Since Bitcoin was launched, the blockchain technology and the cryptocurrencies have been in the spotlight. Ethereum, the second-generation blockchain introduced smart contracts, and many DApps have emerged due to them. Those DApps showed the feasibility of blockchain in various industries. However, even though the growth of blockchain technology, still many DApps are based on Ethereum, and supper its performance issue. Since the performance evaluation in Ethereum mainnet is almost impossible, and there are no formalized performance evaluation frameworks, it is hard to perform appropriate performance evaluation of Ethereum. Detail performance evaluations on Ethereum networks are essential for developing and operating DApps. In this paper, we use Hyperledger Caliper, an automated performance evaluation framework to evaluate an Ethereum private network, and the Ropsten testnet to overcome above problems. We evaluate the performance with a specific smart contract and analyze the results. Our evaluation results show that the Ethereum private network performs better than the Ropsten testnet, and the Ropsten testnet is unstable for performance evaluation. In addition, our results show that the performance of the transactions can differ following their content.
Blockchains are gaining momentum due to the interest of industries and people in \emph{decentralized applications} (Dapps), particularly in those for trading assets through digital certificates secured on blockchain, called tokens. As a consequence, providing a clear unambiguous description of any activities carried out on blockchains has become crucial, and we feel the urgency to achieve that description at least for trading. This paper reports on how to leverage the \emph{Ontology for Agents, Systems, and Integration of Services} ("\ONT{}") as a general means for the semantic representation of smart contracts stored on blockchain as software agents. Special attention is paid to non-fungible tokens (NFTs), whose management through the ERC721 standard is presented as a case study.
Summary In order to ensure best health care experience for the user, the user should be able to access or grant access to his medical information as the requirement arises. Currently, when the data are residing in hospital proprietary Electronic Health Record (EHR), this is not possible. Hence there is a requirement for patient operated PHR where the details of the treatment of patient is with the patient. This paper proposes a blockchain‐based, secure interoperable patient‐centric data access management system—MediTrans. In the proposed model, the patient owns his/her treatment related data and stores it in a secure personal health record (PHR) cloud. The tool provides technique for secure transfer of patient related data from Hospital EHR to patient PHR Cloud. Patient can give controlled access of patient's medical data to Health Care Providers for a specific period of time. MediTrans proposes secure access management of data stored in PHR cloud to authorised person via Blockchain Technology and Ciphertext policy attribute‐based encryption technique. The proposed design was evaluated by developing a prototype on Ethereum Blockchain with OpenEMR as the healthcare system. Performance analysis was carried out by varying the size of the data and the number of users. The results obtained were promising, which gives clear insights on the feasibility of the approach taken.
Blockchains are gaining momentum due to the interest of industries and people in \emph{decentralized applications} (Dapps), particularly in those for trading assets through digital certificates secured on blockchain, called tokens. As a consequence, providing a clear unambiguous description of any activities carried out on blockchains has become crucial, and we feel the urgency to achieve that description at least for trading. This paper reports on how to leverage the \emph{Ontology for Agents, Systems, and Integration of Services} ("\ONT{}") as a general means for the semantic representation of smart contracts stored on blockchain as software agents. Special attention is paid to non-fungible tokens (NFTs), whose management through the ERC721 standard is presented as a case study.
Blockchain is a digital technology built on three pillars: decentralization, transparency and immutability. Bitcoin and Ethereum are two prevalent Blockchain platforms, where the participants are globally connected in a peer-to-peer manner and anonymously perform trade electronically. The vast number of decentralized transactions and the pseudo-anonymity of participants open the door for scams, cyber frauds, hacks, money laundering and fraudulent transactions. It is challenging to detect such fraudulent activities using traditional auditing techniques, since they need more processing power, time and memory for complex queries to join combinations of tables. This paper proposes several algorithms to extract the transaction- related features from the Bitcoin and Ethereum networks and to represent the features as graphs. Moreover, the paper discusses how visualisation of graphs can reflect the anomalies and patterns of fraudulent activities.
In the era of the fourth industrial revolution, all aspects of the industrial domain are being affected by emerging technologies. Digitalization of every process is taking place or under process. One of the most important components common to every domain is the supply chain process. Organizations employ a digital supply chain to track the delivery of their products or materials. The digital supply chain is still suffering from a few issues such as no provenance, less transparency, and a trust issue. Blockchain technology, one of the emerging technologies, can be integrated with the supply chain to deal with the existing issues and to improve its performance. In this paper, a model is proposed to integrate blockchain technology with the supply chain to improve performance. The proposed model uses the combination of the Ethereum blockchain and the interplanetary file system to maintain the traceability, transparency, and trustworthiness of the supply chain.
