Oleksandr Kuznetsov, Dzianis Kanonik, Alex Rusnak, Anton Yezhov · 6 authors
The scalability of blockchain technology remains a pivotal challenge, impeding its widespread adoption across various sectors. This study introduces an innovative approach to address this challenge by proposing the adaptive restructuring of Merkle and Verkle trees, fundamental components of blockchain architecture responsible for ensuring data integrity and facilitating efficient verification processes. Unlike traditional static tree structures, our adaptive model dynamically adjusts the configuration of these trees based on usage patterns, significantly reducing the average path length required for verification and, consequently, the computational overhead associated with these processes. Through a comprehensive conceptual framework, we delineate the methodology for adaptive restructuring, encompassing both binary and non-binary tree configurations. This framework is validated through a series of detailed examples, demonstrating the practical feasibility and the efficiency gains achievable with our approach. Moreover, we present a comparative analysis with existing scalability solutions, highlighting the unique advantages of adaptive restructuring in terms of simplicity, security, and efficiency enhancement without introducing additional complexities or dependencies. This study's implications extend beyond theoretical advancements, offering a scalable, secure, and efficient method for blockchain data verification that could facilitate broader adoption of blockchain technology in finance, supply chain management, and beyond. As the blockchain ecosystem continues to evolve, the principles and methodologies outlined herein are poised to contribute significantly to its growth and maturity.
This research aims to examine the influence of cryptocurrencies on stock market prices in Indonesia. This research uses multiple regression analysis using daily tme series data from 2020-2022 so that the number of observations in this research is 1096. The findings in this research show mixed results between cryptocurrency assets and stock market prices in Indonesia. From the research results, Bitcoin does not have a significant influence on stock market prices in Indonesia, while Ethereum and Binance Coin have a positive and significant influence, but this is different from Maker and Pax Gold. Maker and Pax Gold have a negative and significant influence on stock market prices. The findings in this research show that the nature of the influence of cryptocurrency on stock market prices in Indonesia is not the same but depends on the cryptocurrency asset itself. The findings in this research suggest that investors and market players need to consider these two assets together in their investment strategies.
Abstract–An electronic voting system based on blockchain technology, addressing the challenges of traditional paper ballots and digital voting methods. The system aims to provide security, integrity, transparency, and privacy for voters. It evaluates various blockchain frameworks and proposes a novel e-voting system that can be particularly useful for small-scale elections within corporate environments. The implementation relies on Ethereum’s smart contracts, with development and testing facilitated by the Truffle framework and Ganache as the Ethereum client for testing. It also highlights the historical challenges in developing secure electronic voting systems that balance fairness and privacy with transparency and flexibility. The paper explores the application of blockchain technology in addressing these issues, emphasizing the potential of distributed ledger technology for hosting nationwide elections securely and cost-effectively. The paper also underscores the significance of blockchain technology in ensuring anonymity, privacy, verifiability, and fairness in the voting process. Keywords- Blockchain, Decentralized, Ethereum, E-Voting, Smart Contracts.
This study delves into the dynamic landscape of public sentiment surrounding cryptocurrency through a comprehensive social media discourse analysis. Employing the Python Selenium library, data from 1000 public profiles across major platforms—X, Facebook, Instagram, and LinkedIn—were systematically collected. Using advanced text-mining techniques in R Studio, sentiment analysis was conducted with the ‘Syuzhet’ package and word frequency analysis via the ‘tm’ package. The results unveiled a nuanced emotional landscape characterized by dominant sentiments of anticipation and positivity, interwoven with expressions of negativity, notably anger, and loss. Word frequency analysis highlighted vital themes such as established cryptocurrencies (e.g., Bitcoin, Ethereum), blockchain technology, and practical and financial aspects of cryptocurrency usage. The study illuminated technical interest, financial speculation, and reactions to regulatory and economic developments. Offering insights crucial for stakeholders, including investors and policymakers, this research contributes to the academic understanding of public sentiment, emphasizing the volatile nature of crypto-currency markets and the transformative potential of blockchain technology and calls for ongoing monitoring of public sentiment to inform policy, investment, and technological innovation in the ever-evolving cryptocurrency ecosystem.
