Abstract. Volatility is a fluctuation or random change in return prices, in time series data volatility can cause heteroscedasticity or variance that is not constant where in general time series data must have a constant variance (homogeneous). Analysis that can be used in handling data with non-constant (heterogeneous) variance is using the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model. Return data is also identical to asymmetric changes in terms of negative and positive changes, it can also be analyzed with a special model, namely the Threshold GARCH model, where the TGARCH model considers positive and negative changes in return data to have different effects. The return data used in this study is Ethereum return data taken from the investment website and will be modeled using the TGARCH model. The first model estimated is the ARIMA (2,1,1) model. After testing the residual data, it was found that the ARIMA(2,1,1) model had an ARCH effect (heteroscedasticity), the second model estimated was the GARCH(1,1) model. After testing the residual data, it is found that the GARCH(1,1) model has an asymmetric effect, so the TGARCH model can be estimated. The best TGARCH model is the TGARCH(1,1) model. From the results of the research conducted, it can be concluded that the influence of positive allowances (good news) has a greater influence on the return value of ethereum than negative allowances (bad news). When there is a positive allowance, it will have an impact on the volatility of the Ethereum return value of 0.0006212. Meanwhile, when there is a negative allowance, it will have an impact on the volatility of the Ethereum return value of -0.32753. Abstrak. Volatilitas adalah fluktuasi atau perubahan harga return secara acak, dalam data time series volatilitas dapat menyebabkan terjadinya heteroskedastisitas atau varians yang tidak konstan dimana pada umumnya data time series harus memiliki varians yang konstans (homogen). Analisis yang dapat digunakan dalam menangani data dengan varians yang tidak konstans (heterogen) yaitu menggunakan model Generalized Autoregressive Conditional Heteroscedasticity (GARCH). Data return pun identik dengan perubahan asimetris dalam hal perubahan negatif dan positif, hal tersebut juga dapat dianalisis dengan model khusus yaitu model Threshold GARCH, dimana model TGARCH menganggap perubahan positif dan negatif pada data return memiliki efek yang berbeda. Data return yang digunakan dalam penelitian ini adalah data return Ethereum yang diambil dari website investasi dan akan dimodelkan menggunakan model TGARCH. Model pertama yang diestimasi adalah model ARIMA(2,1,1). Setelah dilakukan pengujian pada residual data, didapatkan hasil bahwa model ARIMA(2,1,1) memiliki efek ARCH (heteroskedastisitas), model kedua yang diestimasi adalah model GARCH(1,1). Setelah dilakukan pengujian pada residual data, didapatkan bahwa model GARCH(1,1) memiliki efek asimetris, maka model TGARCH dapat diestimasikan. Model TGARCH yang terbaik adalah model TGARCH(1,1). Dari hasil penelitian yang dilakukan dapat disimpulkan bahwa pengaruh gunjangan positif (good news) memiliki pengaruh yang lebih besar pada nilai return ethereum dibandingkan gunjangan negatif (bad news). Ketika terdapat gunjangan positif, maka akan berdampak pada volatilitas nilai return Ethereum sebesar 0.0006212. Sedangkan ketika terdapat gunjangan negatif, maka akan berdampak pada volatilitas nilai return Ethereum sebesar -0.32753.
In Ethereum, the ledger exchanges messages along an underlying Peer-to-Peer (P2P) network to reach consistency. Understanding the underlying network topology of Ethereum is crucial for network optimization, security and scalability. However, the accurate discovery of Ethereum network topology is non-trivial due to its deliberately designed security mechanism. Consequently, existing measuring schemes cannot accurately infer the Ethereum network topology with a low cost. To address this challenge, we propose the Distributed Ethereum Network Analyzer (DEthna) tool, which can accurately and efficiently measure the Ethereum network topology. In DEthna, a novel parallel measurement model is proposed that can generate marked transactions to infer link connections based on the transaction replacement and propagation mechanism in Ethereum. Moreover, a workload offloading scheme is designed so that DEthna can be deployed on multiple distributed probing nodes so as to measure a large-scale Ethereum network at a low cost. We run DEthna on Goerli (the most popular Ethereum test network) to evaluate its capability in discovering network topology. The experimental results demonstrate that DEthna significantly outperforms the state-of-the-art baselines. Based on DEthna, we further analyze characteristics of the Ethereum network revealing that there exist more than 50% low-degree Ethereum nodes that weaken the network robustness.
