Nidhi Desai, Damiano Di Francesco Maesa, Nishanth Sastry, Steve Schneider · 5 authors
This paper is concerned with helping people who are vulnerable during important transitions in life, such as 'coming out' as LGBTQIA+, experiencing serious illness, undergoing relationship breakdown etc. Rich sensor streams derived from so-called 'smart' Internet of Things (IoT) devices can be highly beneficial, for example in ensuring the safety of such individuals during their sensitive life transitions, or in providing functionality that can mitigate some of the difficulties faced by them. However, the data that needs to be extracted to provide these benefits can itself be highly sensitive and needs to be processed with safeguards to protect privacy. We develop scenarios that highlight issues arising from having to merge data streams from multiple devices, including data governance issues that are relevant when the sensors are owned by multiple individuals. We propose a "Transition Guardian" architecture that leverages "Smart Experts" written as smart contracts operating on homomorphically encrypted sensor data streams to provide real-time protection without disclosing their sensitive information. We have also implemented a proof-of-concept on the Ethereum protocol to validate our proposed solution.
Data deduplication is a valuable technique for compressing and minimizing data duplication during data transfers, especially in cloud environments. By eliminating redundant data, it optimizes transmission capacity and reduces memory usage. To ensure the integrity of sensitive data, encryption is applied throughout the deduplication process. The SHA algorithm is commonly used for storing text data during deduplication. It generates security bits by padding the text and computes a hash consisting of hexadecimal, string, and integer data. Hash-based deduplication involves hashing the entire file and treating the hash values of text data as unique identifiers. This allows clients to identify duplicate data within the cloud. The Proof-of-Work (PoW) algorithm is widely utilized in blockchain networks like Bitcoin and Ethereum. Its primary function is to verify transactions through a process called mining. Miners engage in competition to solve complex mathematical problems, and the first one to find a solution is granted the right to add a new block to the blockchain. PoW relies on cryptographic hashing, such as the SHA-256 hashing function, to validate and secure transactions. On the other hand, the Proof of Retrievability (PoR) algorithm finds application in cloud computing systems. It serves as a consensus mechanism, ensuring that cloud storage providers store and retrieve data accurately. PoR enables cloud providers to demonstrate to consumers that their files can be fully recovered. This algorithm incorporates cryptographic proofs that validate the integrity and availability of the stored data. In cloud storage, deduplication is often implemented using the Memory mapping technique (MPT). This allows multiple data owners to store the same data in a single copy, enhancing storage efficiency. To maintain data security, encryption is applied both before and during the deduplication process, ensuring that sensitive information remains protected. In summary, data deduplication is a powerful method for compressing data, minimizing duplication, and optimizing transmission capacity. With encryption techniques and hash-based identification, it provides secure and efficient deduplication in cloud environments, while memory deduplication and MPT support further enhance performance and storage efficiency.
There is a growing interest in understanding the energy and environmental footprint of digital currencies, specifically in cryptocurrencies such as Bitcoin and Ethereum. These cryptocurrencies are operated by a geographically distributed network of computing nodes, making it hard to estimate their energy consumption accurately. Existing studies, both in academia and industry, attempt to model cryptocurrency energy consumption often based on a number of assumptions, for instance, about the hardware in use or the geographic distribution of the computing nodes. A number of these studies have already been widely criticized for their design choices and subsequent over- or under-estimation of energy use. In this study, we evaluate the reliability of prior models and estimates by leveraging existing scientific literature from fields cognizant of blockchain, such as social energy sciences and information systems. We first design a quality assessment framework based on existing research, and we then conduct a systematic literature review examining scientific and non-academic literature demonstrating common issues and potential avenues of addressing these issues. Our goal with this article is to to advance the field by promoting scientific rigor in studies focusing on blockchain energy footprint. To that end, we provide a novel set of codes of conduct for the five most widely used research methodologies: quantitative energy modeling, literature reviews, data analysis and statistics, case studies, and experiments. We envision that this code of conduct would assist in standardizing the design and assessment of studies focusing on blockchain-based systems' energy and environmental footprint.
