Comprendre la consommation énergétique des blockchains : un regard sur les contrats intelligents Les systèmes de chaînes de blocs sont des registres répliqués dans un réseau pair à pair. Elles ont connu un développement rapide depuis quelques années en s'illustrant dans de nombreux domaines d'activités. En permettant le traitement et la sauvegarde de données dans un contexte distribué et Byzantin, ces technologies ont le potentiel de modifier de nombreux secteurs. Par exemple, dans le cadre de la finance décentralisée, les cryptomonnaies se développement comme une alternative aux monnaies fiduciaires en proposant un système de paiement dépourvu de tiers de confiance. Cependant, une certaine inquiétude vis-à-vis de l’impact environnemental des chaînes de blocs a émergé en parallèle de leur développement. En particulier, de nombreuses recherches ont démontré le coût énergétique important des chaînes basées sur les preuves de travail. Dans cette thèse, nous proposons de contribuer à l'étude expérimentale du coût énergétique des solutions logicielles basées sur les chaînes de blocs. Face à l'enrichissement progressif de l'écosystème lié aux chaînes de blocs, nous proposons BCTMark, un nouvel outil de déploiement et d'évaluation des performances des chaînes de blocs. Partant de cet outil, nous concentrons notre étude sur l'impact des contrats intelligents sur la chaîne de blocs Ethereum. D'une part, nous proposons un modèle pour l'estimation du coût énergétique des contrats intelligents développé pour Ethereum. D'autre part, nous proposons un nouveau protocole pour l'identification et l'élimination des contrats non utilisés dans le but de proposer des chaînes de blocs plus frugales en calculs et espaces de stockages.
In modern times, the definition and the library’s expected functionality did not change much as before. It is still a place for us to hold massive collections of information. Traditionally, libraries require physical storage space for writings and publications, but storing and managing costs can be tremendous. Although the aid of digital promises and computers allows a super high density of information storage, it does not lower the library’s complexity. As our main source of information is moving away from physical writings toward digital, the new digital library (i.e., state-run library) faces the challenges of records’ integrity and storage efficiency. Focused on this issue, we learn the demands from the Royal Library in Denmark and try to explore the use of blockchain technology. We introduce a system named LibBlock, by integrating with both smart contract and IPFS in order to provide a robust, decentralized, flexible, and adaptive e-Library, which enables the ease of scalability and rigid record keeping. In the evaluation, we investigate the initial performance of LibBlock with Ethereum and show its viability and efficiency.
Tan Hui Yang Zen, Chin Bing Hong, P. Mohan, Vivek Balachandran
The propagation of misinformation has become prevalent in recent years and is one of the predominant factors for social media myths and conspiracy theories. This paper proposes and develops a solution to detect fake news and hence control misinformation broadcasting in social media. Existing solutions for detecting fake news involve either using Machine learning/AI or employing a crowdsourcing-based fact-checker to evaluate the reliability of the information. In our proposed solution - ABC-verify - we designed and developed an integrated framework combining both AI and a Proof-of-stake (PoS) smart contract algorithm for crowdsourcing to achieve better accuracy than AI-only or pure crowdsourcing. The advantage of the proposed solution is two-fold. Firstly, the AI model can continuously learn from the output of the smart contract algorithm. Secondly, the validated news that is added to the blockchain is immutable. The validators from the public Ethereum blockchain stake ERC721 tokens in exchange for a reward if the information reliability were accurate. The prediction from the AI classification model is based on a pre-trained BERT model on a dataset of 10,000 labelled Twitter datasets. The AI classification model proxies as one of the validators in the PoS algorithm. The final verdict from the smart contract is then fed back into the training dataset to improve the AI classification model and achieve better overall accuracy of 93%. Unlike traditional crowdsourcing platforms, news stored within the blockchain is immutable. Furthermore, the Ethereum blockchain is transparent, and every transaction is recorded within the blockchain, hence enabling authenticity and trust between peer-to-peer transactions.