Set reconciliation, where two parties hold fixed-length bit strings and run a protocol to learn the strings they are missing from each other, is a fundamental task in many distributed systems. We present Rateless Invertible Bloom Lookup Tables (Rateless IBLTs), the first set reconciliation protocol, to the best of our knowledge, that achieves low computation cost and near-optimal communication cost across a wide range of scenarios: set differences of one to millions, bit strings of a few bytes to megabytes, and workloads injected by potential adversaries. Rateless IBLT is based on a novel encoder that incrementally encodes the set difference into an infinite stream of coded symbols, resembling rateless error-correcting codes. We compare Rateless IBLT with state-of-the-art set reconciliation schemes and demonstrate significant improvements. Rateless IBLT achieves 3--4× lower communication cost than non-rateless schemes with similar computation cost, and 2--2000× lower computation cost than schemes with similar communication cost. We show the real-world benefits of Rateless IBLT by applying it to synchronize the state of the Ethereum blockchain, and demonstrate 5.6× lower end-to-end completion time and 4.4× lower communication cost compared to the system used in production.
Cryptography has always been the science of Secure Communication. The past decade has been epitomized by the emergence of Blockchain Technology, without leaving Cryptography unaffected. The manifesto of Blockchains is Decentralization, making it inevitable that the information is stored and verified in real-time by thousands of participants. This spotlighted two necessities: information should be as concise as possible and verification of processes should be fast. From a cryptographic perspective, this translates to a central desideratum: Succinctness. A cryptographic construction is called Succinct if its algorithm is generating outputs that are (exponentially) smaller than the inputs. This allows the cryptosystem to treat large data and produce concise outputs that, nevertheless, preserve the desired functionality of the system. In this thesis, we are concerned with a specific type of Succinct cryptographic primitives: Succinct Commitments. Cryptographic commitments are objects that allow one to commit to some data, providing a binding representative. Then at any later point they can open back the (committed) data providing an opening proof, but without being able to open differently the representative. In more detail, in our work we deal with commitments with more fine-grained openings, where one can generate an opening proof of the commitment to a function f (m) of the initial data m. Firstly, we deal with set commitments with private (non-)memberhsip openings. We construct succinct zero-knowledge proofs for the problem of set (non-)membership. Intuitively, a zero- knowledge proof is a cryptographic primitive that allows one to prove a statement, in a sound way, without leaking any other information except for the fact that the statement holds. In a zero-knowledge proof for set membership first one commits to a public set and then a party can prove membership to the set but without betraying which element of the set exactly is. Such (set) commitments with this type of fine-grained openings are the cornerstone of Anonymous Cryptocurrencies such as Zcash. In particular we provide efficient zero-knowledge proofs for the opening of RSA accumulators, one of the most popular set commitments. First, we show efficient protocols for membership and non-membership of a single element. Then we construct succinct zero-knowledge membership proofs for multiple elements, where the size of the proof is independent of the number of elements proven. The two techniques are qualitatively different. Secondly, we switch our attention to Vector Commitments, with local positional openings. We put forth the notion of Incremental Aggregation, in which one can arbitrarily aggregate opening proofs of any positions into a single (concise) proof and inversely disaggregate a proof of multiple points to many. We show applications of this notion (1) to speeding up the proof computation by using precomputation and moderate-sized precomputed values and (2) to Verifiable Decentralized Storage. Finally, we provide efficient construction of Incrementally Aggregatable Vector Commitments from Groups of Unknown Order. Thirdly, we turn to Functional Commitments, for linear functions, where one commits to a vector v and then can open f(v) = y, for a public f. We construct functional commitments that admit constant-sized public parameters and proofs. To this end, our core technique is a novel succinct protocol of cardinality for a set committed with an RSA accumulator, which is in turn based on a Range Proof. Finally, we show a generic way to turn any Vector Commitment into a Key-Value Map Commitment for arbitrary keys. A Key-Value Map resembles a Vector but the ordering of the values is not characterized by subsequent indices but by arbitrary keys. Key-Value Maps are