This paper proposes a novel recursive polynomial commitment scheme (PCS) and a new polynomial interactive oracle proof (PIOP) protocol, which compile into efficient and transparent zk-SNARKs (zero-knowledge succinct non-interactive arguments of knowledge). The Ethereum blockchain utilizes zero-knowledge Rollups (ZKR) to improve its scalability (the ability to handle a large number of transactions), and ZKR uses zk-SNARKs to validate transactions. The currently used zk-SNARKs rely on a trusted setup ceremony, where a group of participants uses secret information about transactions to generate the public parameters necessary to verify the zk-SNARKs. This introduces a security risk into Ethereum's system. Thus, researchers have been developing transparent zk-SNARKs (which do not require a trusted setup), but those are not as efficient as non-transparent zk-SNARKs, so ZKRs do not use them. In this research, I developed LUMEN, a set of novel algorithms that generate transparent zk-SNARKs that improve Ethereum's security without sacrificing its efficiency. Various techniques were creatively incorporated into LUMEN, including groups with hidden orders, Lagrange basis polynomials, and an amortization strategy. I wrote mathematical proofs for LUMEN that convey its completeness, soundness and zero-knowledgeness, and implemented LUMEN by writing around $8000$ lines of Rust and Python code, which conveyed the practicality of LUMEN. Moreover, my implementation revealed the efficiency of LUMEN (measured in proof size, proof computation time, and verification time), which surpasses the efficiency of existing transparent zk-SNARKs and is on par with that of non-transparent zk-SNARKs. Therefore, LUMEN is a promising solution to improve Ethereum's security while maintaining its efficiency.
We propose a secure, stateless and composable transaction scheme to establish delivery-versus-payment (DvP) across two (or more) blockchains without relying on time-locks, centralized escrow, or stateful intermediaries. The method minimizes coordination overhead and removes race conditions via a stateless decryption oracle that conditionally releases cryptographic keys. Specifically, the scheme requires: 1) a decryption oracle service (either centralized or using threshold decryption) that decrypts transaction-specific encrypted messages, and 2) a payment contract on the payment chain that executes conditional payments via transferAndDecrypt and emits the appropriate key, depending on transaction outcome (finality). The decrypted key then deterministically enables follow-up transactions - such as asset delivery or cancellation - on a separate blockchain. The protocol is lightweight and compatible with existing blockchain infrastructure (e.g., Ethereum), and avoids timeouts or pre-defined orderings. Our approach improves atomic cross-chain settlement and can serve as a blueprint for decentralized inter-chain financial markets. The protocol allows for multi-party DvP across multiple chains. A multi-party delivery versus payment is a valuable trade feature as it allows to bound multiple trade into a single atomic unit, effectively reducing liquidity requirements.
This paper presents the design and preliminary development a new model based on blockchain for improving brand loyalty in agribusiness by customer reward. Using facilities of Ethereum Network and solidity programing, we have created an Agri non-fungible token (NFT) according to ERC-721 protocol that is most widely used protocol for creating NFTs on Ethereum. One of the main advantages of ERC-721 is its flexibility, as it allows developers to define custom metadata for each NFT and customize the attributes. The interaction between Ethereum nodes and the user is established via the frontend, which is developed in Nodejs and connects to the Ethereum network. In previous works on loyalty systems, blockchain-based solutions were proposed that resemble the system developed in this study but there are significant differences between our work and other related works such as: the use of NFT and personalization for customers, the adoption of a new version of Solidity, as well as the adoption of the metadata feature for using media to encourage and assist customer brand awareness and repurchase. We tried to provide a safe and transparent way to use NFTs in the way of increasing brand loyalty in agricultural businesses. Due to characters of NFTs as uniqueness and personalization, customers in a loyalty cycle prefer to shop more in order to earn more AgriNFT, causing retailer to sell more items and manufacturers to generate more goods.