Blockchain technology is continually gaining momentum, with applications expanding in sectors beyond digital assets and financial services. With the existence of a public distributed ledger, the validity of transactions and accounts on the blockchain can be easily reviewed. Nevertheless, there are malicious persons that attempt to fraud cryptocurrency holders, undermining the reliability of the blockchain. This study focuses on identifying fraudulent transactions and accounts by detecting anomalies in the Bitcoin and the Ethereum transaction networks, the two largest cryptocurrencies. By leveraging GPU-accelerated machine learning models, including Support Vector Machines, Random Forest, and Logistic Regression, we draw the metadata of over 30 million transactions on the Bitcoin network and confirmed transactions from over 500 thousand accounts on the Ethereum network. We offer insight into feature importance through sensitivity analysis, as well as train accurate models that allow for method adoption in automated fraud detection systems. The trained models achieve an accuracy and recall of 96.9% and 0.987 on the Bitcoin dataset, and 80.2% and 0.835 on the Ethereum dataset. The study of anomaly detection in the cryptocurrency blockchain done in this paper can be generalized to other blockchain networks, including health service blockchains, public sector blockchains, and financial intelligence blockchains.
The traditional public key infrastructure (PKI) issues certificates by trusted certification authority (CA). But due to the centralized structure of CA, it brings some problems, such as single-point failure, certificate opacity and so on. Another decentralized system web of trust (WOT) has a high access threshold. In addition, WOT cannot ensure the authenticity of users’ authentication because of lack of incentive, and it cannot authenticate the specific user identity. In this paper, we propose a new distributed identification system based on blockchain, called VAPKI. This system is implemented through smart contracts on Ethereum, which is transparent and immutable. Meanwhile, it can authenticate the fine-grained attributes of user identity and strengthen the authenticity of user identity through transparent authentication. In addition, VAPKI can identify the malicious user through the regular validation tasks (RVTs). In addition, it realizes the credit and deposit mechanism, ensuring users to be honest and trustworthy. This mechanism can also force users to authenticate strictly and punish malicious users found in RVTs.
Blockchain Technology Applications and Security
Internet Traffic Analysis and Secure E-voting
Advanced Steganography and Watermarking Techniques
Recent research and publications. A blockchain is a distributed data structure that is replicated and distributed among network members. The first blockchain specification was proposed together with the digital currency Bitcoin in 2008 by a man under the pseudonym Satoshi Nakamoto to solve the problem of centralizing finances around banks. Today, block-chains are used mainly in the field of decentralized finance (DeFi) in the form of cryptocur-rencies and instruments to them. There are also a few specialized foreign studies on the use of blockchain in the monitoring of supplies, but these studies are more focused on the economic and logistical feasibility of using the blockchain in supply chains, without the exact models of information systems on which such a system should work. The aim of the study. Study of blockchain technology in information systems for moni-toring the movement of goods and resources, which can improve the processes of tracking and automation in supply chains. Main material of the study. The paper develops a prototype of the information system for monitoring the movements of goods in supply chains, which is working above the Ethereum virtual machine. The system is working using two smart-contracts and the paper describes the exact structure and specification of smart-contracts and principles of communication between them for the information system for monitoring the movements of goods. Conclusions. The article presents a prototype of an information system using blockchain technology and smart contracts which are working on the Ethereum network. Based on prototyping of the information system for monitoring the movements of goods, it was concluded that the transparency of the tracking and automation process in supply chains is improved. This work is useful for designing and creating more detailed and sophisticated systems for monitoring and managing the movement of goods and other supplies based on the use of Blockchain technology.
A virtual asset is a type of asset which does not have a material representation, although its value is reflected in a real currency. Due to their nature, the price of digital assets is usually highly volatile, especially with futures, which are derivative financial contracts. This is the most important contributing factor to the problem of the low usability of digital-based contracts in enterprise operations.Previously existing virtual assets included photography, logos, illustrations, animations, audiovisual media, etc. However, virtually all of such assets required a third-party platform for exchange to currency. The necessity of having a trusted by both sides mediator greatly limited the ease of use, and ultimately restricted the number of such transactions. Still, popularity of digital assets only grew, as evidenced by an explosive growth of software applications in the 2000s, as well as blockchain-based asset space in the 2010s.The newest and most promising solution developed is based on cryptoassets. Underlying usage of block- chain technology for the transactions checking and storage ensures clarity in virtual assets’ value history. Smart contracts written for the Ethereum platform, as an example, provide a highly trustful way of express- ing predefined conditions of a certain transaction. This allows safe and calculated enterprise usage, and also eliminates the need of having a mutually trusted third-party. The transactions are fully automated and happen at the same time as the pre-defined external conditions are met.Ethereum was chosen as an exemplary platform due to its high flexibility and amount of existing development. Even now, further advancements are being explored by its founder and community. Besides Ether, it is also used nоn-fungible tokens, decentralized finance, and enterprise blockchain solutions. Another important point is how much more nature friendly it is compared to main competitors, due to energy-efficiency of the mining process, enforced by the platform itself. This makes it ideal for responsible usage as well as further research.This article explores the digital assets usage, as well as explains cryptoassets technological background, in order to highlight the recent developments in the area of futures based on virtual assets, using certain Ether implementation as an example, which offers perpetual futures.