the core data-structures in Cryptocurrencies like Ethereum. Our construction of Key-Value Map Commitments is generic and is based on a novel cryptographic application of Cuckoo-Hashing. RESUMEN La última década se ha caracterizado por la aparición de la tecnología Blockchain, afectando la criptografía. El manifiesto de Blockchains es la Descentralización, en la que la información es guardada y verificada en tiempo real por miles de participantes. Esto centra la atención en dos necesidades: la información debe ser lo más concisa posible y la verificación de los procesos debe ser rápida. Desde una perspectiva criptográfica, esto se traduce en un desiderátum central: la Compacidad. En esta tesis, nos ocupamos de un tipo específico de primitivas criptográficas compactas: Compromisos Compactos. Los compromisos criptográficos son objetos que permiten comprom- eterse con algunos datos, proporcionando un representante vinculante, de modo que en cualquier momento posterior se pueden volver a abrir, proporcionando una prueba de apertura. En nuestro trabajo tratamos compromisos con aperturas más detalladas, donde se puede generar una prueba de apertura del compromiso con una función f (m) de los datos iniciales m. En primer lugar, nos ocupamos de compromisos de conjuntos con aperturas privadas de (no) pertenencia. Construimos pruebas compactas de conocimiento cero para el problema de la (no) pertenencia a conjuntos. Una prueba de conocimiento cero es una primitiva criptográfica que permite probar una afirmación, de forma sólida, sin filtrar ninguna otra información excepto el hecho de que la afirmación es cierta. En una prueba de conocimiento cero para la pertenencia a un conjunto, primero uno se compromete con un conjunto público y luego una parte puede demostrar la pertenencia al conjunto, pero sin revelar qué elemento del conjunto es exactamente. Estos compromisos de conjuntos con este tipo de aperturas detalladas son la piedra angular de las criptomonedas anónimas como Zcash. En particular, proporcionamos pruebas eficientes de conocimiento cero para la apertura de acumuladores RSA, uno de los compromisos establecidos más populares. Primero, mostramos protocolos eficientes para la membresía y no membresía de un solo elemento. Luego construimos pruebas compactas de membresía de conocimiento cero para múltiples elementos, donde el tamaño de la prueba es independiente del número de elementos probados. Las dos técnicas son cualitativamente diferentes. En segundo lugar, centramos nuestra atención en los compromisos de vectores, con aperturas posicionales locales. Presentamos la noción de Agregación Incremental, en la que se pueden agregar arbitrariamente pruebas de apertura de cualquier posición en una prueba única (concisa) e inversamente desagregar una prueba de múltiples puntos en muchos. Mostramos aplicaciones de esta noción (1) para acelerar el cálculo de la prueba mediante el uso de precómputo y valores precalculados de tamaño moderado y (2) para el Almacenamiento Descentralizado Verificable. Finalmente, proporcionamos una construcción eficiente de compromisos vectoriales incrementalmente agregables a partir de grupos de orden desconocido. En tercer lugar, pasamos a los compromisos funcionales, para funciones lineales, donde uno se compromete con un vector v y luego puede abrir f(v) = y, para un f público. Construimos compromisos funcionales que admiten pruebas y parámetros públicos de tamaño constante. Con este fin, nuestra técnica principal es un protocolo novedoso y compacto de cardinalidad para un conjunto comprometido con un acumulador RSA, que a su vez se basa en una prueba de rango. Finalmente, mostramos una forma genérica de convertir cualquier compromiso de vector en un compromiso de mapa-de-valores-clave para claves arbitrarias. Un mapa-de-valores-clave se parece a un vector, pero el orden de los valores no se caracteriza por índices posteriores sino por claves arbitrarias. Los mapas-de-valores-clave son las estructuras de datos centrales en criptomonedas como Ethereum. Nuestra construcción de compromisos de mapas de valores clave es genérica y se basa en una novedosa aplicación criptográfica de Cuckoo-Hashing.
This article presents a secure framework for remote healthcare monitoring in the context of home isolation, thereby addressing the concerns related to untrustworthy client connections to a hospital information system (HIS) within a secure network. Our proposed solution leverages a public blockchain network as a secure distributed database to buffer and transmit patient vital signs. The framework integrates an algorithm for the secure gathering and transmission of vital signs to the Ethereum network. Additionally, we introduce a publish/subscribe paradigm, thus enhancing security using the TLS channel to connect to the blockchain network. An analysis of the maintenance cost of the distributed database underscores the cost-effectiveness of our approach. In conclusion, our framework provides a highly secure and economical solution for remote healthcare monitoring in home isolation scenarios.