This article presents an empirical investigation into the determinants of total revenue generated by counterfeit tokens on Uniswap. It offers a detailed overview of the counterfeit token fraud process, along with a systematic summary of characteristics associated with such fraudulent activities observed in Uniswap. The study primarily examines the relationship between revenue from counterfeit token scams and their defining characteristics, and analyzes the influence of market economic factors such as return on market capitalization and price return on Ethereum. Key findings include a significant increase in overall transactions of counterfeit tokens on their first day of fraud, and a rise in upfront fraud costs leading to corresponding increases in revenue. Furthermore, a negative correlation is identified between the total revenue of counterfeit tokens and the volatility of Ethereum market capitalization return, while price return volatility on Ethereum is found to have a positive impact on counterfeit token revenue, albeit requiring further investigation for a comprehensive understanding. Additionally, the number of subscribers for the real token correlates positively with the realized volume of scam tokens, indicating that a larger community following the legitimate token may inadvertently contribute to the visibility and success of counterfeit tokens. Conversely, the number of Telegram subscribers exhibits a negative impact on the realized volume of scam tokens, suggesting that a higher level of scrutiny or awareness within Telegram communities may act as a deterrent to fraudulent activities. Finally, the timing of when the scam token is introduced on the Ethereum blockchain may have a negative impact on its success. Notably, the cumulative amount scammed by only 42 counterfeit tokens amounted to almost 11214 Ether.
Over the last decades, the money market has become more and more complex and diversified bringing about both greater opportunities and risks for investors.Thus, the investment environment has significantly changed lately, and technology has been playing a major role in this conversion.In time cryptocurrencies have been given greater and greater attention by investors and mass-media, due to the significant increase in their prices and their possibility of providing an alternative to traditional coins and investments.This has led to an increase in demand and interest for cryptocurrencies such as Bitcoin, Ethereum and Litecoin, and has made them be one of the most popular and innovating active financial assets at present.The key factors influencing global cryptocurrency market increase are the increasingly greater demand of expertise in business, limited cash-paying systems, increase in payment demand in developing countries, improvement of data security and increase in market size.
Internet of Things (IoT) paves the way for the modern smart industrial applications and cities. Trusted Authority acts as a sole control in monitoring and maintaining the communications between the IoT devices and the infrastructure. The communication between the IoT devices happens from one trusted entity of an area to the other by way of generating security certificates. Establishing trust by way of generating security certificates for the IoT devices in a smart city application can be of high cost and expensive. In order to facilitate this, a secure group authentication scheme that creates trust amongst a group of IoT devices owned by several entities has been proposed. The majority of proposed authentication techniques are made for individual device authentication and are also utilized for group authentication; nevertheless, a unique solution for group authentication is the Dickson polynomial based secure group authentication scheme. The secret keys used in our proposed authentication technique are generated using the Dickson polynomial, which enables the group to authenticate without generating an excessive amount of network traffic overhead. IoT devices' group authentication has made use of the Dickson polynomial. Blockchain technology is employed to enable secure, efficient, and fast data transfer among the unique IoT devices of each group deployed at different places. Also, the proposed secure group authentication scheme developed based on Dickson polynomials is resistant to replay, man-in-the-middle, tampering, side channel and signature forgeries, impersonation, and ephemeral key secret leakage attacks. In order to accomplish this, we have implemented a hardware-based physically unclonable function. Implementation has been carried using python language and deployed and tested on Blockchain using Ethereum Goerli's Testnet framework. Performance analysis has been carried out by choosing various benchmarks and found that the proposed framework outperforms its counterparts through various metrics. Different parameters are also utilized to assess the performance of the proposed blockchain framework and shows that it has better performance in terms of computation, communication, storage and latency.
The study explores an Ethereum blockchain-based data-sharing system for a computer assembly simulation game, emphasizing the relationship between gas limits and transaction speeds.The research integrates smart contracts for secure data storage of scores and player profiles.A significant challenge identified was the complexity of blockchain's variable transaction speeds for the average user.The research investigated how different gas limits affected transaction times, with experiments conducted across three networks.Results show that a gas limit of 200,000 to 300,000 resulted in transaction speeds of approximately 30 seconds.Increasing the gas limit to 400,000 to 500,000 reduced transaction times to 15-30 seconds, while a limit of 600,000 to 700,000 led to speeds below 15 seconds.These findings suggest a direct correlation between higher gas limits and quicker transaction validations.The research concludes that investing in higher gas can significantly reduce transaction times, presenting a trade-off between cost and speed in blockchain data-sharing for educational simulation media.
