To govern smart contracts running on Ethereum, multiple Ethereum Request for Comment (ERC) standards have been developed, each containing a set of rules to guide the behaviors of smart contracts. Violating the ERC rules could cause serious security issues and financial loss, signifying the importance of verifying smart contracts follow ERCs. Today's practices of such verification are to either manually audit each single contract or use expert-developed, limited-scope program-analysis tools, both of which are far from being effective in identifying ERC rule violations. This paper presents a tool named AuditGPT that leverages large language models (LLMs) to automatically and comprehensively verify ERC rules against smart contracts. To build AuditGPT, we first conduct an empirical study on 222 ERC rules specified in four popular ERCs to understand their content, their security impacts, their specification in natural language, and their implementation in Solidity. Guided by the study, we construct AuditGPT by separating the large, complex auditing process into small, manageable tasks and design prompts specialized for each ERC rule type to enhance LLMs' auditing performance. In the evaluation, AuditGPT successfully pinpoints 418 ERC rule violations and only reports 18 false positives, showcasing its effectiveness and accuracy. Moreover, AuditGPT beats an auditing service provided by security experts in effectiveness, accuracy, and cost, demonstrating its advancement over state-of-the-art smart-contract auditing practices.
Everyday life depends heavily on the supply chain, and its traceability guarantees the quality and safety of the products. Thus, there is a pressing need for an effective and trustworthy solution to enhance logistic traceability. Traditional traceability systems suffer from low tracking efficiency and inconsistent data. However, the developing blockchain technology promises to improve these issues by being transparent, tamper-proof, and decentralised. This article analyses previous research, highlights problems, and investigates logistic traceability options based on blockchain. First, the conventional traceability approach and stakeholder demands are explained, along with the fundamentals of blockchain technology. Next, a thorough evaluation and analysis of the current publications and enterprise applications is conducted. Lastly, difficulties and potential lines of inquiry are explored. Subsequent studies may concentrate on developing focused consensus processes, creating suitable access controls, examining the function of regulators in the supply chain, etc. This analysis demonstrates that although there are still many obstacles to overcome, blockchain offers a lot of promise to solve traceability problems.
The blockchain ecosystem, particularly with the rise of Web3 and Non-Fungible Tokens (NFTs), has experienced a significant increase in users and applications. However, this expansion is challenged by the need to connect early adopters with a wider user base. A notable difficulty in this process is the complex interfaces of blockchain wallets, which can be daunting for those familiar with traditional payment methods. To address this issue, the category of "embedded wallets" has emerged as a promising solution. These wallets are seamlessly integrated into the front-end of decentralized applications (Dapps), simplifying the onboarding process for users and making access more widely available. However, our insights indicate that this simplification introduces a trade-off between ease of use and security. Embedded wallets lack transparency and auditability, leading to obscured transactions by the front end and a pronounced risk of fraud and phishing attacks. This paper proposes a new protocol to enhance the security of embedded wallets. Our VELLET protocol introduces a wallet verifier that can match the audit trail of embedded wallets on smart contracts, incorporating a process to verify authenticity and integrity. In the implementation architecture of the VELLET protocol, we suggest using the Text Record feature of the Ethereum Name Service (ENS), known as a decentralized domain name service, to serve as a repository for managing the audit trails of smart contracts. This approach has been demonstrated to reduce the necessity for new smart contract development and operational costs, proving cost-effective through a proof-of-concept. This protocol is a vital step in reducing security risks associated with embedded wallets, ensuring their convenience does not undermine user security and trust.