Mikel Cortes-Goicoechea, Tarun Mohandas-Daryanani, José L. Muñoz, Leonardo Bautista-Gomez
Scalability is a common issue among the most used permissionless blockchains, and several approaches have been proposed to solve this issue. Tackling scalability while preserving the security and decentralization of the network is a significant challenge. To deliver effective scaling solutions, Ethereum achieved a major protocol improvement, including a change in the consensus mechanism towards Proof of Stake. This improvement aimed a vast reduction of the hardware requirements to run a node, leading to significant sustainability benefits with a lower network energy consumption. This work analyzes the resource usage behavior of different clients running as Ethereum consensus nodes, comparing their performance under different configurations and analyzing their differences. Our results show higher requirements than claimed initially and how different clients react to network perturbations. Furthermore, we discuss the differences between the consensus clients, including their strong points and limitations.
Open access
3 source records
Intracranial Aneurysms: Treatment and Complications
Arlen Brickman, Yiğit Baykara, Miguel Carabaño, Sean Hacking
Background: Distributed ledger technology (DLT) enables the creation of tamper-resistant, decentralized, and secure digital ledgers. A non-fungible token (NFT) represents a record on-chain associated with a digital or physical asset, such as a whole-slide image (WSI). The InterPlanetary File System (IPFS) represents an off-chain network, hypermedia, and file sharing peer-to-peer protocol for storing and sharing data in a distributed file system. Today, we need cheaper, more efficient, highly scalable, and transparent solutions for WSI data storage and access of medical records and medical imaging data. Methods: WSIs were created from non-human tissues and H&E-stained sections were scanned on a Philips Ultrafast WSI scanner at 40× magnification objective lens (1 μm/pixel). TIFF images were stored on IPFS, while NFTs were minted on the Ethereum blockchain network in ERC-1155 standard. WSI-NFTs were stored on MetaMask and OpenSea was used to display the WSI-NFT collection. Filebase storage application programing interface (API) were used to create dedicated gateways and content delivery networks (CDN). Results: A total of 10 WSI-NFTs were minted on the Ethereum blockchain network, found on our collection "Whole Slide Images as Non-fungible Tokens Project" on Open Sea: https://opensea.io/collection/untitled-collection-126765644. WSI TIFF files ranged in size from 1.6 to 2.2 GB and were stored on IPFS and pinned on 3 separate nodes. Under optimal conditions, and using a dedicated CDN, WSI reached retrieved at speeds of over 10 mb/s, however, download speeds and WSI retrieval times varied significantly depending on the file and gateway used. Overall, the public IPFS gateway resulted in variably poorer WSI download retrieval performance compared to gateways provided by Filebase storage API. Conclusion: Whole-slide images, as the most complex and substantial data files in healthcare, demand innovative solutions. In this technical report, we identify pitfalls in IPFS, and demonstrate proof-of-concept using a 3-layer architecture for scalable, decentralized storage, and access. Optimized through dedicated gateways and CDNs, which can be effectively applied to all medical data and imaging modalities across the healthcare sector. DLT and off-chain network solutions present numerous opportunities for advancements in clinical care, education, and research. Such approaches uphold the principles of equitable healthcare data ownership, security, and democratization, and are poised to drive significant innovation.
One of the technological advances found in the economic field is the emergence of crypto currency or also known as cryptocurrency, one type of which is ethereum. The progressivity of these transactions makes transaction activities more efficient because they can be applied virtually. When viewed in terms of profit obtained, ethereum can provide significant returns, but on the other hand also has a very high potential risk in terms of investment. The extreme volatility of ethereum digital transactions allows for spikes in price increases and decreases very quickly. The high volatility in question is a reflection of the level of risk faced by investors. This research was conducted to review the essentiality of ethereum digital transactions in the perspective of Islamic economics, because these transactions are still experiencing debate both in terms of legality and regulation, especially from the perspective of Islamic economics.