The rapid expansion of the Internet of Things (IoT) has introduced significant challenges in data authentication, necessitating a balance between scalability and security. Traditional approaches often rely on third parties, while blockchain-based solutions face computational and storage bottlenecks. Our novel framework employs edge aggregating servers and Ethereum Layer 2 rollups, offering a scalable and secure IoT data authentication solution that reduces the need for continuous, direct interaction between IoT devices and the blockchain. We utilize and compare the Nova and Risc0 proving systems for authenticating batches of IoT data by verifying signatures, ensuring data integrity and privacy. Notably, the Nova prover significantly outperforms Risc0 in proving and verification times; for instance, with 10 signatures, Nova takes 3.62 s compared to Risc0's 369 s, with this performance gap widening as the number of signatures in a batch increases. Our framework further enhances data verifiability and trust by recording essential information on L2 rollups, creating an immutable and transparent record of authentication. The use of Layer 2 rollups atop a permissionless blockchain like Ethereum effectively reduces on-chain storage costs by approximately 48 to 57 times compared to direct Ethereum use, addressing cost bottlenecks efficiently.
Abstract Scientific workflows are essential for many applications, enabling the configuration and execution of complex tasks across distributed resources. In this paper, we contribute an Ethereum blockchain-based scientific workflow execution manager, which distributes workflows to run on cluster computing providers that utilize the Slurm workload manager to execute them. We extended our blockchain-based autonomous resource broker called eBlocBroker, which is a DAO-based decentralized coordinator, by providing distributed workflow execution via blockchain. Through various tests, we demonstrate how our eBlockBroker autonomous organization, which is programmed as a smart contract, can manage scientific workflow submission, scheduling, and execution on cluster computing providers. The utilization of blockchain for distributed workflow execution is a new concept. We are motivated because our system has been developed with e-Science in mind where scientific workflows are widely utilized.
Abstract This paper presents a novel method for decentralized storage in deep-learning-based face recognition systems using the Hierarchical Navigable Small World (HNSW) algorithm. The proposed solution utilizes Ethereum smart contracts, which acts as highly available data storage systems for storing identifiable data for authorized personnel. In addition, the solution is integrated with a centralized vector database that is in charge of vector indexing, searching and associating face embeddings to an identity on the Ethereum blockchain with anonymous hashes. Vector indexing and search processes involve different machine learning algorithms that enable computations to be carried out in a reasonable time with good matching accuracy. Specifically, we compared different approaches and selected the HNSW algorithm. Accordingly, we successfully implemented a prototype of a reliable and privacy-focused decentralized face identification system for areas under government surveillance, such as customs inspection sites. In our measurements, the system could handle 20,000 face vectors easily with high matching accuracy, and the performance could be further improved using more powerful hardware. Finally, we also propose additional methods to further scale up the system to handle millions of face vectors.
Hongyu Guo, Haozhe Liang, Ju Huang, Wei Ou · 7 authors
The proliferation of blockchain technology has resulted in diverse token standards, posing challenges for compatibility, security, and performance in existing cross-chain bridges. This paper introduces a novel framework capable of concurrently facilitating fungible token exchange, as well as the processing of both individual and batch non-fungible tokens (NFTs). We deploy token bridges that meet different token standards to support cross-chain staking and unlocking of ERC20, ERC721, and ERC1155. To minimize both waiting times and handling fees, we relocate processes necessitating frequent transactions and verifications to the sidechain. Additionally, we adopt a batch-processing approach for tokens necessitating cross-chain transfers, leveraging payment channels to facilitate efficiency. The system’s reliability is upheld through the validator group. Validators acquire an initial reputation value by making deposits and enhance both their rewards and reputation by successfully completing NFT auction tasks on the sidechain. We use OpenZeppelin’s security library functions to standardize token operations, and carefully design the validator’s reward, punishment, and reputation mechanisms. Our comprehensive contract security audit and system analysis validate our solution’s effectiveness in mitigating common vulnerabilities and internal threats. Implementation and testing with Ethereum and its test network demonstrate substantial reductions in transmission time for key cross-chain token steps by nearly half. Moreover, our framework showcases efficiency and cost-effectiveness with an average gas cost of 693,379.