Smart contracts have played a pivotal role in the evolution of blockchains and Decentralized Applications (DApps). As DApps continue to gain widespread adoption, multiple smart contract languages have been and are being made available to developers, each with its distinctive features, strengths, and weaknesses. In this paper, we examine the smart contract languages used in major blockchain platforms, with the goal of providing a comprehensive assessment of their main properties. Our analysis targets the programming languages rather than the underlying architecture: as a result, while we do consider the interplay between language design and blockchain model, our main focus remains on language-specific features such as usability, programming style, safety and security. To conduct our assessment, we propose an original benchmark which encompasses a wide, yet manageable, spectrum of key use cases that cut across all the smart contract languages under examination. ⢠We give an abstract overview of smart contract platforms, discussing the impact of different design choices. ⢠We illustrate by examples how different design choices give rise to different programming styles for smart contracts. ⢠We consider 6 leading smart contract languages: Solidity (Ethereum), Rust (Solana), Aiken (Cardano), PyTeal (Algorand), Move (Aptos), SmartPy (Tezos). ⢠We develop an open-source benchmark of use cases of smart contracts, implemented in all the languages in our selection. ⢠Based on our benchmark, we evaluate smart contract languages focussing on their security, code readability, usability, and functionalities.
Francesco Buccafurri, Vincenzo De Angelis, Roberto Nardone
The interest in Self-Sovereign Identity (SSI) in research, industry, and governments is rapidly increasing. SSI is a paradigm where users hold their identity and credentials issued by authorized entities. SSI is revolutionizing the concept of digital identity enabling the definition of a trust framework wherein a service provider (verifier) validates the claims presented by a user (holder) for accessing services. However, current SSI solutions primarily focus on the presentation and verification of claims, overlooking a dual aspect: ensuring that the verifier is authorized to access the holder's claims. Addressing this gap, this paper introduces an innovative SSI-based solution that integrates decentralized wallets with Ciphertext-Policy Attribute-Based Proxy Re-Encryption (CP-ABPRE). This combination effectively addresses the challenge of verifier authorization. Our solution, implemented on the Ethereum platform, enhances accountability by notarizing key operations through a smart contract. The paper also offers a prototype demonstrating the practicality of the proposed approach. Furthermore, it provides an extensive evaluation of the solution's performance, emphasizing its feasibility and efficiency in real-world applications.
Cryptocurrencies have become an attractive asset class for all types of investors. A relevant question is whether their inclusion in portfolios improves their risk-return output. In this chapter, we conduct an empirical study of the effect of the inclusion of Bitcoin and Ethereum in the portfolio of a European investor. Additionally, we analyze the results of previous studies on this question under other assumptions. The empirical data are overwhelming regarding the attractiveness of Bitcoin and by extension other cryptocurrencies as an asset class. The important question is whether this appeal is temporary and will eventually disappear so investors do not have to worry about this new asset class. In the chapter we discuss this issue.
This paper proposes a novel blockchain-based advanced banking system that leverages the distributed ledger technology of Ethereum to create a secure, transparent, and efficient financial system. The system utilizes a permission blockchain network, where participants are pre-authorized to join and contribute to the network. This ensures the security and integrity of the system, while still allowing for a wider range of participants than a fully public blockchain network. The system works by first having a user initiate a transaction request. The system then verifies the user's identity and ensures the authenticity of the request. The transaction is entered into the blockchain ledger after it has been validated. This ensures that the transaction is immutable and transparent, as all participants in the network can view the transaction details. The system also includes a fraud detection mechanism that can detect any changes made to the transaction after it has been recorded on the blockchain. This helps to ensure the security of the system and protect users from fraudulent activity. The proposed system has the potential to revolutionize the banking industry by providing a more secure, transparent, and efficient way to conduct financial transactions. Keywords: blockchain, online banking, ethereum, ganache, hash, smart contract, blocks
Ethereum and its native cryptocurrency, Ether, have played a worthy attention in the development of the blockchain and cryptocurrency space. Its programmability and smart contract capabilities have made it a foundational platform for decentralized applications and innovations across various industries. Because of its anonymous and decentralized structure, the hotheaded expansion of cryptocurrencies in the payment space has created both enormous potential and concerns related to cybercrime, including money laundering, financing terrorism, illegal and dangerous services. As more financial institutions attempt to integrate cryptocurrencies into their networks, there is an increasing need to create a more transparent network that can withstand these kinds of attacks. In this work, we are using different classification techniques, such as logistic regression (LR), random forest (RF), k-nearest neighbors (KNN), adaptive boosting (AdaBoost), and extreme gradient boosting (XGBoost) for Ethereum fraud detection. The dataset we are using includes rows of legitimate transactions done using the cryptocurrency Ethereum as well as known fraudulent transactions. The âXGBoostâ model, which is noteworthy, detects variations that might attract notice and prevent potential issues in this chore.