Due to the transparency, simplicity, and blockchain system, cryptocurrencies gained popularity in the modern world. This led to more use of cryptocurrencies for speculation and investment rather than a medium of exchange. It is crucial to analyse the nature of the crypto market before investing in such currencies. With this intention, the paper tried to know the extent of following (Followness) of altcoins to the bitcoin in the different dominance phases like High Dominance, Low Dominance, and Moderate Dominance. For this purpose, daily closing prices of the Bitcoin and five major altcoins (Ethereum, Litecoin, Namecoin, Doge, and Ripple) are collected for the last five years and analyse the relationship between bitcoin and altcoins. Pearson's correlation coefficient test is used to know the direction of the relationship, and Vector Error Correction Model is used to see the extent of the relation. In general, the empirical result of the study showed cointegration between bitcoin and Altcoin. It also depicted that Altcoin showed a high level of followness in the moderate dominance phase and low followness in the low dominance phase. The study developed a price estimation equation to predict the price of altcoins depending upon the price of bitcoin and its dominance in the crypto market. This paper concludes that the dominance of Bitcoin also has a significant role in the price movement of altcoins.
This paper is a review of some real-time issues associated with the development of Ethereum smart contracts like out of gas exception and gas inefficient code patterns and focuses on the methods and solutions presented in recent years. With the help of this paper, we are trying to summarize the methods which can be used in future researches for gas prediction with the help of old transaction data and machine learning algorithms and have a look at old researches which are trying to predict with the help of regression algorithms and their efficiency. KEYWORDS: Blockchain, Etherium, Gas Consumption, Gas Prediction, Transaction
Public Key Infrastructure (PKI) is the fundamental of secure digital communications. It provides a secure means to authenticate identities over the Internet. Symmetric or asymmetric encryption schemes are widely used in identity authentication in any kind of PKI. The conventional PKI has several drawbacks due to the centralized and non-transparent design. Several recent research works utilize blockchain technology to overcome the limitations of conventional implementations of PKI. Blockchain-based PKI integrates blockchain technology with PKI to form a new type of decentralized PKI (DPKI). Several works utilize the currency property in blockchains to implement the reward-and-punishment mechanism. In this paper, we propose a smart contract-based PKI which utilizes the Ethereum smart contract to build a new type of blockchain-based PKI with the reward-and-punishment mechanism using ERC-20 tokens. It has several advantages over previous implementations of similar research that use Ethereum’s main currency—Ether.
Smart contracts, scripts at the heart of blockchain-based applications, are meant to be available forever once deployed. However, this property has a price. The amount of space required to store new contracts keeps increasing. This increase impacts each participating node's performance and makes it inconvenient for low-end devices to participate in the network. Among all contracts deployed in the blockchain, a vast majority will lead to little if any usage. We demonstrate that, in the course of one year, 70 % of deployed contracts lead to no use. Unfortunately, unused contracts keep occupying space on the blockchain. To tackle this issue, we propose a new protocol to identify and delete unused contracts. Through simulation, based on Ethereum historical data, we show that deletion of smart contracts after an inactivity period of 90 days could lead to a 66 % reduction in the number of contracts stored over a year.
The development of information and communication technology in terms of sensor technologies cause the Internet of Things (IoT) step toward smart homes for prevalent sensing and management of resources. The gateway connections contain various IoT devices in smart homes representing the security based on the centralized structure. To address the security purposes in this system, the blockchain framework is considered a smart home gateway to overcome the possible attacks and apply Deep Reinforcement Learning (DRL). The proposed blockchain-based smart home approach carefully evaluated the reliability and security in terms of accessibility, privacy, and integrity. To overcome traditional centralized architecture, blockchain is employed in the data store and exchange blocks. The data integrity inside and outside of the smart home cause the ability of network members to authenticate. The presented network implemented in the Ethereum blockchain, and the measurements are in terms of security, response time, and accuracy. The experimental results show that the proposed solution contains a better outperform than recent existing works. DRL is a learning-based algorithm which has the most effective aspects of the proposed approach to improve the performance of system based on the right values and combining with blockchain in terms of security of smart home based on the smart devices to overcome sharing and hacking the privacy. We have compared our proposed system with the other state-of-the-art and test this system in two types of datasets as NSL-KDD and KDD-CUP-99. DRL with an accuracy of 96.9% performs higher and has a stronger output compared with Artificial Neural Networks with an accuracy of 80.05% in the second stage, which contains 16% differences in terms of improving the accuracy of smart homes.