This article investigates the Ethereum Merge, which occurred on 15 September 2022, and we employ the time-series difference in differences (DiD) model and vector autoregression (VAR) models and analyse how the protocol change from proof-of-work to proof-of-stake (PoS) affects the dynamic relationship between cryptocurrency returns and network factors. The results show that the Merge caused a structural change between Ethereum and Bitcoin networks. The network factors of Ethereum show a significant increase compared to Bitcoin, the cointegration has been strengthened and the lag length is shortened after the Merge. The spillover effect on the Bitcoin network can be seen from both DiD and VAR, indicating the increasing impact of the Ethereum network on Bitcoin. The concern of losing the number of participants due to the implantation of PoS on cryptocurrency is not apparent on Ethereum Merge, and it increases the investors’ attention and involvement.
Kausthav Pratim Kalita, Debojit Boro, Dhruba K. Bhattacharyya
Abstract The rise of technology has resulted in the evolution of data generation at a rapid speed. With the high increase in the volume of data, it has become necessary to store it using reliable and scalable data management systems. Blockchain offers a storage structure that ensures the security and reliability of the stored data. Smart contracts further enhance the technology by enforcing more stringent record management activities. In recent times, there has been widespread utilization of IPFS in conjunction with blockchain technology. This utilization facilitates the establishment of decentralized and distributed data storage, as well as the connection of blockchain transactions to external data, thereby enhancing scalability and reducing storage expenses within blockchain applications. This paper introduces an effective collaborative ecosystem called SSE_CIB, where images undergo multiple operations including quantization before getting uploaded to IPFS. The image‐related details are stored in the blockchain to keep track of record entries. In our approach, a watermarking process has also been included to ensure the protection of copyright. Further, the images undergo block‐wise rotation based on a secret key and bit‐wise operation with a key image for enhanced security and protection. The work is implemented and tested using real‐life images in an Ethereum environment incorporated with a smart contract that enables proper execution of transactions.
Open access
Advanced Steganography and Watermarking Techniques
Several studies were conducted to demonstrate the application of Process Mining (PM) techniques to Ethereum-compatible application event data. However, the availability of event data is constrained by the application’s process awareness, which is under-reported in the literature. Based on domain analysis, which identified several challenges to mining the business process from blockchain applications, a framework was designed, instantiated, and tested in this study. The framework supports identification of appropriate cases for PM and automates the generation of event logs from blockchain data. It consists of two modules, the Process Awareness Recognizer (PAR) and the Event Log Generator (ELG). PAR is a rule-based classifier to assess the process awareness of a given application. ELG is an automated batch processing model consisting of three methods: (1) Extractor: to retrieve event data from blockchains; (2) Decoder: to transform the extracted data to a human-readable format; and (3) Formatter: to produce event log files in a format compatible with PM tools. It was validated by implementing a proof-of-concept application with an input set of 201 real-world applications. The results prove the framework’s feasibility and applicability.
The significant increase in cryptocurrency trading on digital blockchain platforms has led to a growing interest in employing machine learning techniques for the effective prediction of highly nonlinear and nonstationary data, becoming increasingly popular among both individual and institutional market participants. The aim of this research is to deal with the challenging task of predicting the closing prices of two prominent cryptocurrencies, Binance Coin (BNB) and Ethereum (ETH), utilizing machine-learning techniques. This study evaluates the efficacy of various machine learning models in predicting cryptocurrency prices, with a particular focus on Support Vector Machines for Regression (SVR), least-squares Boosting (LSBoost), and Artificial Neural Networks and Adaptive Neuro-Fuzzy Inference System (ANFIS). These models are compared under various metrics. ANFIS models exhibited superior predictive performance on both training and testing datasets based on diverse performance metrics. Comparatively, SVR with a linear kernel demonstrated strong generalization capabilities, particularly on the testing set. LSBoost, while showing promise in training accuracy, indicated results with higher test errors. ANN models maintained a balance between training and testing. This comparison showed the models’ effectiveness, particularly the robustness of ANFIS in capturing the volatile cryptocurrency market trends. The experimental data suggest that certain of the above models can be utilized to predict the ETH and BNB closing price in real time with promising accuracy and experimentally proven profitability.