Abstract: As a consequence of mass unemployment being the byproduct of COVID-19, people around the world discovered investment in cryptocurrency as a means to tackle their declining financial condition. Subsequently, the prominence of Ethereum as a platform for crypto transactions also gave rise to fraudulent transactions. The need to detect these frauds exists even today. This study proposes a token-based approach to detect fraud in Ethereum transactions incorporating the ERC20 standard, by employing machine learning techniques. After cleaning and preprocessing of the dataset, the transaction data was fed to Random Forest (RF), AdaBoost, Extra Trees (ET), Gradient Boosting (GB) and Extreme Gradient Boosting (XGB) classifiers in search of the most suitable model for fraud detection. Meticulous evaluation revealed that RF, ET and XGB classifiers yielded the highest accuracy of 95%. The proposed token-based approach hence presents a novel and efficient solution for fraud detection, with room for improvement and scalability.
Kushal Patil, Asit Khobragade, Aditya Awati, Bharati Nikose
This paper presents the Blockchain-Based Decentralised File Sharing System (BBDFS),also called DecentraShare a complete, production-ready system that solves thethree fundamental weaknesses of cloud-based le sharing: lack of privacy, absence oftamper evidence, and susceptibility to censorship. The system integrates three tech-nologies: the InterPlanetary File System (IPFS) for peer-to-peer distributed storage,Ethereum-compatible smart contracts deployed on the Polygon Proof-of-Stake net-work for programmable access control, and AES-256-GCM symmetric encryptionperformed entirely inside the user's browser via the Web Crypto API.When a le is uploaded, it is encrypted locally before leaving the user's device the backend server never receives plaintext content. A content identier (CID)returned by IPFS is anchored on the blockchain alongside a SHA-256 integrity hash.Three Solidity 0.8.19 smart contracts FileRegistry.sol, AccessControl.sol,and IntegrityLedger.sol manage ownership, per-wallet permission grants andrevocations, and a chained integrity audit log respectively. A stateless Node.js 20/ Express 4.18 backend exposes 19 REST API endpoints, while real-time Server-Sent Events (SSE) deliver access notications to recipients within a median of 18milliseconds.Performance evaluation over 1,500 independent le-transfer trials demonstrates amedian upload latency of 2.58 seconds for 1MB payloads, on-chain conrmationwithin 2.1 seconds on Polygon, and 99.97% data retrievability under normal operat-ing conditions. Gas optimisation reduced smart contract deployment cost by 46.7%over naive reference implementations. Comparative analysis against Storj, Filecoin,Swarm, and AWS S3 conrms that BBDFS is the only evaluated system simultane-ously providing end-to-end encryption, on-chain access control, and tamper-evidentauditing.