Open access
Manufacturing Process and Optimization
Additive Manufacturing and 3D Printing Technologies
U ovom radu su istraženi mehanizmi koji osiguravaju i decentraliziraju blok-lanac mreže čiji se konsenzus postiže mehanizmom dokaza o zalogu (engl. Proof of Stake). Dan je pregled različitih načina na koji korisnici mogu sudjelovati validaciji mreže. Predstav- ljene su prednosti za korisnike koji sudjeluju, no i mane koje se pojavljuju prisutnošću većeg broja transakcija na mreži u jedinici vremena. Objašnjen je predložen pristup koji povezuje tehnologije skaliranja blok-lanac mreža i validacije uz analizu rezultata i pri- jedloge poboljšanja te decentralizirane aplikacije.
Abstract Counterfeit artwork presents a significant risk to copyright holders and the economy. Without expertise in art, it is not straightforward to distinguish an artwork counterfeit from a genuine piece. This work designs and implements a reliable solution that enables users with near‐field communication‐enabled Android smartphones to verify artwork authenticity by accessing its respective certificate of authenticity stored in the Ethereum blockchain. To represent a physical artwork, the artwork image and metadata are stored in an inter‐planetary file system and minted as an ERC721 non‐fungible token to a smart contract deployed in Ethereum Rinkeby Testnet. The mobile app ArtProtect was developed to generate certificate of authenticity based on each non‐fungible token fetched through OpenSea Testnet API. Users access this information by scanning an near‐field communication tag embedded into the artwork. The content of the tag is signed by the respective artwork's artist and responsible agent by using Ethereum signing with their Ethereum wallet accounts. Through testing and analysis, the implemented work is secure, tamper‐evident, usable, flexible, and inexpensive to be applied to a real‐world scenario.
Open access
User Authentication and Security Systems
Innovative Human-Technology Interaction
Advanced Steganography and Watermarking Techniques
Battula Venkata Satish Babu, Kare Suresh Babu, Durga Prasad Kare
The secure access and reliable access revocation methods of modern digital systems are based on access control mechanisms.Access policies, which are used in access control mechanisms, are very important in safeguarding security and ensuring data protection.It is evident that the protection and tamper-proofing of such policies are very important.In addition, efficient access revocation schemes are required to promptly remove access privileges when users are no longer needed or authorized.The shortcomings of existing systems in ensuring efficient, streamlined access revocation and tamper-proof protection of access control policies underscore the need for innovative solutions.In this paper, we have introduced the novel Blockchain Attribute-Based Secure Data Management Model (BAB-SDMM).Our model is the first to integrate attribute-based encryption (ABE), Attribute-Based Access Control (ABAC), and blockchain to achieve multiple security features as well as provide partial and complete revocation at the same time.The experimental results and analysis, performed using the Ethereum blockchain network, demonstrated the enhanced performance of the proposed BAB-SDMM compared to existing research works.
Considering that cryptocurrencies are now present in practically every financial transaction because they are widely accepted as an alternate means of making payments and exchanging currencies, academics and economists have more opportunities to study cryptocurrency prices. Over the years, investors, traders and investment banks have found it difficult to predict the closing daily price of Ethereum due to its rapid price fluctuation. The daily closing price of cryptocurrency is essential to consider when trading or investing in Ethereum. This report focuses on carrying out a comparative study of the predictive capabilities of deep machine learning algorithms with a stacking ensemble modelling framework using daily historical observations of the price of Ethereum obtained from Coindesk, tweets extracted from Twitter ranging from the 1st of August 2022 to the 8th of August 2022 and other five covariates (closing price lag1, closing price lag2, noltrend, daytype and month) engineered from the closing price of Ethereum. Seven models are used to compute the forecasts for the daily closing price of Ethereum; these are the recurrent neural network, ensemble stacked recurrent neural network, gradient boosting machine, generalized linear model, distributed random forest, deep neural networks and stacked ensemble for gradient boosting machine, generalized linear model, distributed random forest and deep neural networks. The main evaluation metric used is the mean absolute error. According to MAE, RNN forecasts outperform the other model’s forecasts in this study, producing an MAE of 0.0309.
