Bogdan-Ionut Pahontu, Adrian Petcu, Alexandru Predescu, Diana Arsene · 5 authors
Water is one of the most important resources in our lives, and because of this, the interest in water management systems is growing constantly. A primary concern regarding urban water distribution is how to build robust solutions to facilitate water monitoring flows with the support of consumer involvement. Crowdsensing solutions contribute to the involvement in social platforms for increased awareness about the importance of water resources based on incentives and rewards. Blockchain is one of the technologies that has become increasingly popular in the last few years. The possibility of using this architecture in such different sectors while integrating emerging concepts, such as crowdsensing, the Internet of Things, serious gaming, and decision support systems, offers a lot of alternatives and approaches for designing modern applications. This paper aims to present how these technologies can be combined in order to migrate the functionalities of a water distribution management system from a centralized architecture to a decentralized one by leveraging blockchain technologies. The proposed application was designed to facilitate incident reporting flows in public water distribution networks. The proposed solution was to migrate the rewarding mechanisms using the Ethereum infrastructure. The novelty of this solution is determined by the introduction of this decentralized approach into the architecture and also by increasing customer interest by offering tradeable rewards and dynamic subscription discounts. This results in a new decentralized architecture that allows for more transparent interactions between the water provider and clients and increases customer engagement to contribute to water reporting flows.
This paper delivers a comprehensive report on the successful implementation of an Ethereum and Smart Contract based decentralized web application that is a platform for healthcare institutes to maintain their patient medical records. The system allows multiple healthcare institutes to share patient medical data. The immutability of the data is an essential requirement when handling sensitive data like a patient’s medical history, hence the incorporation of blockchain technology which is a digital edger that keeps track of transactions or information in a secure and transparent way. Key Words: Blockchain, Ethereum, Smart Contracts, Healthcare, Security and Privacy.
Tiffany Tien Nee Pragasam, John V. Thomas, V. Maria Anu, Subhashini Radhakrishnan
Cryptocurrencies are rapidly growing and are increasingly accepted by major commercial vendors. However, along with their rising popularity, they have also become the go-to currency for illicit activities driven by the anonymity they provide. Cryptocurrencies such as the one on the Ethereum blockchain provide a way for entities to hide their real-world identities behind pseudonyms, also known as addresses. Hence, the purpose of this work is to uncover the level of anonymity in Ethereum by investigating multiclass classification models for Externally Owned Accounts (EOAs) of Ethereum. The researchers aim to achieve this by examining patterns of transaction activity associated with these addresses. Using a labelled Ethereum address dataset from Kaggle and the Ethereum crypto dataset by Google BigQuery, an address profiles dataset was compiled based on the transaction history of the addresses. The compiled dataset, consisting of 4371 samples, was used to tune and evaluate the Random Forest, Gradient Boosting and XGBoost classifier for predicting the category of the addresses. The best-performing model found for the problem was the XGBoost classifier, achieving an accuracy of 75.3% with a macro-averaged F1-Score of 0.689. Following closely was the Random Forest classifier, with an accuracy of 73.7% and a macro-averaged F1-Score of 0.641. Gradient Boosting came in last with 73% accuracy and a macro-averaged F1-Score of 0.659. Owing to the data limitations in this study, the overall scores of the best model were weaker in comparison to similar research, with the exception of precision, which scored slightly higher. Nevertheless, the results proved that it is possible to predict the category of an Ethereum wallet address such as Phish/Hack, Scamming, Exchange and ICO wallets based on its transaction behaviour.
Joachim Neu, Srivatsan Sridhar, Lei Yang, David Tse
Classic BFT consensus protocols guarantee safety and liveness for all clients if fewer than one-third of replicas are faulty. However, in applications such as high-value payments, some clients may want to prioritize safety over liveness. Flexible consensus allows each client to opt for a higher safety resilience, albeit at the expense of reduced liveness resilience. We present the first construction that allows optimal safety--liveness tradeoff for every client simultaneously. This construction is modular and is realized as an add-on applied on top of an existing consensus protocol. The add-on consists of an additional round of voting and permanent locking done by the replicas, to sidestep a sub-optimal quorum-intersection-based constraint present in previous solutions. We adapt our construction to the existing Ethereum protocol to derive optimal flexible confirmation rules that clients can adopt unilaterally without requiring system-wide changes. This is possible because existing Ethereum protocol features can double as the extra voting and locking. We demonstrate an implementation using Ethereum's consensus API.