Block chain technology has surfaced as a transformative influence, revolutionizing the verification and recording of transactions across industries. In to traditional data base systems, presenting a novel approach to database management. The objective is to enhance the trust, transparency, and security of data storage and transactions. Research in this field aims to strengthen data protection, reduce the risk of unauthorized access, and prevent data tampering. By providing a transparent and auditable record of transactions, block chain enhances trust among parties. This is particularly important in industries such as finance, where the integrity of transactions is paramount. The Weighted Sum Model (WSM) stands as a straightforward and widely utilized method of multi-criteria decision-making. It operates by multiplying assigned value of each attribute by the weight of importance designated by the decision maker. Subsequently, it entails aggregating these products across all criteria to compute evaluation scores for each alternative parameters considered in this study include Bitcoin, Ethereum, Biance Smart Chain, Hyperledger Fabric, and Corda, evaluated based on criteria such as Transaction Throughput (tps), Security Level (1-10), Energy Efficiency (kWh/tx), and Initial Setup Cost (USD). the term "Weighted Sum Model (WSM)" is correctly spelled, but the term "multi-Criteria" should be "multi-criteria" for proper phrasing. It involves assigning a value to each attribute, determined by the decision maker, which is then multiplied by a specified importance weight Bitcoin, Ethereum, Binance Smart Chain, Hyperledger Fabric and Corda. Transaction Throughput (tps), Security Level (1-10) Energy, Efficiency (kWh/tx) and Initial Setup Cost (USD). the ranking for blockchain-based database systems. Binance Smart Chain) got the top rank, whereas faculty strength Bitcoin) has a low ranking.
The aim of this study is to examine whether the assets known as safe-haven assets during crises fulfill these qualities for equity investors in Turkey during the Covid-19 pandemic. According to the results obtained under the assumption of GJR-GARCH (1,1) error terms, no asset has shown safe-haven characteristics against the stock market. However, when the BIST100 index depreciates by 5%, Ethereum, silver and Government Bonds show strong safe-haven characteristics, US dollar and Euro show weak safe-haven characteristics. When the BIST100 index depreciates by 2.5%, Bitcoin, gold and DJIMTR show weak safe haven asset characteristics. If BIST100 depreciates by 1%, gold and Government Bonds show strong safe-haven characteristics, and Bitcoin, Ethereum, Silver, the US dollar and Euro show weak safe-haven characteristics.
Bank of Italy, Carlo Gola, Guido Befani, Patrizio Fiorenza ¡ 7 authors
The work provides a method for classifying blockchains and crypto-assets, facilitating their comparison for business and regulatory purposes. Blockchains - and more generally systems based on Distributed Ledger Technology (DLT) - adopt different technologic configurations, each distinguished by the activities performed, technical characteristics, and governance structures. Particular noteworthy is the difference between some DLTs, which integrate automated organizational procedures with traditional decision-making processes, and others that adopt entirely algorithmic governance. Equally complex is the task of identifying types of crypto-assets, also known as digital tokens, generated and transferred through this technology. The work outlines the primary characteristics of DLTs and crypto-assets, utilizing âswitching circuitsâ to visually represent or express them in a simple formula. The proposed methodology, applicable both in the EU and in the US, untangles a given DLT/crypto ecosystem and reassembles it through logic maps, facilitating the identification of the technologic, economic, and legal features and overcoming the risk of a rigid and easily outdated taxonomy. The ultimate purpose of the paper is to propose a working method capable of accommodating every possible configuration of DLT and digital token and their potential evolutions. Practical examples of regulatory application and two case studies (Ethereum and Polkadot) are provided
Alaa Haddad, Mohamed Hadi Habaebi, Elfatih A. A. Elsheikh, Md. Rafiqul Islam ¡ 6 authors
To secure sensitive medical records in the healthcare clouds, this paper proposes an End-to-End Encryption (E2EE) to enhance a patient-centric blockchain-based system for electronic health record (EHR) management. The suggested system with a focus on the patient enables individuals to oversee their medical records within various involved parties by authorizing or withdrawing permission for access to their records. Utilizing the inter-planetary file system (IPFS) for record storage is chosen due to its decentralized nature and its ability to guarantee the unchangeability of records. Then an E2EE enhancement maintains the medical data integrity using dual level-Hybrid encryption: symmetric Advanced Encryption Standard (AES) and asymmetric Elliptic Curve Cryptography (ECC) cryptographic techniques. The proposed system is implemented using the Ethereum blockchain system for EHR data sharing and integration utilizing a web-based interface for the patient and all users to initiate the EHR sharing transactions over the IPFS cloud. The proposed system performance is evaluated in a working system prototype. For different file sizes between 512 KB to 100 MB, the performance metrics used to evaluate the proposed system were the time consumed for generating key, encryption, and decryption. The results demonstrate the proposed system's superiority over other cutting-edge systems and its practical ability to share secure health data in cloud environments.