Staking has emerged as a crucial concept following Ethereum’s transition to Proof-of-Stake consensus. The introduction of Liquid Staking Derivatives (LSDs) has effectively addressed the illiquidity issue associated with solo staking, gaining significant market attention. This paper analyzes the LSD market dynamics from the perspectives of both liquidity takers (LTs) and liquidity providers (LPs). We first quantify the price discrepancy between the LSD primary and secondary markets. Then we investigate and empirically measure how LTs can leverage such discrepancy to exploit arbitrage opportunities, unveiling the potential barriers to LSD arbitrages. In addition, we evaluate the financial profit and losses experienced by LPs who supply LSDs for liquidity provision. Our results show that 66% of LSD liquidity positions generate returns lower than those from simply holding the corresponding LSDs.
Le nombre croissant de transactions sur le réseau Ethereum et les transactions numériques, y compris dans le métaverse, entraînera certainement des litiges. La question sous-jacente est de savoir comment ces nouveaux différends seront résolus. Cet article examine comment l’intelligence artificielle (IA) ou l’arbitrage participatif blockchain permettront de le faire. Il aborde, entre autres, la manière dont l’arbitrage international s’adaptera à ces technologies. L’IA et la blockchain, bien qu’interdépendantes, pourraient cependant être liées dans le cadre du règlement des litiges. L’article étudiera, également, comment ces deux technologies pourraient représenter des opportunités ou des avantages pour l’arbitrage dans ce nouvel espace technologique. Il se demandera si l’IA et l’arbitrage participatif blockchain résistent aux défis évoqués, ou si des modifications devront être apportées pour répondre aux besoins et assurer un respect des principes fondamentaux. L’article étudiera dans un premier temps les opportunités et les avantages de l’IA et l’arbitrage participatif blockchain et abordera, dans une deuxième partie, les défauts et les défis associés à l’IA et l’arbitrage participatif blockchain.
Le nombre croissant de transactions sur le réseau Ethereum et les transactions numériques, y compris dans le métaverse, entraînera certainement des litiges. La question sous-jacente est de savoir comment ces nouveaux différends seront résolus. Cet article examine comment l’intelligence artificielle (IA) ou l’arbitrage participatif blockchain permettront de le faire. Il aborde, entre autres, la manière dont l’arbitrage international s’adaptera à ces technologies. L’IA et la blockchain, bien qu’interdépendantes, pourraient cependant être liées dans le cadre du règlement des litiges. L’article étudiera, également, comment ces deux technologies pourraient représenter des opportunités ou des avantages pour l’arbitrage dans ce nouvel espace technologique. Il se demandera si l’IA et l’arbitrage participatif blockchain résistent aux défis évoqués, ou si des modifications devront être apportées pour répondre aux besoins et assurer un respect des principes fondamentaux. L’article étudiera dans un premier temps les opportunités et les avantages de l’IA et l’arbitrage participatif blockchain et abordera, dans une deuxième partie, les défauts et les défis associés à l’IA et l’arbitrage participatif blockchain.
Abstract Predictive demand forecasting plays a pivotal role in optimizing supply chain management, enabling businesses to effectively allocate resources and minimize operational inefficiencies. This paper introduces a novel approach to enhancing demand forecasting processes by leveraging the Ethereum virtual machine within a blockchain framework. The proposed system capitalizes on the inherent security, transparency, and decentralized nature of blockchain technology to create a secure and efficient platform for predictive demand forecasting. The system leverages the Ethereum virtual machine to establish a secure, decentralized, and tamper‐resistant platform for demand prediction while ensuring data integrity and privacy. By utilizing the capabilities of smart contracts and decentralized applications within the Ethereum ecosystem, the proposed system offers an efficient and transparent solution for demand forecasting challenges. The current research focused on Ethereum virtual machine characteristics, features, components, and implementation details. A secured framework for the prediction of demand forecasting systems is proposed. Finally, the authors tried to validate and optimize the gas cost by using distinguished statistics and analysis.