Stefan Pancari, Anik Rashid, Jason Zheng, Shirali Patel · 6 authors
Despite the lack of blockchain systems being utilized in modern IoT environments, the prevalence of blockchain technology is increasing, due to its high level of security and accountability. The integration of blockchain technology and access control in a decentralized system for smart home networks is a promising solution to this issue. This paper compares the implementation of attribute-based access control (ABAC) with two popular blockchain platforms, Ethereum and Hyperledger Fabric, for a smart home internet of things (IoT) environment. We present a comprehensive summary of access-control and blockchain-access-control methods, to provide the necessary background for this study. Additionally, we present an original ABAC smart contract for Ethereum, and the modification of a pre-existing Hyperledger Fabric ABAC smart contract, for this comparison. Through the simulation of both implementations, the advantages and limitations will be considered, to determine which is better suited for a smart home IoT environment.
Tayeb Kenaza, Sami Messai, Islam Debicha, Mehdi Sehaki
Abstract Electronic Health Records (EHRs) store sensitive and critical patient information, necessitating stringent access control and sharing mechanisms to uphold data security and comply with privacy regulations such as the General Data Protection Regulation (GDPR). In this paper, we propose a comprehensive architecture and a suite of efficient protocols that leverage the synergistic capabilities of blockchain and Interplanetary File System (IPFS) technologies to enable secure access control and sharing of EHRs. Our approach is based on a private blockchain, wherein smart contracts are deployed to enforce control exclusively by patients. By granting patients exclusive control over their EHRs, our solution ensures compliance with personal data protection laws and empowers individuals to manage their health information autonomously. Notably, our proposed architecture seamlessly integrates with existing health provider information systems, facilitating interoperability and effectively addressing security and data heterogeneity challenges. To exhibit the effectiveness of our approach, we developed a prototype implementation utilizing the Ethereum platform, enabling the simulation of diverse scenarios involving access control and health data sharing among healthcare practitioners. Our experimental results demonstrate the scalability, recovery mechanisms, and revocation procedures embedded within our solution, thereby substantiating its efficacy and robustness in real-world healthcare settings. Overall, our study provides a significant contribution towards a secure and interoperable framework for EHR access control and sharing, leveraging patient-centric control mechanisms.
Fake products are items that are marketed and sold as genuine, high-quality products but are counterfeit or low-quality knockoffs. These products are often designed to closely mimic the appearance and branding of the genuine product to deceive consumers into thinking they are purchasing the real thing. Fake products can range from clothing and accessories to electronics and other goods and can be found in a variety of settings, including online marketplaces and brick-and-mortar stores. Blockchain technology can be used to help detect fake products in a few different ways. One of the most common ways is through the use of smart contracts, which are self-executing contracts with the terms of the agreement between buyer and seller being directly written into lines of code. This allows for a high level of transparency and traceability in supply chain transactions, making it easier to identify and prevent the sale of fake products and the use of unique product identifiers, such as serial numbers or QR codes, that are recorded on the blockchain. This allows consumers to easily verify the authenticity of a product by scanning the code and checking it against the information recorded on the blockchain. In this study, we will use smart contracts to detect fake products and will evaluate based on Gas cost and ethers used for each implementation.
This paper addresses the lack of research on quantifying Maximal Extractable Value (MEV) on Ethereum Layer 2 networks (L2s). Our findings reveal a substantial amount of MEV to be extracted on L2s, particularly on Polygon, with a lower bound of $213 million surpassing previous estimates. We observe that the majority of detected MEV on L2s consists of arbitrage opportunities, as liquidations are rare. These results emphasize the need for continuous monitoring and analysis of MEV on L2s, promoting informed decision-making for network selection and highlighting the associated risks.