Abstract Bitcoin and Ethereum are the two largest cryptocurrencies in the world by market capitalization and trading volume and the most popular despite high price fluctuations. This paper analyzes the relationship between Bitcoin and Ethereum metrics and the internet search interest on cryptocurrencies. As the literature shows, Google searches signal investor attention and Google Trends has proven useful for nowcasting economic and financial indicators. We aim to find the impact of Google Trends on Bitcoin and Ethereum prices, trading volumes and market capitalization since 2015 and discuss the potential correlations and patterns that may exist between these metrics and Google search interest. Through correlation and time-series analysis, we provide insights into the dynamics of this relationship and its implications for understanding cryptocurrency market behavior. The interest in cryptocurrencies tracked by Google Trends is a good indicator of measuring the social interest in the cryptocurrency market that drives a price movement. On the other hand, the price fluctuations of Bitcoin and Ethereum generate media and social attention and increase the interest in these cryptocurrencies. We also observe a positive effect of Google Trends values on trading volumes. The findings could help investors to understand the cryptocurrencies dynamics and build their trading strategies and could be of special interest to policymakers.
Cryptocurrencies have revolutionized the financial landscape by providing decentralized and anonymous payment systems, making them an intriguing subject for investors and researchers. This article delves into applying machine learning techniques for predicting cryptocurrency prices, mainly focusing on Bitcoin, Ethereum, and Binance Coin. Employing a range of machine learning models, including XGBoost, Linear Regression, and Gaussian Processes, the study aims to evaluate their predictive performance comprehensively. The results are promising; our models outperform existing studies, achieving impressively low RMSE values of 0.0040 for Bitcoin, 0.028 for Ethereum, and 0.027 for Binance Coin. These findings contribute valuable insights into the volatility and dynamics of cryptocurrency prices and underscore the potential of machine learning in shaping financial decision-making. Future directions include integrating advanced deep learning models, additional data sources, and ensemble methods to enhance prediction accuracy and robustness.
Purpose : The burgeoning cryptocurrency market offers an extremely profitable means of generating high-returning investments in a sector that is increasingly in demand. The goal of this research study was to examine the volatility of four cryptocurrencies, Bitcoin, Ethereum, Tether, and BNB, and determine how these fluctuations affected inflation for Indian investors. Methodology : The purpose of this research study was to examine the volatility of four cryptocurrencies, Bitcoin, Ethereum, Tether, and BNB, and to determine how these fluctuations affected inflation for Indian investors (ââŹĹCryptocurrency prices in India Today,ââŹÂ 2023). To analyze volatility margins and determine trading volumes during such periods, data were retrieved using the MS Excel function, and Bollinger Bands were generated using the R console 4.4 open access program. Findings : To analyze volatility margins and determine trade volumes at such times, Bollinger Bands were constructed using R console 4.4 open access software and data for bitcoin transactions was gathered using an MS Excel function. While Tether and BNB showed only mild volatility, Bitcoin and Ethereum were shown to be quite volatile.Practical Implications : The findings of this research paper could prove to be very useful for academicians, investors (existent and prospective), and policymakers in the present and future markets. Originality : The goal of this research was to identify safer cryptocurrencies, which is a major problem rather than just one of utility. This is one of the very few research studies that focused on examining safer cryptocurrencies to buy in the upcoming years.