Selfish mining has potential hazards to blockchain systems by hiding mined blocks and broadcasting them strategically. It lets the adversary gain additional rewards in the mining process which was first proposed in Bitcoin recommended by Satoshi Nakamoto. The blockchain-based application Ethereum using GHOST (Greedy Heaviest-Observed Sub-Tree) protocol with regard to Bitcoin to alleviate the loss of honest miners of stale blocks and compensate uncle blocks which will increase if selfish mining occurs. But it only makes things worse since uncle incentive mechanism reduces the cost of the failure of selfish mining and decreases the threshold of selfish mining pool needed to be profitable. There is no research which focuses on the rationale behind uncle incentive mechanism in Ethereum. This paper focuses on the feasible modifications on uncle incentive mechanism against selfish mining in Ethereum. The uncle block reference behaviors of miners are analyzed when selfish mining occurs in Ethereum by building models. Furthermore, a feasible uncle incentive mechanism against selfish mining is proposed. It is not necessary to have a strategy with a monotonous order with regard to generations of uncle blocks. The uncle incentive mechanism suggested in this paper can efficiently raise the threshold for selfish mining to be profitable by around 3.17%, an increase by around 9.76%. It offers an alternative uncle incentive mechanism for cryptocurrencies which are based on GHOST protocol.
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Blockchain is a decentralized, immutable, peer-to-peer network which provides trust, transparency, and tamper resistance and these features have led to various applications such as bitcoin and ethereum. Various structures of Blockchains are designed in fields such as e-health as it can facilitate safe and secure storage of the patient’s information in the health care system. Data integration problem in healthcare can be overcome by bringing a decentralized system in the hospital organization. This paper focuses on the use of Blockchains in managing medical records for insurance claim. By removing the central administrator, the suggested architecture uses Ethereum smart contracts to create a tamperproof and transparent healthcare system and ensure the integrity of sensitive patient data. Additionally, the smart contract keeps the patient well-informed and governs the communication between all the participants in the network. The proposed approach provides a solution to eliminate fraudulent insurance claims using Machine Learning (ML) with Blockchain. Machine Learning techniques such as Random Forest Classifier, Support Vector Machines are used along with Python and Solidity languages and an accuracy of 88% is achieved on the healthcare data set.
Anwar Said, Muhammad Umar Janjua, Saeed‐Ul Hassan, Zeeshan Muzammal · 8 authors
Ethereum, the second-largest cryptocurrency after Bitcoin, has attracted wide attention in the last few years and accumulated significant transaction records. However, the underlying Ethereum network structure is still relatively unexplored. Also, very few attempts have been made to perform link predictability on the Ethereum transactions network. This paper presents a Detailed Analysis of the Ethereum Network on Transaction Behavior, Community Structure, and Link Prediction (DANET) framework to investigate various valuable aspects of the Ethereum network. Specifically, we explore the change in wealth distribution and accumulation on Ethereum Featured Transactional Network (EFTN) and further study its community structure. We further hunt for a suitable link predictability model on EFTN by employing state-of-the-art Variational Graph Auto-Encoders. The link prediction experimental results demonstrate the superiority of outstanding prediction accuracy on Ethereum networks. Moreover, the statistic usages of the Ethereum network are visualized and summarized through the experiments allowing us to formulate conjectures on the current use of this technology and future development.