With the rise of "Metaverse" and "Web 3.0", Non-Fungible Token (NFT) has emerged as a kind of pivotal digital asset, garnering significant attention. By the end of March 2024, more than 1.7 billion NFTs have been minted across various blockchain platforms. To effectively locate a desired NFT, conducting searches within a vast array of NFTs is essential. The challenge in NFT retrieval is heightened due to the high degree of similarity among different NFTs, regarding regional and semantic aspects. In this paper, we will introduce a benchmark dataset named "NFT Top1000 Visual-Text Dataset" (NFT1000), containing 7.56 million image-text pairs, and being collected from 1000 most famous PFP1 NFT collections2 by sales volume on the Ethereum blockchain. Based on this dataset and leveraging the CLIP series of pre-trained models as our foundation, we propose the dynamic masking fine-tuning scheme. This innovative approach results in a 7.4\% improvement in the top1 accuracy rate, while utilizing merely 13\% of the total training data (0.79 million vs. 6.1 million). We also propose a robust metric Comprehensive Variance Index (CVI) to assess the similarity and retrieval difficulty of visual-text pairs data. The dataset will be released as an open-source resource. For more details, please refer to: https://github.com/ShuxunoO/NFT-Net.git.
Cryptocurrencies have become a popular investment option and the Ethereum has become a mainstream cryptocurrency because of the additional functionality that can be accomplished with the backing of the powerful Ethereum network compared to Bitcoin. The high volatility of Ethereum offers both profits and risks, making it crucial to improve the forecasting ability for its price volatility. The results of this study could be useful for investors and policymakers who are interested in understanding and managing the risks associated with investing in Ethereum. Several studies have explored similar topics using heterogeneous autoregressive (HAR) models for cryptocurrencies, but this paper offers a more expansive approach. This paper employs five-minute high-frequency data to construct 4 HAR models to predict the volatility of Ethereum, taking into account the impact of structural breaks, Bitcoin, SP500 and VIX. The model that considers all factors outperforms other models for out-of-sample predictions for the 1-week forecasting. Due to the nature of the Ethereum price, the HAR-RV model has achieved a perfect fit in 1-day and 1-month forecasting. Therefore, other models have a very small improvement in fitness and prediction accuracy. This paper contributes to the understanding of Ethereum’s volatility and its impact on the cryptocurrency market.
Krzysztof Gogol, Benjamin Kraner, Malte Schlosser, Tao Yan · 6 authors
Liquid staking has become the largest category of decentralized finance protocols in terms of total value locked. However, few studies exist on its implementation designs or underlying risks. The liquid staking protocols allow for earning staking rewards without the disadvantage of locking the capital at the validators. Yet, they are seen by some as a threat to the Proof-of-Stake blockchain security. This paper is the first work that classifies liquid staking implementations. It analyzes the historical performance of major liquid staking tokens in comparison to the traditional staking for the largest Proof-of-Stake blockchains. Furthermore, the research investigates the impact of centralization, maximum extractable value and the migration of Ethereum from Proof-of-Work to Proof-of-Stake on the tokens' performance. Examining the tracking error of the liquid stacking providers to the staking rewards shows that they are persistent and cannot be explained by macro-variables of the currency, such as the variance or return.
In the fourth industrial revolution era of today, individuals encounter an immense volume of information daily. The digital world is rich in data like IoT, social media, healthcare, business, cryptocurrencies, cybersecurity, etc. The situation can become problematic as these vast amounts of data require significant storage capacity, which leads to challenges in executing tasks such as analytical operations, processing operations, and retrieval operations that are time-consuming and arduous. To effectively analyze and utilize this data, artificial intelligence, particularly machine learning, and deep learning, can provide a practical solution. Clustering, an unsupervised learning technique, aims to identify a specific number of clusters to effectively categorize the data through data grouping. Hence, clustering is related to many fields and is used in various applications that deal with large datasets. This survey examines seven widely recognized clustering techniques, namely k -means, G -means, DBSCAN, Agglomerative hierarchical clustering, Two-stage density (DBSCAN and k -means) algorithm, Two-levels (DBSCAN and hierarchical) clustering algorithm, and Two-stage MeanShift and k -means clustering algorithm and compares them with a real dataset - The Blockchain dataset, including prominent cryptocurrencies like Binance, Bitcoin, Doge, and Ethereum, under several metrics such as silhouette coefficient, Calinski-Harabasz, Davies-Bouldin Index, time complexity, and entropy.