S Sultana, Ch. Rupa, R. Pavana Malleswari, Thippa Reddy Gadekallu
In the digital age, ensuring the authenticity and security of academic certificates is a critical challenge faced by educational institutions, employers, and individuals alike. Traditional methods for verifying academic credentials are often cumbersome, time-consuming, and susceptible to fraud. However, the emergence of blockchain technology offers a promising solution to address these issues. The proposed system utilizes a blockchain network, where each academic certificate is stored as a digital asset on the blockchain. These digital certificates are cryptographically secured, timestamped, and associated with unique identifiers, such as hashes or public keys, ensuring their integrity and immutability. Anyone with access to the blockchain network can verify a certificate’s authenticity, using the MetaMask extension and Ethereum network, eliminating the need for intermediaries and reducing the risk of fraudulent credentials. The main strength of the paper is that the data that are stored in the blockchain are unique identifiers of the encrypted data, which is encrypted by using an encryption technique that provides more security to the academic certificates. Furthermore, IPFS is also used to store large amounts of encrypted data.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Jorge Ceron, Cristian Tinipuclla, Pedro Shiguihara-Juárez
Blockchain has become an important alternative in plenty of industries due to its features such as traceability, transparency and data integrity. Blockchain is not only present in the management of structured data-like names, timestamps, addresses and so on, but also in the management of unstructured data such as videos or images. In the present work, we focus on videos because nowadays, they are potentially exposed to be modified or altered from plenty of sources and platforms. However, we found that there are hardly any surveys or systematic literature reviews containing applications of both blockchain and integrity of videos. Thus, in this survey, we state a literature review for primary articles about the use of blockchain for video integrity to update and provide a better comprehension about this topic. As results, we encountered Ethereum and internet of vehicles as the largest underlying blockchain and field of application, respectively. At the end, we explain our findings about blockchains for video integrity and why it is an emerging topic.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Punam Bedi, T.K. Saha Roy, Vidhi Arora, Pushkar Gole
Every recruitment process usually starts with shortlisting candidates based on the skills mentioned in their resumes. Candidates may try to boost their level of expertise on their resume to appear more qualified to employers, and in order to confirm their skills and experience, the company’s HR staff tries to contact the candidate’s former employers. However, this process is laborious and not very thorough. As a result, the candidates’ exaggerated remarks about their skills on resumes may result in poor hires, ultimately leading to a financial and reputational loss for the company. Therefore, in order to revamp the existing workflow, an automated system is required for skill verification in the recruitment process, which guarantees an effective hiring process in the company. In this research work, a novel system is devised and implemented for automatically verifying employees’ skills with the help of Smart Contracts based on the Ethereum blockchain. The major contribution of the proposed system is that it allows the registered companies to monitor their employees and their skills. Furthermore, the proposed system will benefit the employees registering into the system to get their skills endorsed and experiences approved. Due to the need for achieving consensus in Blockchain technology, it is harder to make fraudulent profiles. By leveraging the security and decentralization offered by blockchain technology, the companies can evaluate the skills as well as experiences of the candidates before hiring them and be sure about the information verified by the candidate’s former employers.
Neha Bhat, Anil A. Bharath, N Nagashree, Shantakumar B. Patıl
Ride Booking platforms have become increasingly popular due to their convenience and other benefits. Taxi-service giants have dominated the market with the increasing demands and have introduced predatory practices such as surge-pricing in order to maintain the demand-supply balance, while providing drastically poor levels of reliability and responsiveness in majority of the cities. The existing solutions still face problems such as lack of privacy, control by intermediaries and centralization. In this paper, we propose a Blockchain-based Ride Booking platform built using Ethereum Smart Contracts and novel features such as an Optimal Price Calculation algorithm and Ride Fare Bidding. The platform leverages the inherent advantages of blockchain technology, such as decentralization, transparency, and immutability, to solve these problems. Blockchain technology also aids in increasing security and eliminating the single point of failure. The use of smart contracts enables the platform to execute transactions automatically and transparently, eliminating the need for intermediaries and reducing costs. The proposed solution demonstrates characteristics such as fairness, accountability, and privacy in the ride booking industry. In summary, our decentralized ride booking platform represents a significant step towards a fairer and more efficient ride booking ecosystem.
Smart farming, as a branch of the Internet of Things (IoT), combines the recognition of agricultural economic competencies and the progress of data and information collected from connected devices with statistical analysis to characterize the essentials of the assimilated information, allowing farmers to make intelligent conclusions that will maximize the harvest benefit. However, the integration of advanced technologies requires the adoption of high-tech security approaches. In this paper, we present a framework that promises to enhance the security and privacy of smart farms by leveraging the decentralized nature of blockchain technology. The framework stores and manages data acquired from IoT devices installed in smart farms using a distributed ledger architecture, which provides secure and tamper-proof data storage and ensures the integrity and validity of the data. The study uses the AWS cloud, ESP32, the smart farm security monitoring framework, and the Ethereum Rinkeby smart contract mechanism, which enables the automated execution of pre-defined rules and regulations. As a result of a proof-of-concept implementation, the system can detect and respond to security threats in real time, and the results illustrate its usefulness in improving the security of smart farms. The number of accepted blockchain transactions on smart farming requests fell from 189,000 to 109,450 after carrying out the first three tests while the next three testing phases showed a rise in the number of blockchain transactions accepted on smart farming requests from 176,000 to 290,786. We further observed that the lesser the time taken to induce the device alarm, the higher the number of blockchain transactions accepted on smart farming requests, which demonstrates the efficacy of blockchain-based poisoning attack mitigation in smart farming.
