposed prototype succeeded in processing 1366 blocks and 300 messages. A comparatively satisfactory file access time in the range of 10 m to 20 s and file transmission time between 100 m and 200 s were achieved. The results obtained show that this prototype can effectively verify the feasibility of the model, the layered architecture, and the related sharing mechanism. For the functional and performance testing, practical projects were implemented and evaluated. The promising results obtained testify that the research offers a theoretical background for innovative research in the domain and specialized guidelines for practical implementation.
In blockchains , transaction fees are fixed by the users. The probability for a transaction to be processed quickly increases with the fee level. In this paper, we study the transaction fee optimization problem in the Ethereum blockchain. This problem consists of determining the minimum price a user should pay so that its transaction is processed with a given probability in a given amount of time. To reach this goal, we define a new solution method based on a Monte Carlo approach to predict the probability that a transaction will be mined within a given time limit. Numerical results on real data highlight the quality of the results.
Blockchain-based systems have gained immense popularity as enablers of independent asset transfers and smart contract functionality. They have also, since as early as the first Bitcoin blocks, been used for storing arbitrary contents such as texts and images. On-chain data storage functionality is useful for a variety of legitimate use cases. It does, however, also pose a systematic risk. If abused, for example by posting illegal contents on a public blockchain, data storage functionality can lead to legal consequences for operators and users that need to store and distribute the blockchain, thereby threatening the operational availability of entire blockchain ecosystems. In this paper, we develop and apply a cloud-based approach for quickly discovering and classifying content on public blockchains. Our method can be adapted to different blockchain systems and offers insights into content-related usage patterns and potential cases of abuse. We apply our method on the two most prominent public blockchain systems - Bitcoin and Ethereum - and discuss our results. To the best of our knowledge, the presented study is the first to systematically analyze non-financial content stored on the Ethereum blockchain and the first to present a side-by-side comparison between different blockchains in terms of the quality and quantity of stored data.
Sandra Johnson, David Hyland-Wood, Anders L. Madsen, Kerrie Mengersen
The concept of 'Stateless Ethereum' was conceived with the primary aim of mitigating Ethereum's unbounded state growth. The key facilitator of Stateless Ethereum is through the introduction of 'witnesses' into the ecosystem. The changes and potential consequences that these additional data packets pose on the network need to be identified and analysed to ensure that the Ethereum ecosystem can continue operating securely and efficiently. In this paper we propose a Bayesian Network model, a probabilistic graphical modelling approach, to capture the key factors and their interactions in Ethereum mainnet, the public Ethereum blockchain, focussing on the changes being introduced by Stateless Ethereum to estimate the health of the resulting Ethereum ecosystem. We use a mixture of empirical data and expert knowledge, where data are unavailable, to quantify the model. Based on the data and expert knowledge available to use at the time of modelling, the Ethereum ecosystem is expected to remain healthy following the introduction of Stateless Ethereum.
Abylay Satybaldy, Anton Hasselgren, Mariusz Nowostawski
Background: The increasing use of various online services requires an efficient digital identity management (DIM) approach. Unfortunately, the original Internet protocols were not designed with built-in identity management, which creates challenges related to privacy, security, and usability. There is an increasing societal concern regarding the management of these sensitive data, access to it, and where it is stored. Blockchain technology can potentially offer a secure solution to address these issues in a decentralized manner without centralized authority. This is important for e-health services where the patient and the healthcare provider often are required to prove their identity. Blockchain technology can be utilized for creating digital identities and making its management easier, thus giving a higher degree of control to the user than what current solutions offer. It can be used to create a digital identity on the blockchain, making it easier for individuals and entities to manage, giving them greater control over who has their personal information and how they handle it. In addition, it might be utilized to create a higher degree of trust and security for e-health applications. Objective: The aim of this research work was to review the state-of-the-art regarding blockchain-based decentralized identity management for healthcare applications. Based on this summary, we provide a viewpoint on how blockchain-based decentralized identity frameworks might be utilized for virtualized healthcare applications. Methods: This research study applied a scoping, semi-systematic review approach to summarize the state-of-the-art. Included identity management systems were evaluated based on seven criteria: autonomy, authority, availability, approval, confidentiality, tenacity, and Interoperability. Results: Seven blockchain-based identity management systems were included and evaluated in this work: these include solutions built with Ethereum, Hyperledger Indy, Hyperledger Fabric, Hedera, and Sovrin blockchains. Conclusions: DIM is crucial for virtual health care. Decentralized identity management for healthcare purposes is currently being explored in both academia and the private sector. More work is needed with the aim of improving the efficiency of current DIM solutions and to fully understand what technical frameworks are best suited for e-health applications.