Francesco Mogavero, Ivan Visconti, Andrea Vitaletti, Marco Zecchini
Ethereum's founder Buterin raised the challenge of solving the blockchain Trilemma towards a decentralized computer that could together achieve high degrees of security, scalability, and decentralization. Later on, Algorand claimed to have resolved Buterin's blockchain Trilemma and is nowadays increasingly adopted by designers of decentralized computations. Motivated by the need of selecting a blockchain to run some decentralized computations, we observe the limitations of using the Trilemma as benchmark, and we propose as alternative a Quadrilemma that takes into account also computational effectiveness, namely the capability of running non-trivial decentralized computations at affordable costs. For concreteness, motivated by the current trends of using blockchains for the management of non-fungible tokens (NFTs) related to highly desired items (i.e., NFTs for art), we consider the use case of decentralized auctions in various scenarios that mainly differ on the desired degree of confidentiality. Our contribution gives the following insights. Except very limited cases where also Bitcoin can be taken into account (i.e., when latency is not a big deal and only notarization is required), Algorand can often be the right choice as long as decentralized computations consist of basic operations only. Instead, Ethereum is advisable when more sophisticated computations are required, in particular when ad-hoc cryptographic tasks are essential, and one can afford the involved costs and latency. Focusing on those three blockchains, the state of affairs about resolving the blockchain Quadrilemma is somewhat unsatisfying. Even in natural cases where computations and storage requirements for a smart contract are low (e.g., public-key encryption), none of those decentralized computers achieves simultaneously low cost and fast transaction confirmations.
Blockchain Technology Applications and Security
Cryptography and Data Security
Advanced Steganography and Watermarking Techniques
Francesco Bruschi, Tommaso Paulon, Vincenzo Rana, Donatella Sciuto
If on one hand the possibility of using pseudonymous identities is an important feature of blockchains and smart contracts, on the other hand official identity can be required in some applications to comply with regulations such as Know Your Customer and Anti Money Laundering. These regulatory compliance issues are usually dealt with either through “custo-dial” approaches, which however neutralize the decentralization of these systems, or with “whitelisting” mechanisms, that present critical issues with regard to privacy. In this paper we propose a protocol that allows only potentially identifiable users to use a given decentralized applications. The system ensures that users are identifiable only by a competent authority, under certain conditions, and that in any other case they remain pseudonyms. To evaluate performance and costs, we present an Ethereum implementation of the protocol.
This paper initially presents a nontechnical overview of cryptocurrency, its history, and the technicalities of its usage as a means of exchange. Bitcoin’s working methodology and mathematical baseline is further presented in more depth. For the remaining majority of the paper, recent cryptocurrency price data of Bitcoin, Ethereum, Tether, Dogecoin, and Binance coin was used to train a machine learning model of Feed Forward Neural Networks to predict future prices for each of the datasets. Further and in conclusion, the results are discussed, and the efficiency and accuracy of these models are evaluated.
Hanlin Zhang, Peng Gao, Jia Yu, Jie Lin · 5 authors
Linear Regression (LR) is a classical machine learning algorithm which has many applications in the cyber physical social systems (CPSS) to shape and simplify the way we live, work, and communicate. This paper focuses on the data analysis for CPSS when the Linear Regression is applied. The training process of LR is time-consuming since it involves complex matrix operations, especially when it gets a large scale training dataset In the CPSS. Thus, how to enable devices to efficiently perform the training process of the Linear Regression is of significant importance. To address this issue, in this paper, we present a secure, verifiable and fair approach to outsource LR to an untrustworthy cloud-server. In the proposed scheme, computation inputs/outputs are obscured so that the privacy of sensitive information is protected against cloud-server. Meanwhile, computation result from cloud-server is verifiable. Also, fairness is guaranteed by the blockchain, which ensures that the cloud gets paid only if he correctly performed the outsourced workload. Based on the presented approach, we exploited the fair, secure outsourcing system on the Ethereum blockchain. We analysed our presented scheme on theoretical and experimental, all of which indicate that the presented scheme is valid, secure and efficient.
Blockchain is a reliable and innovative technology that harnesses education and training through digital technologies. Nonetheless, it has been still an issue keeping track of student/graduate academic achievement and blockchain access rights management. Detailed information about academic performance within a certain period (semester) is not present in the official education documents. Furthermore, academic achievement documents issued by institutions are not secured against unauthorized changes due to the involvement of intermediaries. Therefore, verification of official educational documents has become a pressing issue owing to the recent development of digital technologies. However, effective tools to accelerate the verification are rare as the process takes time. This study provides a prototype of the UniverCert platform based on a consortium version of the decentralized, open-source Ethereum blockchain technology. The proposed platform is based on a globally distributed peer-to-peer network that allows educational institutions to partner with the blockchain network, track student data, verify academic performance, and share documents with other stakeholders. The UniverCert platform was developed on a consortium blockchain architecture to address the problems universities face in storing and securing student data. The system provides a solution to facilitate students’ registration, verification, and authenticity of educational documents.