As the distributed ledger technology underlying cryptocurrencies such as Bitcoin and Ethereum, blockchain has empowered various industries, such as supply chain management, healthcare, government services, e-voting, etc. However, the ever-growing ledger on each node has been the main bottleneck for blockchain scalability as the network scale expands, which worsens in blockchain-enabled IoT scenarios with resource-limited devices. With the support of the Reed–Solomon (RS) code, the Cuckoo-Store (CS), a ledger storage optimization engine, is proposed in this paper to dramatically decrease the storage burden on each node by encoding the ledger as data segments with redundancy and distributing them to multiple nodes. These distributed data segments can be collected and decoded using RS code to recover the original ledger. Furthermore, the Cuckoo filter (CF) is used to guarantee the integrity of the encoded segments, which helps detect the forged segments and facilitates the process of ledger recovery. Theoretical analysis and simulation results show that the CS engine can decrease the storage in each node by more than 94%, and the original ledger can be recovered efficiently with acceptable communication overheads.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Stealth addresses are a privacy-enhancing technology that provides recipient anonymity on blockchains. In this work, we investigate the recipient anonymity and unlinkability guarantees of Umbra, the most widely used implementation of the stealth address scheme on Ethereum, and its three off-chain scalability solutions, i.e., Arbitrum, Optimism, and Polygon. Specifically, we define and evaluate four heuristics to uncover the real recipients of stealth payments. We find that for the majority of Umbra payments, it is straightforward to establish the recipient, hence nullifying the benefits of using Umbra. In particular, we identify the real recipient of 48.5%, 25.8%, 65.7%, and 52.6% of all Umbra transactions on the Ethereum main net, Polygon, Arbitrum, and Optimism networks, respectively. Finally, we suggest easily implementable countermeasures to evade our deanonymization and linking attacks.
A large amount of raw data collected by satellites is processed by the production chain to obtain a large amount of product data, of which the secure exchange and storage is of interest to researchers in the field of remote sensing information science. Authentic, secure data is a critical foundation for data analysis and decision-making. And traditional centralized cloud computing systems are vulnerable to attacks, and once the central server is successfully attacked, all data will be lost. Distributed Ledger Technology (DLT) is an innovative computer technology that can ensure information security, traceability and tamper-proof, and can be applied to the field of remote sensing. Although there are many advantages to using DLT in remote sensing applications, there are some obstacles and limitations to its application. Remote sensing data has the characteristics of large data volume, spatiotemporal nature, global and so on, and it is difficult to store and interconnect remote sensing data in the blockchain. To address these issues, this paper proposes a trustworthy and decentralized system using blockchain. The novelty of this paper is to propose a multi-level blockchain architecture in which the system collects remote sensing data and stores it in the Interplanetary File System (IPFS) network, after generating the IPFS hash, the network rehashes the value again and uploads it on the Ethereum chain for public query. Distributed data storage improves data security, supports the secure exchange of information, and improves the efficiency of data management.
Smart contracts are widely used on the blockchain to implement complex transactions, such as decentralized applications on Ethereum. Effective vulnerability detection of large-scale smart contracts is critical, as attacks on smart contracts often cause huge economic losses. Since it is difficult to repair and update smart contracts, it is necessary to find the vulnerabilities before they are deployed. However, code analysis, which requires traversal paths, and learning methods, which require many features to be trained, are too time-consuming to detect large-scale on-chain contracts. Learning-based methods will obtain detection models from a feature space compared to code analysis methods such as symbol execution. But the existing features lack the interpretability of the detection results and training model, even worse, the large-scale feature space also affects the efficiency of detection. This paper focuses on improving the detection efficiency by reducing the dimension of the features, combined with expert knowledge. In this paper, a feature extraction model Block-gram is proposed to form low-dimensional knowledge-based features from bytecode. First, the metadata is separated and the runtime code is converted into a sequence of opcodes, which are divided into segments based on some instructions (jumps, etc.). Then, scalable Block-gram features, including 4-dimensional block features and 8-dimensional attribute features, are mined for the learning-based model training. Finally, feature contributions are calculated from SHAP values to measure the relationship between our features and the results of the detection model. In addition, six types of vulnerability labels are made on a dataset containing 33,885 contracts, and these knowledge-based features are evaluated using seven state-of-the-art learning algorithms, which show that the average detection latency speeds up 25× to 650×, compared with the features extracted by N-gram, and also can enhance the interpretability of the detection model.