Sharing of Electronic Health Records (EHRs) is of significant importance in health care. Lately, a cloud-based electronic health record sharing scheme has been used extensively to share patient records among various healthcare organizations. However, cloud centralization may compromise patients’ privacy and security. Due to the special features of blockchain, it is important to see this technology as a promising solution to resolve these issues. This article proposes a privacy-preserving, secure EHR sharing and access control framework based on blockchain technology. The proposal aims to implement EHR blockchain technology and ensure that electronic records are stored safely by specifying user access permissions. We emulate the cryptographic primitives and use smart contracts to describe the relationships between the EHR owner and EHR user through the proposed system on the Ethereum blockchain. We assess the proposal results based on encryption and decryption time and the costs of the smart contract. The encryption and decryption times are proportional to the size of the EHR, which varies from 128 KB to 128 MB. When it comes to encryption, the smallest EHR takes 0.0012 s to encrypt, while the largest EHR, which is 128 MB, takes 1.4149 s. On the other hand, a 128 KB EHR takes 0.0013 s to decrypt, whereas a 128 MB EHR requires 1.6284 s. As a result, performance evaluation and security analysis confirm that the proposal is secure for practical application.
Going ahead, Ethereum Trader appears nicely supported with the aid of using high-quality basics past Ethereum Trader 2.zero. Its platform remains a main participant withinside the improvement and launching of latest decentralised programs (dApps). Ethereum Trader has additionally been connected with the challenge COSMOS, an infrastructure as a way to permit interoperability and the cappotential to carry out transactions among one-of-a-kind blockchain structures through the so-referred to as Gravity Bridge.\n\n\nhttps://www.theethereumtrader.com\nhttps://twitter.com/ethereumtrader_\nhttps://www.instagram.com/ethereumtrader_\nhttps://www.pinterest.co.uk/ethereumtrader\nhttps://www.linkedin.com/in/ethereumtrader/\nhttps://www.facebook.com/ethereumtrader.officials\nhttps://www.youtube.com/channel/UC5tBxyrI9LhP6OiHVyA6KdQ
Matthias Lohr, Kenneth Skiba, Marco Konersmann, Jan Jürjens · 5 authors
Existing fair exchange protocols usually neglect consideration of cost when assessing their fairness. However, in an environment with non-negligible transaction cost, e.g., public blockchains, high or unexpected transaction cost might be an obstacle for wide-spread adoption of fair exchange protocols in business applications. For example, as of 2021-12-17, the initialization of the FairSwap protocol on the Ethereum blockchain requires the selling party to pay a fee of approx. 349.20 USD per exchange. We address this issue by defining cost fairness, which can be used to assess two-party exchange protocols including implied transaction cost. We show that in an environment with non-negligible transaction cost where one party has to initialize the exchange protocol and the other party can leave the exchange at any time cost fairness cannot be achieved.
Blockchain has attracted much attention from both academia and industry since emerging in 2008. Due to the inconvenience of the deployment of large-scale blockchains, blockchain simulators are used to facilitate blockchain design and implementation. We evaluate state-of-the-art simulators applied to both Bitcoin and Ethereum and find that they suffer from low performance and scalability which are significant limitations. To build a more general and faster blockchain simulator, we extend an existing blockchain simulator, i.e. BlockSim. We add a network module integrated with a network topology generation algorithm and a block propagation algorithm to generate a realistic blockchain network and simulate the block propagation efficiently. We design a binary transaction pool structure and migrate BlockSim from Python to C++ so that bitwise operations can be used to accelerate the simulation and reduce memory usage. Moreover, we modularize the simulator based on five primary blockchain processes. Significant blockchain elements including consensus protocols (PoW and PoS), information propagation algorithms (Gossip) and finalization rules (Longest rule and GHOST rule) are implemented in individual modules and can be combined flexibly to simulate different types of blockchains. Experiments demonstrate that the new simulator reduces the simulation time by an order of magnitude and improves scalability, enabling us to simulate more than ten thousand nodes, roughly the size of the Bitcoin and Ethereum networks. Two typical use cases are proposed to investigate network-related issues which are not covered by most other simulators.