With the ever-increasing advancement in blockchain technology, security is a significant concern when substantial investments are involved. This paper explores known smart contract exploits used in previous and current years. The purpose of this research is to provide a point of reference for users interacting with blockchain technology or smart contract developers. The primary research gathered in this paper analyses unique smart contracts deployed on a blockchain by investigating the Solidity code involved and the transactions on the ledger linked to these contracts. A disparity was found in the techniques used in 2021 compared to 2023 after Ethereum moved from a Proof-of-Work blockchain to a Proof-of-Stake one, demonstrating that with the advancement in blockchain technology, there is also a corresponding advancement in the level of effort bad actors exert to steal funds from users. The research concludes that as users become more wary of malicious smart contracts, bad actors continue to develop more sophisticated techniques to defraud users. It is recommended that even though this paper outlines many of the currently used techniques by bad actors, users who continue to interact with smart contracts should consistently stay up to date with emerging exploitations.
Blockchain technology has been incorporated into the Healthcare Internet of Things (IoT) landscape as a revolutionary solution to tackle issues related to the sharing of medical records. This paper presents an innovative method that utilizes Temporal Blockchain for the purpose of Provenance Tracking. The introductory section provides context by delineating the significance of trust and transparency in medical data sharing within the healthcare IoT ecosystem. The study examines current blockchain solutions, delving into frameworks such as Hyperledger Fabric, Ethereum, Corda, and specialized approaches like temporal blockchain. The paper examines the difficulties associated with tracking the origin of data, concerns regarding privacy, problems related to scalability, and the need to comply with regulations. These challenges provide the context for the proposed methodology. The main emphasis is on Temporal Blockchain, integrating temporal elements to improve the tracking of origin and history. The evaluation parameters, such as security, provenance tracking, scalability, interoperability, privacy compliance, and performance, undergo a thorough assessment. The attained values demonstrate a strong emphasis on security at a high level, thorough tracking of origin and history, and strict adherence to privacy regulations. Nevertheless, the need for scalability and interoperability necessitates meticulous consideration. The study showcases the capacity of Temporal Blockchain to establish trust and enhance transparency in the sharing of medical records. The future scope focuses on tackling scalability challenges, improving interoperability, and making continuous optimization efforts. The proposed approach highlights notable accomplishments and emphasizes the continuous development and collaborative aspect of Blockchain-Based Medical Record Sharing in Healthcare IoT.
Open access
Blockchain Technology Applications and Security
Privacy-Preserving Technologies in Data
Artificial Intelligence in Healthcare and Education
Blockchain performance has historically faced challenges posed by the throughput limitations of consensus algorithms. Recent breakthroughs in research have successfully alleviated these constraints by introducing a modular architecture that decouples consensus from execution. The move toward independent optimization of the consensus layer has shifted attention to the execution layer. While concurrent transaction execution is a promising solution for increasing throughput, practical challenges persist. Its effectiveness varies based on the workloads, and the associated increased hardware requirements raise concerns about undesirable centralization. This increased requirement results in full nodes and stragglers synchronizing from signed checkpoints, decreasing the trustless nature of blockchain systems. In response to these challenges, this paper introduces Chiron, a system designed to extract execution hints for the acceleration of straggling and full nodes. Notably, Chiron achieves this without compromising the security of the system or introducing overhead on the critical path of consensus. Evaluation results demonstrate a notable speedup of up to 30%, effectively addressing the gap between theoretical research and practical deployment. The quantification of this speedup is achieved through realistic blockchain benchmarks derived from a comprehensive analysis of Ethereum and Solana workloads, constituting an independent contribution.