Conception d'une architecture spécifique Low Power pour les accès blockchain et Smart Contracts des plateformes IoT De nos jours, de nombreuses applications IoT sont devenues une partie essentielle de la vie des gens, des industries et des écosystèmes modernes. La plupart des applications IoT sont basées sur un système centralisé dans lequel tous les participants au système doivent s'en remettre à une entité centrale. Dans un tel système, l'immuabilité, la traçabilité et la transparence des données ne peuvent être assurées. La technologie Blockchain est un système entièrement décentralisé dans lequel le tiers de confiance (entité centrale) est supprimé. La particularité de cette technologie est qu'elle prévoit qu'une fois que les données y sont déployées, elles ne peuvent pas être modifiées ou retirées du système. Contrairement aux systèmes centralisés, la blockchain assure la traçabilité et la transparence des données. La plupart des blockchains modernes permettent également le déploiement de Smart Contracts, qui sont des programmes numériques pouvant être lus par tous les participants et exécutés automatiquement en fonction d'un événement sur la blockchain. Les caractéristiques avantageuses de la technologie blockchain montrent un intérêt évident pour l'intégration des IoT avec la technologie blockchain.Cette contribution de thèse étudie les possibilités d'intégration des IoT avec la technologie blockchain. L'une des principales parties de la contribution est le développement d'un modèle d'architecture matérielle IoT dédiée à faible consommation d'énergie qui permet la communication avec plusieurs types de blockchains. Le modèle d'architecture est composé d'un CPU basé sur ARM émulé sur QEMU et d'accélérateurs matériels cryptographiques modélisés dans le langage de description matérielle de haut niveau SystemC-TLM. Un système d'exploitation (OS) Linux est exécuté au sommet de l'architecture.Le développement de pilotes de périphériques dédiés au noyau Linux a été nécessaire car les API exécutés sur Linux ne peuvent pas accéder directement à des IP matérielles (propriétés intellectuelles) données. Les pilotes de périphériques dédiés et la bibliothèque SystemC TLM PwClkARCH ont été utilisés pour mettre en œuvre la gestion de l'énergie de l'architecture afin d'optimiser la consommation énergétique globale de l'architecture lorsqu'une API blockchain donnée est exécutée. Ce travail propose également différentes API de blockchain (Ethereum, Hyperledger Sawtooth) écrites en C++, incluant toutes les exigences de la blockchain donnée, par exemple, l'encodage ABI, la structure de transaction et les primitives cryptographiques. Les résultats de la contribution montrent qu'une réduction significative de la consommation énergétique globale peut être obtenue lorsque l'opération de multiplication des points de la courbe elliptique est accélérée par le matériel. Les résultats montrent également que lorsque la taille de la charge utile de la transaction augmente, il est intéressant d'utiliser des accélérateurs matériels de hachage pour réduire la consommation d'énergie globale et accélérer l'exécution de l'API donnée.
Health insurance plays a significant role in ensuring quality healthcare. In response to the escalating costs of the medical industry, the demand for health insurance is soaring. Additionally, those with health insurance are more likely to receive preventative care than those without health insurance. However, from granting health insurance to delivering services to insured individuals, the health insurance industry faces numerous obstacles. Fraudulent actions, false claims, a lack of transparency and data privacy, reliance on human effort and dishonesty from consumers, healthcare professionals, or even the insurer party itself, are the most common and important hurdles towards success. Given these constraints, this chapter briefly covers the most immediate concerns in the health insurance industry and provides insight into how blockchain technology integration can contribute to resolving these issues. This chapter finishes by highlighting existing limitations as well as potential future directions.
Marco Ortu, Stefano Vacca, Giuseppe Destefanis, Claudio Conversano
We analyse, using a mixture of statistical models and natural language process techniques, what happened in social media from June 2019 onwards to understand the relationships between Cryptocurrencies’ prices and social media, focusing on the rise of the Bitcoin and Ethereum prices. In particular, we identify and model the relationship between the cryptocurrencies market price changes, and sentiment and topic discussion occurrences on social media, using Hawkes’ Model. We find that some topics occurrences and rise of sentiment in social media precedes certain types of price movements. Specifically, discussions concerning governments, trading, and Ethereum cryptocurrency as an exchange currency appear to negatively affect Bitcoin and Ethereum prices. Those concerning investments, appear to explain price rises, whilst discussions related to new decentralized realities and technological applications explain price falls. Finally, we validate our model using a real case study: the already famous case of ”Wallstreetbet and GameStop”1 that took place in January 2021.