Matthias Lohr, Kenneth Skiba, Marco Konersmann, Jan Jürjens · 5 authors
Existing fair exchange protocols usually neglect consideration of cost when\nassessing their fairness. However, in an environment with non-negligible\ntransaction cost, e.g., public blockchains, high or unexpected transaction cost\nmight be an obstacle for wide-spread adoption of fair exchange protocols in\nbusiness applications. For example, as of 2021-12-17, the initialization of the\nFairSwap protocol on the Ethereum blockchain requires the selling party to pay\na fee of approx. 349.20 USD per exchange. We address this issue by defining\ncost fairness, which can be used to assess two-party exchange protocols\nincluding implied transaction cost. We show that in an environment with\nnon-negligible transaction cost where one party has to initialize the exchange\nprotocol and the other party can leave the exchange at any time cost fairness\ncannot be achieved.\n
The number of attacks and accidents leading to significant losses of crypto-assets is growing. According to Chainalysis, in 2021, approx. $14 billion has been lost due to various incidents, and this number is dominated by Decentralized Finance (DeFi) applications. In order to address these issues, one can use a collection of tools ranging from auditing to formal methods. We use formal verification and provide the first formalisation of a DeFi contract in a foundational proof assistant capturing contract interactions. We focus on Dexter2, a decentralized, non-custodial exchange for the Tezos network similar to Uniswap on Ethereum. The Dexter implementation consists of several smart contracts. This poses unique challenges for formalisation due to the complex contract interactions. Our formalisation includes proofs of functional correctness with respect to an informal specification for the contracts involved in Dexter's implementation. Moreover, our formalisation is the first to feature proofs of safety properties of the interacting smart contracts of a decentralized exchange. We have extracted our contract from Coq into CameLIGO code, so it can be deployed on the Tezos blockchain. Uniswap and Dexter are paradigmatic for a collection of similar contracts. Our methodology thus allows us to implement and verify DeFi applications featuring similar interaction patterns.
In this article forecasting of daily closing price series of Bitcoin, Ripple, Dash, Litecoin and Ethereum crypto currencies, using data on prices (open, low, high), market capital and volumes using prior days is focused. The value conduct of cryptographic forms of money remains to a great extent neglected, giving new chances to scientists and business analysts to feature the likenesses and contrasts with standard monetary costs. Hence the paper is focused on this area. he results are compared with various benchmarks. Predictions are done using statistical techniques and machine learning algorithms. A simple linear regression (SLR) model that uses only a single-variable sequence of closing prices for forecasting, and a multiple linear regression (MLR) model that uses a multivariate sequence of prices and quantities at the same time. The simple linear regression (SLR) model for univariate serial forecasting uses only closing prices. Mean Absolute Percentage Error (MAPE) and relative Root Mean Square Error (relative RMSE) performance measures are considered. The accuracy achieved by the ARIMA model on our dataset is the highest, followed by Multivariable Linear Regression and LSTM.
Seunghyeon Lee, Hong‐Woo Seok, Kirim Lee, Hoh Peter In
When surveying national reference points using a global positioning system (GPS), appropriate work regulations pertaining to the surveying time must be observed. However, such data can be modified easily, so identifying non-compliance with work regulations and forgeries is challenging. If such incidents occur in cadastral surveys, it may result in financial damages to stakeholders, such as citizens and the state. Therefore, it is necessary to improve the reliability by ensuring the integrity of the GPS positioning data and allowing anyone to track them. In this study, a prototype system was developed to record GPS data and the corrections generated during survey processes using the Ethereum blockchain network. Blockchain is a distributed ledger system that prevents the manipulation of uploaded data without the need for a centralized institution by allowing anyone to check the data. Unlike in the past, the proposed system improves the data integrity and reliability for the entire survey process through blockchain, thereby ensuring transparency of the checks using smart contract addresses.
Internet-of-Things (IoT) are increasingly operating in the zero-trust environments where any devices and systems may be compromised and hence untrusted. In addition, data collected by and sent from IoT devices may be shared with and processed by edge computing systems, in order to reduce the reliance on centralized (cloud) servers, leading to further security and privacy issues. To cope with these challenges, this paper proposes an innovative blockchain-enabled information sharing solution in zero-trust context to guarantee anonymity yet entity authentication, data privacy yet data trustworthiness, and participant stimulation yet fairness. This new solution is able to support filtering of fabricated information through smart contracts, effective voting, and consensus mechanisms, which can prevent unauthenticated participants from sharing garbage information. We also prove that the proposed solution is secure in the universal composability framework, and further evaluate its performance over an Ethereum-based blockchain platform to demonstrate its utility.