Eduardo Jorge Lira Antunes da Silva, Cristina Souza de Araújo, Jucimar Maia da Silva Júnior, Roberto Junio Rodrigues Gomes · 5 authors
The importance of developing software that keeps pace with Web3 technologies is crucial in fostering discussions about their utilization in the current scientific landscape. Within this context, this research aims to present the development of a "criptogame" that utilizes blockchain technology to incorporate NFTs (Non-Fungible Tokens) into its mechanics. The text addresses fundamental concepts of Web3, making references to relevant literature. Subsequently, the development process is described, including specific methodologies and models. The obtained results consist of a puzzle game that incorporates NFTs as playable characters and enables the transfer of these assets between digital wallets.
James Austgen, Andrés Fábrega, Sarah Levin Allen, Kushal Babel · 6 authors
Decentralized Autonomous Organizations (DAOs) use smart contracts to foster communities working toward common goals. Existing definitions of decentralization, however-the 'D' in DAO-fall short of capturing key properties characteristic of diverse and equitable participation. We propose a new metric called Voting-Bloc Entropy (VBE, pronounced ''vibe'') that formalizes a broad notion of decentralization in voting on DAO proposals. VBE measures the similarity of participants' utility functions across a set of proposals. We use VBE to prove a number of results about the decentralizing effects of vote delegation, proposal bundling, bribery, and quadratic voting. Our results lead to practical suggestions for enhancing DAO decentralization. One of our results highlights the risk of systemic bribery with increasing DAO decentralization. To show that this threat is realistic, we present the first practical realization of a Dark DAO, a proposed mechanism for privacy-preserving corruption of identity systems, including those used in DAO voting. Our Dark-DAO prototype uses trusted execution environments (TEEs) in the Oasis Sapphire blockchain for attacks on Ethereum DAOs. It demonstrates that Dark DAOs constitute a realistic future concern for DAO governance.
Computer networks and internet services are increasingly threatened by attacks like Distributed Denial-of-Service (DDoS). DDoS attack mitigation techniques now in use are ineffective due to a lack of resources and a lack of adaptability. Using blockchains like Ethereum, DDoS attacks can be thwarted in innovative ways. With smart contracts, it is possible to track down the IP addresses of attackers without additional hardware. This study examines blockchain-based solutions to combat DDoS attacks for feasibility, effectiveness, as well as cost and performance. The cost model delves into economic aspects like gas, gas price, and Ether value. In it, the evaluation of various smart contracts for the signalization of DDoS attacks is documented and compared to assess three system variants, analyzing gas costs, deployment, speed, and accuracy. It also details Ethereum's ecosystem and how that affects smart contract design and it also acknowledges scalability challenges and suggests outsourcing data for a more scalable solution, advocating for specialized blockchains for DDoS signaling applications. The analysis provides insights into the gas costs associated with different variants, considering various scenarios and highlighting the trade-offs and efficiencies of each approach.
This article examines the causal relationship between stock indices and cryptocurrencies during the ongoing Russia-Ukraine war. The econometric investigation covers the period from February 24, 2022 to April 12, 2023, and focuses on seven stock market indices (S&P 500, DAX, CAC40, Nikkei, TSX, MOEX, and PFTS) and seven cryptocurrencies (Bitcoin, Ethereum, Litecoin, Dash, Ripple, DigiByte, and XEM). In this article, we investigate how investors react to fluctuations in financial assets and whether they seek safe havens in cryptocurrencies. We use dynamic causality in the Granger (1969) sense to detect a possible causal relationship in the short term, and seven models to estimate the long-term relationship between cryptocurrencies and financial assets. Our results show that in the short term, three famous cryptocurrencies (Bitcoin, Ethereum, and Ripple) and two digital assets with minor popularity (XEM and DigiByte) are impacted by the German, Russian, and Ukrainian stock markets. In the long term, we find a positive and significant effect of the American, Canadian, French and Ukrainian stock market indices on Bitcoin. These findings suggest that the stability of traditional financial markets during the current war period can be explained on the one hand by investors' fears of an unstable business climate, and on the other hand, by speculators' interest in new electronic products that are perceived as hedging instruments and safe havens in times of crisis.