Youcef Maouchi, Lanouar Charfeddine, Ghassen El Montasser
This paper investigates digital financial bubbles amidst the COVID-19 pandemic. Using a sample of 9 DeFi tokens, 3 NFTs, Bitcoin, and Ethereum, we detect several bubbles overlapping the examined cryptoassets. We also uncover DeFi and NFT-specific bubbles in Summer 2020 suggesting distinct driving factors for this class of assets. We document that DeFi and NFTs bubbles are less recurrent but have higher magnitudes than cryptocurrencies' bubbles. We also find that COVID-19 and trading volume exacerbate bubble occurrences, while Total Value Locked (TVL) is negatively associated with cryptoassets' bubbles. Our results suggest that TVL can be used as a tool for market monitoring.
Nafise Bayrami Fard, Mehdi Salay Naderi, Gevork B. Gharehpetian
Blockchain is an emerging technology that due to its unique features, such as decentralization, elimination of intermediaries, immutability and increased security, accuracy and transparency has been highly regarded in various industries, including the smart grid. Creating an electricity market for energy exchanges between producers and consumers in a microgrid is important because of the increasing tendency to use renewable energy such as solar cells. This paper presents two pricing mechanisms based on the Mid-Market Rate and the auction to find the optimal price of energy exchanges, which increases the profit from sales for producers and reduces the cost of purchase for consumers. This paper also proposes three smart contracts for peer-to-peer energy trading which are responsible for executing energy exchanges and enabling the recording of transaction information on the Ethereum network in an encrypted manner with great precision and transparency.
The development of Vehicular Ad Hoc Networks (VANET) has brought many advantages to facilitate the deployment of the Intelligent Transportation System (ITS). However, without proper protection, VANETs can be vulnerable to severe cyber-attacks. This paper explores the threats to the VANETs and proposes a security scheme for VANETs with a Blockchain (VNB). Furthermore, the proposed VNB with Ethereum was developed. With a graphical user interface, experiments were conducted. For ad hoc communications, a vehicle can randomly select another vehicle, and VNB will authenticate the selected vehicle with the Blockchain and Trusted Authority (TA). Preliminary test results successfully proved that Blockchain can be the key technology to mitigate the security threats to VANETs.
Blockchain technology has the characteristics of decentralization, traceability and tamper proof, which creates a reliable decentralized transaction mode, further accelerating the development of the blockchain platforms. However, with the popularization of various financial applications, security problems caused by blockchain digital assets, such as money laundering, illegal fundraising and phishing fraud, are constantly on the rise. Therefore, financial security has become an important issue in the blockchain ecosystem, and identifying the types of accounts in blockchain (e.g. miners, phishing accounts, Ponzi contracts, etc.) is of great significance in risk assessment and market supervision. In this paper, we construct an account interaction graph using raw blockchain data in a graph perspective, and proposes a joint learning framework for account identity inference on blockchain with graph contrast. We first capture transaction feature and correlation feature from interaction graph, and then perform sampling and data augmentation to generate multiple views for account subgraphs, finally jointly train the subgraph contrast and account classification task. Extensive experiments on Ethereum datasets show that our method achieves significant advantages in account identity inference task in terms of classification performance, scalability and generalization.
Manuel Valentin, Claus Pahl, Nabil El Ioini, Hamid R. Barzegar
Recent developments in distributed ledger technologies have created a whole new set of possibilities in the way of managing trust, security, privacy and traceability in computer-based transactions, principles which are increasingly gaining importance in the world of IoT. Currently, IoT devices are generally based on centralized, client-server systems, where digital service providers have complete control over user data and information generated by their devices. In this paper we present the development of a decentralized access and management system for IoT devices, where operations on these devices, such as the installation and management of apps are handled by a blockchain-based identification and record system. The system prototype consists of a smartphone application acting as a management hub for the whole system, an IoT device API implementation for allowing secure access to management and data functionalities, and a set of smart contracts on the Ethereum blockchain, where all necessary information for the functioning of the system is stored. The system allows app developers to provide their apps as Docker containers for IoT devices without the need to publish them on a centralized app store, and a regular user can subscribe to and access the available applications by paying in cryptocurrency without revealing any private information to the system. A cost and performance evaluation have been performed to assess the feasibility of the proposed solution.