Electronic Health Records (EHRs) are essential in contemporary healthcare as they facilitate the storage and sharing of personal patient data. Traditional cloud-based EHR systems, though, are afflicted with centralized control, privacy threats, restricted interoperability, and susceptibility to data breaches. To alleviate these challenges, this paper suggests a blockchain-based, patient-centered EHR management system that uses Ethereum smart contracts, Decentralized Identifiers (DIDs), and InterPlanetary File System (IPFS) for safe, distributed, and effective handling of health data.The system put forward puts patients at the forefront of EHR access and control, allowing them to grant or withdraw permissions to healthcare providers, insurers, or researchers using fine-grained, attribute-based policies executed through smart contracts. DIDs remove the need for third-party identity providers, providing secure and verifiable user authentication directly on the blockchain. IPFS, on the other hand, provides cost-effective, tamper-proof off-chain storage of medical records, with metadata and access logs stored on-chain to minimize gas usage.Extensive testing on the Ethereum Goerli testnet proves that the system provides greater security, lower storage costs, efficient access control, and better interoperability than conventional models. This method not only solves existing shortcomings in EHR systems but also opens the door to scalable, transparent, and patient-enabled healthcare data management.
The increasing prevalence of renewable energy resources introduces a high variability that complicates the task of energy management in modern power grids. Among other technologies, batteries have proven effective in managing power imbalances in such grids. However, the high cost of large-scale batteries, coupled with their enormous space requirements, could deter their adoption by large consumers such as shared facility controllers. The aggregation of residential energy storage units offers shared facility controllers (SFCs) an alternative way to leverage storage; however, a secure scheme that promotes fairness and transparency in the selection and compensation of shared storage unit owners is needed. To this end, an Ethereum smart contract that makes residential storage capacities available to SFCs via a double auction mechanism is proposed. The contract is written with solidity and deployed in the browser-based Remix-integrated development environment. Scenario tests prove the effectiveness of the smart contract in selecting and compensating the owners of shared storage capacities, according to predefined auction rules.
Dipak D. Gaikwad, Akshay N. Hambir, hantanu S. Chavan, Gayatri K. Khedkar · 5 authors
Abstract: Real Estate Management in India as well as in many parts of the world is a very inefficient and insecure process. Developing a secure system that not only accelerates the process of land registration but also makes it efficient and secure will be effective. Blockchain technology is one of the latest and secured technologies on the horizon and has evolved over the last 9-11 years. There is tremendous potential for usage of Blockchain technology in the land industry. This paper presents a blockchainpowered real estate management system that will impart transparency, efficiency, and security in Real Estate Management. The decentralized data storage application and its interactions with Ethereum Virtual Machine (EVM) are presented to point out the event of a sensible contract which will be used for blockchain smart contracts in real estate management. Further, a detailed design and interaction mechanism are highlighted for the estate owners and users as parties to a sensible contract. It will store all the transactions on a distributed blockchain which will be very secure and will not be prone to hacking. A list of functions for initiating, creating, modifying, or terminating a sensible contract is presented and this will help the user enjoy a more immersive, user-friendly, and visualized contracting process, whereas the owners and real estate agents can enjoy more business and sales. It is a practical solution to the real estate management problem in the real world. Keywords: Blockchain, Smart Contracts, Real Estate Management, Ethereum Virtual Machine, Transparent Contracting Process
We examine the presence of outliers and time-varying jumps in the returns of four major cryptocurrencies (Bitcoin, Ethereum, Ripple, Dogecoin, Litecoin), and a broad cryptocurrency index (CCI30). The results indicate that only Bitcoin returns are contaminated with outliers. Time-varying jumps are present in Bitcoin, Litecoin, Ripple, and the cryptocurrency index. Notably, the presence of jumps in Bitcoin is significant after correcting for outliers. The main findings point to a price instability in some major cryptocurrencies and thereby the importance of accounting for large shocks and time-varying jumps in modelling volatility in the debatable cryptocurrency markets.