Open access
Market Dynamics and Volatility
Economic Sanctions and International Relations
Environmental and Biological Research in Conflict Zones
A adoção de blockchain e Contratos Inteligentes (CI) tem se demonstrado uma opção valiosa para organizações inovarem em seus sistemas de informação. Nesse sentido, presencia-se uma adesão ao desenvolvimento open source por parte dos envolvidos em tal ecossistema, especialmente com base na plataforma Ethereum e no uso do GitHub. Logo, nota-se uma formação de um contexto sociotécnico para desenvolvimento de CI através do uso de plataformas para hospedagem de código-fonte como, por exemplo o GitHub. Porém, apesar da presença de pesquisas prévias sobre manutenção de CIs, constata-se uma lacuna de pesquisa como foco na compreensão de componentes sociotécnicos em tal domínio. Nesse sentido, sob um escopo metodológico exploratório pautado em Mineração de Repositórios de Software na Teoria Sociotécnica, este traba- lho tem como objetivo principal analisar componentes sociotécnicos em repositórios open source de contratos inteligentes implantados na plataforma Ethereum. Como contribuições alcançadas, quatro perspectivas principais foram contempladas: 1) na discussão sobre efeito do deploy na Ethereum sobre os aspectos colaborativos (com foco em contribuidores, commits e issues); 2) na compreensão sobre a caracterização dos contribuidores envolvidos nos projetos; 3) na categorização sobre o que mudou quanto à evolução de software em tais projetos a partir das mensagens de commits e 4) no entendimento sobre a relação entre métricas de mercado (valor de mercado e volume e evolução de software (em termos de commits).
Kokulo K. Lawuobahsumo, Bernardina Algieri, Arturo Leccadito
Abstract This study aims to jointly predict conditional quantiles and tail expectations for the returns of the most popular cryptocurrencies (Bitcoin, Ethereum, Ripple, Dogecoin and Litecoin) using financial and macroeconomic indicators as explanatory variables. We adopt a Monotone Composite Quantile Regression Neural Network (MCQRNN) model to make one- and five-steps-ahead predictions of Value-at-Risk (VaR) and Expected Shortfall (ES) based on a rolling window and compare the performance of our model against the Historical simulation and the standard ARMA(1,1)-GARCH(1,1) model used as benchmarks. The superior set of models is then chosen by backtesting VaR and ES using a Model Confidence Set procedure. Our results show that the MCQRNN performs better than both benchmark models for jointly predicting VaR and ES when considering daily data. Models with the implied volatility index, treasury yield spread and inflation expectations sharpen the extreme return predictions. The results are consistent for the two risk measures at the 1% and 5% level both, in the case of a long and short position and for all cryptocurrencies.
Stefan Kambiz Behfar, Richard Mortier, Jon Crowcroft
Blockchain technology has revolutionized the way information is propagated in decentralized networks. Ethereum plays a pivotal role in facilitating smart contracts and decentralized applications. Understanding information propagation dynamics in Ethereum is crucial for ensuring network efficiency, security, and scalability. In this study, we propose an innovative approach that utilizes Graph Convolutional Networks (GCNs) to analyze the information propagation patterns in the Ethereum network. The first phase of our research involves data collection from the Ethereum blockchain, consisting of blocks, transactions, and node degrees. We construct a transaction graph representation using adjacency matrices to capture the node embeddings; while our major contribution is to develop a combined Graph Attention Network (GAT) and Reinforcement Learning (RL) model to optimize the network efficiency and scalability. It learns the best actions to take in various network states, ultimately leading to improved network efficiency, throughput, and optimize gas limits for block processing. In the experimental evaluation, we analyze the performance of our model on a large-scale Ethereum dataset. We investigate effectively aggregating information from neighboring nodes capturing graph structure and updating node embeddings using GCN with the objective of transaction pattern prediction, accounting for varying network loads and number of blocks. Not only we design a gas limit optimization model and provide the algorithm, but also to address scalability, we demonstrate the use and implementation of sparse matrices in GraphConv, GraphSAGE, and GAT. The results indicate that our designed GAT-RL model achieves superior results compared to other GCN models in terms of performance. It effectively propagates information across the network, optimizing gas limits for block processing and improving network efficiency.