The purposes are to investigate how blockchain can solve the security problems in Intelligent Autonomous Transport System (IATS) and intelligentize the logistics transportation development. Regarding the scarcity of trust and concentration of rights caused by the centralized structure of traditional logistics information systems, a blockchain-based IATS is proposed. The system employs Ethereum as the underlying blockchain to record sensitive information, such as system orders, cargos, and personnel information on the blockchain, ensuring the non-tampering and credibility of data. Simultaneously, an order management module, a warehouse management module, a transportation management module, a transaction management module, and a system management module are established. In the meantime, the Light Gradient Boosting Machine (LightGBM) algorithm is utilized to recommend vehicle and cargo matching during transportation. Finally, the constructed algorithm model is simulated to analyze its performance. Results demonstrate that the security prediction accuracy of the proposed algorithm reaches 88.72%; moreover, the security prediction precision, recall, and F1 of the proposed algorithm are considerably better than those of other algorithms. Furthermore, the actual effect of each algorithm is analyzed. The LightGBM algorithm outperforms other algorithms and unused algorithms in click rate, conversion rate, turnover rate, and average response time. Therefore, the constructed blockchain-based IATS has excellent security performance and prediction accuracy, which provides an experimental basis for the later intelligent logistics transportation development.
Fahad Mostafa, Pritam Saha, Mohammad Rafiqul Islam, Nguyet Nguyen
Cryptocurrencies are currently traded worldwide, with hundreds of different currencies in existence and even more on the way. This study implements some statistical and machine learning approaches for cryptocurrency investments. First, we implement GJR-GARCH over the GARCH model to estimate the volatility of ten popular cryptocurrencies based on market capitalization: Bitcoin, Bitcoin Cash, Bitcoin SV, Chainlink, EOS, Ethereum, Litecoin, TETHER, Tezos, and XRP. Then, we use Monte Carlo simulations to generate the conditional variance of the cryptocurrencies using the GJR-GARCH model, and calculate the value at risk (VaR) of the simulations. We also estimate the tail-risk using VaR backtesting. Finally, we use an artificial neural network (ANN) for predicting the prices of the ten cryptocurrencies. The graphical analysis and mean square errors (MSEs) from the ANN models confirmed that the predicted prices are close to the market prices. For some cryptocurrencies, the ANN models perform better than traditional ARIMA models.
The introduction of smart contracts and support for Dapps in blockchain networks has opened a new era of decentralized application development for different domains. This paper provides insight into the blockchain technology's core components and highlights the differences in the underlying techniques used in the Ethereum and TRON networks. Additionally, it presents a quantitative analysis of both platforms. Therefore, it will help the readers to balance off their choice between these two blockchain networks.
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Abstract Next to cryptocurrencies, tokens are a widespread application area of blockchains. Tokens are digital assets implemented as small programs on a blockchain. Being programmable makes them versatile and an innovative means for various purposes. Tokens can be used as investment, as a local currency in a decentralized application, or as a tool for building an ecosystem or a community. A high-level categorization of tokens differentiates between payment, security, and utility tokens. In most jurisdictions, security tokens are regulated, and hence, the distinction is of relevance. In this work, we discuss the identification of tokens on Ethereum, the most widely used token platform. The programs on Ethereum are called smart contracts, which—for the sake of interoperability—may provide standardized interfaces. In our approach, we evaluate the publicly available transaction data by first reconstructing interfaces in the low-level code of the smart contracts. Then, we not only check the compliance of a smart contract with an established interface standard for tokens, but also aim at identifying tokens that are not fully compliant. Thus, we discuss various heuristics for token identification in combination with possible definitions of a token. More specifically, we propose indicators for tokens and evaluate them on a large set of token and non-token contracts. Finally, we present first steps toward an automated classification of tokens regarding their purpose.
Increased global competition and complex patterns of customer demand have made supply chain management (SCM) extremely challenging for any company. Traditional SCM systems with centralized data storage architecture lead to data access and privacy challenges across the diverse spectrum of stakeholders in supply chain management. Blockchain alleviates the problem of data access and privacy but the task of developing an SCM solution over a blockchain from scratch is challenging, complex, and time-consuming. In this paper, we present our Nocode Development Platform (NCDP) that allows users to build customized SCM applications using a user-friendly interface. Once a customized supply chain is built, our platform autogenerates and deploys the smart contract on the Ethereum blockchain, thus easing the deployment of an SCM solution on the blockchain. A product can be tracked at any point in the supply chain using different tracking mechanisms. Our platform leverages the blockchain concept to provide supply chain data transparency in a secure manner to diverse stakeholders in a decentralized ecosystem. Our platform significantly reduces the time and investment to migrate existing SCM systems on the blockchain.