Internet applications rely on Secure Socket Layer (SSL)/Transport Security Layer (TSL) certifications to establish secure communication. However, the centralized nature of certificate authorities (CAs) poses a risk, as malicious third parties could exploit the CA to issue fake certificates to malicious web servers, potentially compromising the privacy and integrity of user data. In this paper, we demonstrate how the utilization of decentralized certificate verification with blockchain technology can effectively address and mitigate such attacks. We present a decentralized public key infrastructure (PKI) based on a distributed trust model, e.g., Web of Trust (WoT) and blockchain technologies, to overcome vulnerabilities like single points of failure and to prevent tampering with existing certificates. In addition, our infrastructure establishes a trusted key-ring network that decouples the authentication process from CAs in order to enhance secure certificate issuance and accelerate the revocation process. Furthermore, as a proof of concept, we present the implementation of our proposed system in the Ethereum blockchain, confirming that the proposed framework meets the five identified requirements. Our experimental results demonstrate the effectiveness of our proposed system in practice, albeit with additional overhead compared to conventional PKIs.
In the tourism industry, big data has emerged as a revolutionizing the way businesses operate and travelers experience destinations. With the vast amount of data generated from online bookings, social media interactions, and mobile applications, tourism companies can gain valuable insights into traveler preferences, behavior patterns, and market trends. This paper proposes a development strategy for the tourism industry in the post-epidemic situation, leveraging network big data analysis with Multi-Factor Hashing Ethereum Classification (MFH-EC). By harnessing the power of network big data, this strategy aims to provide insights into changing traveler preferences, market dynamics, and risk factors in the wake of the pandemic. The MFH-EC model facilitates the classification and analysis of diverse factors influencing tourism development, including economic indicators, health and safety measures, environmental conditions, and traveler sentiment. Through simulated experiments and empirical validations, the effectiveness of the proposed strategy is assessed, demonstrating significant improvements in predictive accuracy and decision-making capabilities. For instance, the MFH-EC model achieved an 80% accuracy rate in predicting tourism demand shifts and a 30% reduction in forecasting errors compared to traditional methods. These results underscore the potential of network big data analysis with MFH-EC in guiding strategic decision-making and fostering sustainable recovery and growth in the tourism industry post-epidemic.
Abstract: Criminal activities in India are on the rise, with many incidents going unreported. Despite the availability of an online portal for storing the First Information Reports (FIRs) handwritten FIRs always will remain common due to some of the traditional methods and practices. And probably in most cases, the complainants should personally visit the police stations in order to file a offense report. Crime and Criminal Tracking Network and Systems (CCTNS) was launched in 2010 for national wide e-governance, it will operates on a centralized system and it is particularly limited to some individual states. Therefore, there is a need for the decentralized solution in order to ensure failure of a single point and secure managing of criminal complaints from the unauthorized access.
Cryptocurrencies have experienced a surge in popularity, paralleled by an increase in phishing scams exploiting their transactional networks. Therefore, detecting anomalous transactions in the complex structure of cryptocurrency transaction data and the imbalance between legitimate and fraudulent data is considered a very important task. To this end, we introduce a model specifically designed for scam detection within the Ethereum network, focusing on its capability to process long and complex transaction graphs. Our method, Deep Graph traversal based on Transformer for Scam Detection (DGTSD), employs the DeepWalk algorithm to traverse extensive graph structures and a Transformer-based classifier to analyze intricate node relationships within these graphs. The necessity for such an approach arises from the inherent complexity and vastness of Ethereum transaction data, which traditional techniques struggle to process effectively. DGTSD applies subgraph sampling to manage this complexity, targeting significant portions of the network for detailed analysis. Then, it leverages the multi-head attention mechanism of the Transformer model to effectively learn and analyze complex patterns and relationships within the Ethereum transaction graph to identify fraudulent activity more accurately. Our experiments with other models demonstrate the superiority of this model over traditional methods in performance, with an F1 score of 0.9354. By focusing on the challenging aspects of Ethereumâs transaction network, such as its size and intricate connections, DGTSD presents a robust solution for identifying fraudulent activities, significantly contributing to the enhancement of blockchain security.