Sana Hafeez, Mahmoud A. Shawky, Mohammad Al-Quraan, Lina Mohjazi · 6 authors
Unmanned aerial vehicles (UAV), an emerging architecture that embodies flying ad-hoc networks, face critical privacy and security challenges, mainly when engaged in data-sensitive missions. Therefore, message authentication is a crucial security feature in drone communications. This paper presents a Blockchain-based Efficient, and Trusted Authentication scheme for UAV communication, BETA-UAV, which exploits the inherent properties of blockchain technology concerning memorability and is immutable to record communication sessions via transactions using a smart contract. The smart contract in BETA-UAV allows participants to publish and call transactions from the blockchain network. Furthermore, transaction addresses are proof of freshness and trustworthiness for subsequent transmissions. Furthermore, we investigated their ability to resist active attacks, such as impersonation, replaying, and modification. In addition, we evaluate the gas costs associated with the functions of the smart contract by implementing a BETA-UAV on the Ethereum public blockchain. A comparison of the computation and communication overheads shows that the proposed approach can save significant costs over traditional techniques.
Virtual Currency, Digital Currency, Crypto Asset, DLT(Distributed Ledger Technology) , Payment tokens, Virtual Asset, Bitcoin are some of the terms commonly used to denote the crypto currency without any standard common definition. These are all types of crypto assets developed initially in 2009. Bitcoin, Ethereum are some of the widely used crypto currencies. All around the world some countries have regularised this field, some are yet to regularise. The entire event is based on the concept of decentralisation or no need of a Central organ to control or monitor such currencies. The various articles published with different ideas regarding the taxability of such cryptocurrencies are analysed .
Edgar Roberto Dulce Villarreal, Julio Ariel Hurtado Alegría, Eduard Antonio Mantilla Torres, Yenny Stella Núñez Álvarez · 5 authors
La tecnología Blockchain está creciendo a un ritmo acelerado en diferentes entornos. Los contratos inteligentes (SC) son programas descentralizados inmutables para plataformas Blockchain que hacen cumplir, monitorear y ejecutar acuerdos, sin la intervención de un tercero de confianza. Pero, debido a sus especificidades, su desarrollo es un proceso complicado, ya que existen restricciones arquitectónicas de cada plataforma, que los desarrolladores deben comprender. En este trabajo, presentamos una herramienta Model Driven Engineering destinada a la generación de SC para la plataforma de Blockchain Ethereum, para el lenguaje de programación Solidity. Esta herramienta está compuesta de un Metamodelo Especifico de la Plataforma y una Transformación de Modelo a Texto, que permiten generar el código fuente de los SC. También, presentamos una prueba de concepto donde generamos e implementamos un metamodelo, un modelo y desplegamos SC en un entorno sanitario. Los resultados son satisfactorios en cuanto a la sintaxis de los SC generados.
Muhammad Ather Rafi, S M Iftekhar Shaboj, Iftekhar Rasul, Md Sipon Miah · 7 authors
Cryptocurrencies have emerged as highly dynamic digital assets, characterized by extreme price volatility and driven by both speculative behavior and network-level activities. Traditional volatility forecasting methods, including GARCH and LSTM-based models, often fall short in capturing the complex, nonlinear, and temporal dependencies inherent in crypto markets. This paper proposes a novel deep learning framework that leverages Transformer-based architectures originally designed for natural language processing to forecast short-term cryptocurrency volatility with enhanced accuracy and temporal sensitivity. By incorporating a comprehensive set of on-chain metrics (such as transaction volume, wallet activity, miner behavior, and token circulation), our model captures both market sentiment and blockchain-level dynamics. The proposed Transformer model is benchmarked against LSTM and GRU networks using Bitcoin and Ethereum datasets spanning multiple market cycles. Experimental results show that the Transformer-based model outperforms recurrent architectures in predicting both realized and implied volatility, particularly during high-turbulence periods. These findings suggest that attention mechanisms, combined with on-chain data, provide a powerful tool for managing risk and making informed decisions in the rapidly evolving digital asset ecosystem.