In this contribution, we present a simulator for the Ethereum 2.0 Beacon Chain Network. The purpose of this tool is to run various simulations to verify hypotheses and attack scenarios without the need to actually stake cryptocurrency and fulfilling validator's obligations inside the network. Because its codebase is derived from the official specification, functionality such as the fork choice rule and finality mechanisms are guaran-teed to match the behavior of conforming client implementations. Basic metrics about the live Beacon Chain network were collected and used as an input to the conducted simulations and to verify the obtained simulation results. We find that the results of conducted simulations show great correspondence to the behavior of the live Beacon Chain. We further implement various kinds of misbehavior to verify that misbehaving validators are slashed and deprived of their invested capital. Finally, two attacks are implemented to demonstrate how the simulator can be used to verify and test the applicability of various attack scenarios.
Abstract Decentralized Finance (DeFi) is a system of financial products and services built and delivered through smart contracts on various blockchains. In recent years, DeFi has gained popularity and market capitalization. However, it has also been connected to crime, particularly various types of securities violations. The lack of Know Your Customer requirements in DeFi poses challenges for governments trying to mitigate potential offenses. This study aims to determine whether this problem is suited to a machine learning approach, namely, whether we can identify DeFi projects potentially engaging in securities violations based on their tokens’ smart contract code. We adapted prior works on detecting specific types of securities violations across Ethereum by building classifiers based on features extracted from DeFi projects’ tokens’ smart contract code (specifically, opcode-based features). Our final model was a random forest model that achieved an 80% F-1 score against a baseline of 50%. Notably, we further explored the code-based features that are the most important to our model’s performance in more detail by analyzing tokens’ Solidity code and conducting cosine similarity analyses. We found that one element of the code that our opcode-based features can capture is the implementation of the SafeMath library, although this does not account for the entirety of our features. Another contribution of our study is a new dataset, comprising (a) a verified ground truth dataset for tokens involved in securities violations and (b) a set of legitimate tokens from a reputable DeFi aggregator. This paper further discusses the potential use of a model like ours by prosecutors in enforcement efforts and connects it to a wider legal context.
In institutes of higher learning, most of the time course material development and delivery follow a centralized model which is fully lecturer-controlled. In this model, engaging students as partners in learning is a challenging problem as: 1) students are usually hesitant to contribute due to the fear of getting it wrong, 2) not much incentive for them to put in the extra effort, and 3) current online learning systems lack adequate facilities to support seamless and anonymous interactions between students. In this work, we propose EtherLearn, a blockchain based peer-learning system to distribute the control of how course material and formative assessments could be developed and delivered over the set of stakeholders in the particular course. EtherLearn leverages features of the rising blockchain technology, e.g., decentralization, anonymity, transparency and security to address the aforementioned concerns in university learning environments. To this end, we have successfully implemented a proof of concept for EtherLearn based on the Ethereum blockchain network. We have also conducted preliminary evaluations to demonstrate that it can be useful in decentralizing learning resource creation and student sharing in an encouraging teaching and learning environment.
Runkai Yang, Xiaolin Chang, Jelena Mišić, Vojislav B. Mišić
Bitcoin and Ethereum are the top two blockchain-based cryptocurrencies whether from cryptocurrency market cap or popularity. However, they are vulnerable to selfish mining and stubborn mining due to that both of them adopt Proof-of-Work consensus mechanism. In this paper, we develop a novel Markov model, which can study selfish mining and seven kinds of stubborn mining in both Bitcoin and Ethereum. The formulas are derived to calculate several key metrics, including relative revenue of miners, blockchain performance in terms of stale block ratio and transactions per second, and blockchain security in terms of resistance against double-spending attacks. Numerical analysis is conducted to investigate the quantitative relationship between the relative-revenue-optimal mining strategy for malicious miners and two miner features in Bitcoin and Ethereum, respectively. The quantitative analysis results can assist honest miners in detecting whether there is any malicious miner in the system and setting the threshold of mining node's hash power in order to prevent malicious miners from making profit through selfish and stubborn mining.