The past year has witnessed significant growth in the size, and mainstream profile, of financial markets built on distributed ledger technology (“DLT”), in particular “blockchain”. In February 2021, the market capitalisation of the cryptoasset Bitcoin, which is built on the first operational blockchain, topped USD 1 trillion. So-called decentralised finance (“DeFi”), built mostly on the Ethereum blockchain, grew from less than USD 1 billion to over USD 80 billion in May 2021. Even if one adopts a sceptical posture towards these developments, “crypto” markets cannot be ignored by scholars or practitioners of financial and monetary law.
Alpesh Bhudia, Anna Cartwright, Edward Cartwright, Darren Hurley-Smith · 5 authors
Consensus algorithms facilitate agreement on and resolution of blockchain functions, such as smart contracts and transactions. Ethereum uses a Proof-of-Stake (PoS) consensus mechanism, which depends on financial incentives to ensure that validators perform certain duties and do not act maliciously. Should a validator attempt to defraud the system, legitimate validators will identify this and then staked cryptocurrency is `burned' through a process of slashing. In this paper, we show that an attacker who has compromised a set of validators could threaten to perform malicious actions that would result in slashing and thus, hold those validators to ransom. We use game theory to study how an attacker can coerce payment from a victim, for example by deploying a smart contract to provide a root of trust shared between attacker and victim during the extortion process. Our game theoretic model finds that it is in the interests of the validators to fully pay the ransom due to a lack of systemic protections for validators. Financial risk is solely placed on the victim during such an attack, with no mitigations available to them aside from capitulation (payment of ransom) in many scenarios. Such attacks could be disruptive to Ethereum and, likely, to many other PoS networks, if public trust in the validator system is eroded. We also discuss and evaluate potential mitigation measures arising from our analysis of the game theoretic model.
Over the last decade, the cryptocurrency market has experienced unprecedented growth, emerging as a prominent financial market. As this market rapidly evolves, it necessitates re-evaluating which cryptocurrencies command the market and steer the direction of blockchain technology. We implement a network-based cryptocurrency market analysis to investigate this changing landscape. We use novel hourly-resolution data and Kendall's Tau correlation to explore the interconnectedness of the cryptocurrency market. We observed critical differences in the hierarchy of cryptocurrencies determined by our method compared to rankings derived from daily data and Pearson's correlation. This divergence emphasizes the potential information loss stemming from daily data aggregation and highlights the limitations of Pearson's correlation. Our findings show that in the early stages of this growth, Bitcoin held a leading role. However, during the 2021 bull run, the landscape changed drastically. We see that while Ethereum has emerged as the overall leader, it was FTT and its associated exchange, FTX, that greatly led to the increase at the beginning of the bull run. We also find that highly-influential cryptocurrencies are increasingly gaining a commanding influence over the market as time progresses, despite the growing number of cryptocurrencies making up the market.
Ridwan Setiawan, Indri Tri Julianto, Fikri Fahru Roji
Cryptocurrency has become a phenomenon worldwide. Although not all countries have legalized it, it is considered a promising investment asset. Currently, there are three top-ranking cryptocurrencies: Bitcoin, Ethereum, and Tether. This research aims to compare the performance of five forecasting algorithms, namely Autoregressive Integrated Moving Average (ARIMA), Neural Network, Support Vector Machine, Linear Regression, and Generalized Linear Model, using the dataset of Bitcoin, Ethereum, and Tether cryptocurrencies. The research methodology employed is Knowledge Discovery In Databases (KDD). The technique involves assessing the performance based on the Root Mean Square Error (RMSE) and comparing the results to find the most optimal model performance. The research findings indicate that for Bitcoin cryptocurrency, the Neural Network algorithm produced the most optimal results with an RMSE of 9180.534. For Ethereum cryptocurrency, the Neural Network algorithm demonstrated the best performance with an RMSE value of 537.528. Furthermore, for Tether cryptocurrency, the ARIMA algorithm yielded the best performance with an RMSE value of 0.003. Keywords – bitcoin, cryptocurrency, ethereum, forecasting, tether
Smart agriculture is quickly becoming into a big financial sector with broad global implications.The main forces behind this technical, ecological, and economical movement are the demand for greater food quality, real-time weather and soil condition monitoring, and these.In order to produce not just better agricultural goods, This study explores the exciting notion of fusing IoT, smart contract, and smart agricultural technology, but also enhancing the agriculture logistics and related supply chain, resulting in several advantages for all parties concerned.On the basis of numerical and optionally categorical data, This study explores the exciting notion of fusing IoT, smart contract, and smart agricultural technology.Additionally, Solidity provides an example implementation of one such measure, It is also demonstrated how to create smart contracts for the Ethereum Virtual Machine using a high-level language.Lastly, some digitalization of agricultural assets, A crucial step for smart contracts based on tangible objects is also covered