Amro Saleem Alamaren, Korhan K. Gökmenoğlu, Nigar Taşpınar
Abstract This study investigates volatility spillovers and network connectedness among four cryptocurrencies (Bitcoin, Ethereum, Tether, and BNB coin), four energy companies (Exxon Mobil, Chevron, ConocoPhillips, and Nextera Energy), and four mega-technology companies (Apple, Microsoft, Alphabet, and Amazon) in the US. We analyze data for the period November 15, 2017–October 28, 2022 using methodologies in Diebold and Yilmaz (Int J Forecast 28(1):57–66, 2012) and Baruník and Křehlík (J Financ Economet 16(2):271–296 2018). Our analysis shows the COVID-19 pandemic amplified volatility spillovers, thereby intensifying the impact of financial contagion between markets. This finding indicates the impact of the pandemic on the US economy heightened risk transmission across markets. Moreover, we show that Bitcoin, Ethereum, Chevron, ConocoPhilips, Apple, and Microsoft are net volatility transmitters, while Tether, BNB, Exxon Mobil, Nextera Energy, Alphabet, and Amazon are net receivers Our results suggest that short-term volatility spillovers outweigh medium- and long-term spillovers, and that investors should be more concerned about short-term repercussions because they do not have enough time to act quickly to protect themselves from market risks when the US market is affected. Furthermore, in contrast to short-term dynamics, longer term patterns display superior hedging efficiency. The net-pairwise directional spillovers show that Alphabet and Amazon are the highest shock transmitters to other companies. The findings in this study have implications for both investors and policymakers.
Purpose: This paper aims to develop a coffee supply chain traceability system based on Blockchain (BC) and Machine Learning (ML) with the aim of ensuring the quality of coffee beans production. BC functions to ensure supply chain performance, while the ML model ensures product quality.Design/methodology/approach: Smart Contracts will be built on the Ethereum Virtual Machine BC network based on Ethereum. The ML model to identify good and bad green coffee beans will be built using different YOLO algorithms, which will go through training and validation stages, namely using the k-fold cross validation method. The ML model algorithm is based on Convolutional Neural Network (CNN) using YOLOv5m, YOLOv6m and YOLOv7. The best model will be chosen based on the results of cross-validation with test data in the form of coffee image data that the model has never seen (unseen data). The whole process of building the ML model is done on the Google Collab Pro+ Virtual Machine.Findings: YOLOv5m outperformed the other models in both non-augmented and augmented training datasets, highlighting the proficiency of YOLOv5m in managing compact datasets and its resilience in the face of data augmentation, positioning it as a prime selection for quality discernment tasks within the realm of green coffee beans. The smart contracts offer an all-encompassing approach for user management, monitoring product status, and presenting traceability data within the framework of coffee plantation administration.Originality/value: This research contributes to the development of blockchain network as a solution to implement traceability systems along coffee supply chains in Indonesia. Moreover, it shows that while blockchain can ensure the process of production along the coffee supply to follow certain guidelines, machine learning can verify whether the product that was produced by utilizing BC is of high quality/acceptable.
The secondary market for Ethereum non-fungible tokens (NFTs) has resulted in over $1.8bn being paid to creators in the form of a sales tax commonly called creator royalties. This was despite royalty payments being enforced by no more than social contract alone. Predictably, such an incentive structure led to zero-royalty alternatives becoming abundant and payments dwindled. A purely programmatic solution to royalty enforcement is hampered by the prevailing NFT standard, ERC-721, which is ignorant of sale values and royalty enforcement therefore relies on (potentially dishonest) third parties. We thus introduce an incentive-compatible mechanism for which there is a single rationalisable solution, in which royalties are paid in full, while maintaining full ERC-721 compatibility. The mechanism constitutes the core of ERC-7526.
Digital currencies and coins are methods of computer-generated currency which uses cryptography for safety and operate individually of a dominant authority, like governments and economic institution. They are spread out and usually utilize blockchain technology to note transactions strongly. Cryptocurrencies such as Bitcoins, Ethereum, and some others have grown popularity in the latest eons for their latent to deliver borderless, secure, and fast transactions. Yet, they also arisen with threats like security concerns, regulatory uncertainty, and price volatility. It is vital for operators to conduct detailed research and comprehend the threats included before capitalizing or utilizing cryptocurrencies. Even though cryptocurrencies and coins have grown famous and are being gradually used for countless transactions, it is significant to remind that they still have not passed the traditional banking systems. Traditional banking sectors still perform an important part in the worldwide economic system, offering services like payment processing, savings accounts, and lending. Conversely, the growth of cryptocurrencies has directed to conferences about the possible influence on the financing sector and the necessity for traditional banking systems to acclimate to the varying setting of digital economics. It is important to observe these growths closely to comprehend the evolving association among the crypto-currencies and system of traditional banking. Hence, the current study gives the deep knowledge in the usage of crypto currencies, digital coins and their role in financial sectors that is dominating the traditional banking sector. And analyzed the impact of crypto currencies and digital coins on investors and economy of the nation and also regarding the easy accessibility of finance.