The rapid development of physical device-based data collection in emerging technology needs smart, secure, and intelligent transmission. Cyber physical systems compete with the requirement of intelligent transmission of data. In cyber physical systems, security is a very challenging task due to the heterogeneous connections of devices in real time. This paper proposes a novel methodology for cyber-attack finding in cyber physical systems. The proposed system employed a DNN-deep neural network for the categorization of normal and attack data. The employed deep neural network design for 4 hidden layers for the detection of anomalies. For the secured transmission, we employed the blockchain process in Ethereum. The process of Ethereum generates blocks of blockchain with headers and transmits data over the cyberworld to the physical world with the alteration of data. For the authentication of the projected algorithm tested on two real-time datasets, such as NSL-KDD15 and CIDDS_001. The working of proposed algorithm is very promising in compression of existing algorithms of deep learning like RNN-recurrent neural networks, DBN, and DNN.
Iván Abellán Álvarez, Vincent Gramlich, Johannes Sedlmeir
With the increasing adoption of decentralized information systems based on a variety of permissionless blockchain networks, the choice of consensus mechanism is at the core of many controversial discussions. Ethereum's recent transition from proof-of-work (PoW) to proof-of-stake (PoS)-based consensus has further fueled the debate on which mechanism is more favorable. While the aspects of energy consumption and degree of (de-)centralization are often emphasized in the public discourse, seminal research has also shed light on the formal security aspects of both approaches individually. However, related work has not yet comprehensively structured the knowledge about the security properties of PoW and PoS. Rather, it has focused on in-depth analyses of specific protocols or high-level comparative reviews covering a broad range of consensus mechanisms. To fill this gap and unravel the commonalities and discrepancies between the formal security properties of PoW- and PoS-based consensus, we conduct a systematic literature review over 26 research articles. Our findings indicate that PoW-based consensus with the longest chain rule provides the strongest formal security guarantees. Nonetheless, PoS can achieve similar guarantees when addressing its more pronounced tradeoff between safety and liveness through hybrid approaches.
Barış Coşkunuzer, Ignacio Segovia-Domínguez, Yuzhou Chen, Yulia R. Gel
Learning time-evolving objects such as multivariate time series and dynamic networks requires the development of novel knowledge representation mechanisms and neural network architectures, which allow for capturing implicit time-dependent information contained in the data. Such information is typically not directly observed but plays a key role in the learning task performance. In turn, lack of time dimension in knowledge encoding mechanisms for time-dependent data leads to frequent model updates, poor learning performance, and, as a result, subpar decision-making. Here we propose a new approach to a time-aware knowledge representation mechanism that notably focuses on implicit time-dependent topological information along multiple geometric dimensions. In particular, we propose a new approach, named \textit{Temporal MultiPersistence} (TMP), which produces multidimensional topological fingerprints of the data by using the existing single parameter topological summaries. The main idea behind TMP is to merge the two newest directions in topological representation learning, that is, multi-persistence which simultaneously describes data shape evolution along multiple key parameters, and zigzag persistence to enable us to extract the most salient data shape information over time. We derive theoretical guarantees of TMP vectorizations and show its utility, in application to forecasting on benchmark traffic flow, Ethereum blockchain, and electrocardiogram datasets, demonstrating the competitive performance, especially, in scenarios of limited data records. In addition, our TMP method improves the computational efficiency of the state-of-the-art multipersistence summaries up to 59.5 times.
This study examines whether precious metals, industrial metals, energy and agricultural commodities, or cryptocurrencies form trustworthy safe havens against extreme price volatility of major global bank stock indices during black-swan events such as the COVID-19 pandemic and the Russia-Ukraine conflict. Using daily data and applying Quantile-VAR dynamic pairwise and extended joint connectedness methodologies, we investigate dynamic connectedness between major financial assets and major bank indices during exceptional crises. Findings provide evidence that crude oil and both Ethereum and Bitcoin present evidence of propagating significant shocks towards bank stock indices during crises, but other large-cap cryptocurrencies present no evidence of any specific influence. Further, gold, natural gas, and wheat are identified as the main absorbers of spillovers from banking indices during analysed crises, with more pronounced effects identified during exceptional phases of volatility. Such findings suggest that risk in the banking sector can be efficiently hedged by traditional safe havens such as gold and counterbalanced by highly outperforming assets such as natural gas and wheat. The study significantly contributes to understanding the interplay between banking sectors and various financial assets during crises and the subsequent strategies available for managing systemic risks, providing valuable insights for policymakers, regulators, and investors alike.