Block chain technology, non-fungible tokens (NFTs), and cryptocurrency have all contributed to the explosive rise of the digital assets sector. The dynamic and cutting-edge digital asset marketplace shown in this abstract is intended to satisfy the various demands of traders, investors, and enthusiasts in the digital asset market. The marketplace offers a user-friendly and safe platform for purchasing, selling, and managing different digital assets, and it is distinguished by its user-centric approach. It offers real-time market data, order book information, and robust analytical capabilities for well-informed decision-making, all with an intuitive interface that streamlines asset transactions. The ability to move assets seamlessly between blockchain networks, multi factor authentication, cold storage, and complex trading algorithms that optimise trading tactics are some of the key characteristics. ----------------------------------------------------------------------------------------------------------------------------------- Keywords Blockchain, Smart Contract, Ethereum, Transparency, Data sharing, Privacy, Byzantine fault tolerance, decentralized systems.
The decentralised nature of cryptocurrency, coupled with its potential for significant financial returns, has elevated its status as a sought-after investment opportunity on a global scale. Nonetheless, the inherent unpredictability and volatility of the cryptocurrency market present considerable challenges for investors aiming to forecast price movements and secure profitable investments. In response to this challenge, the current investigation was conducted to assess the efficacy of three Machine Learning (ML) algorithms, namely, Gradient Boosting (GB), Random Forest (RF), and Bagging, in predicting the daily closing prices of six major cryptocurrencies, namely, Binance, Bitcoin, Ethereum, Solana, USD, and XRP. The study utilised historical price data spanning from January 1, 2015 to January 26, 2024 for Bitcoin, from January 1, 2018 to January 26, 2024 for Ethereum and XRP, from January 1, 2021 to January 26, 2024 for Solana, and from January 1, 2019 to January 26, 2024 for USD. A novel approach was adopted wherein the lagging prices of the cryptocurrencies were employed as features for prediction, as opposed to the conventional method of using opening, high, and low prices, which are not predictive in nature. The data set was divided into a training set (80%) and a testing set (20%) for the evaluation of the algorithms. The performance of these ML algorithms was systematically compared using a suite of metrics, including R2, adjusted R2, Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The findings revealed that the GB algorithm exhibited superior performance in predicting the prices of Bitcoin and Solana, whereas the RF algorithm demonstrated greater efficacy for Ethereum, USD, and XRP. This comparative analysis underscores the relative advantages of RF over GB and Bagging algorithms in the context of cryptocurrency price prediction. The outcomes of this study not only contribute to the existing body of knowledge on the application of ML algorithms in financial markets but also provide actionable insights for investors navigating the volatile cryptocurrency market.
Santiago MartĂnez, AgustĂn Ameigenda, Braian De Barros, GuzmĂĄn LlambĂas ¡ 6 authors
Zero-knowledge proofs (zkp) have been used to improve several blockchain limitations (e.g. privacy, scalability), and recent work proposed its usage to improve blockchain interoperability solutions in certain scenarios. However, more studies are needed to understand the full potential of zkp in this context. In particular, zkp may improve existing blockchain interoperability solutions, and help software architects and developers to reduce barriers for blockchain adoption. In this paper, we empirically analyse how zkp may improve a gateway-based interoperability solution. The results showed that it was possible to improve the selected solution and incorporate anonymous cross-chain authentication and private data exchange. A prototype was developed and evaluated using three strategies: 1) its application in a use case scenario, 2) performance tests, and 3) cost analysis. The evaluation showed that the approach is technically feasible, but not suitable for every use case. Furthermore, the private data exchange approach confirmed the results of other studies: zkp is not mature enough for some scenarios, and more work needs to be performed.