Blockchain technology has revolutionized various industries by providing secure transaction mechanisms in a decentralized, trustless environment. In 2014, the Ethereum blockchain platform introduced smart contracts, facilitating the deployment of a wide range of decentralized applications. However, since its inception numerous vulnerabilities have been discovered in the Ethereum Virtual Machine, many resulting in significant financial loss. Consequently, a new smart contract language, Move, has been developed, where security and verifiability are first class features. As the adoption of Move increases, it necessitates robust developer tools and adherence to best practice principles, similar to the existing infrastructure present in Ethereum. This thesis contributes to the advancement of this goal. First, it introduces VeriMove, the first model checking framework for the Move language. Experiments show that model checking is a feasible method to formally verify global properties in Move smart contracts. Second, this thesis presents the first gas optimization analysis of the Move language. Experiments show that the proposed gas optimization patterns reduce gas consumption in a typical smart contract by 7 - 56%. Lastly, this thesis proposes a novel algorithm to automatically audit the transaction order dependency vulnerability present in many popular public blockchain platforms. A prototype implementation is developed on Ethereum for the Solidity smart contract language. Experiments show that the proposed methodology can be used successfully to detect and rectify such vulnerabilities, or to certify their absence.
Rohan Gaikwad, Adesh Bhor, Yash Bhor, Prof. Shubham shelke
The concept of Non-Changeable Documents (NCDs) using blockchain technology is a pivotal development in ensuring data integrity and security. This paper explores the innovative approach of employing blockchain to create immutable records, guaranteeing the authenticity and integrity of documents. NCDs leverage the decentralization and cryptographic features of blockchain, rendering documents unalterable. Their application spans across various industries, including legal, healthcare, and finance, promising significant advantages in terms of fraud prevention, transparency, and compliance. This research paper also delves into the technical underpinnings of NCDs, elucidating the cryptographic mechanisms and consensus protocols that safeguard their reliability. By harnessing the decentralized and tamper-proof nature of blockchain, NCDs provide a formidable solution for organizations seeking to safeguard critical data against unauthorized alterations. This transformative potential of Non-Changeable Documents underscores their ability to elevate document security and establish trust in an increasingly interconnected digital landscape. In an era marked by data breaches and concerns over document authenticity, NCDs offer a promising avenue to ensure the integrity and security of sensitive information, reinforcing trust in digital records. Key Words: Blockchain, Documents, Fraud, Smart Contract, Ethereum Blockchain, Security
Background: Due to the increased interest in cryptocurrencies, opinions on cryptocurrency-related topics are shared on news and social media. The enormous amount of sentiment data that is frequently released makes data processing and analytics on such important issues more challenging. In addition, the present sentiment models in the cryptocurrency domain are primarily focused on English with minimal work on Malay language, further complicating problems. Objective: The performance of the sentiment regression model to forecast sentiment scores for Malay news and tweets is examined in this study. Methods: Malay news headlines and tweets on Bitcoin and Ethereum are used as the input. A hybrid Generalized Autoregressive Pretraining for Language Understanding (XLNet) language model in combination with Bidirectional-Gated Recurrent Unit (Bi-GRU) deep learning model is applied in the proposed sentiment regression implementation. The effectiveness of the proposed sentiment regression model is also investigated using the multi-head self-attention mechanism. Then, a comparison analysis using Bidirectional Encoder Representations from Transformers (BERT) is carried out. Results: The experimental results demonstrate that the number of attention heads is vital in improving the XLNet-GRU sentiment model performance. There are slight improvements of 0.03 in the adjusted R2 values with an average MAE of 0.163 (Malay news) and 0.174 (Malay tweets). In addition, an average RMSE of 0.267 and 0.255 were obtained respectively for Malay news and tweets, which show that the proposed XLNet-GRU sentiment model outperforms the BERT sentiment model with lesser prediction errors. Conclusion: The proposed model contributes to predicting sentiment on cryptocurrency. Moreover, this study also introduced two carefully curated Malay corpora, CryptoSentiNews-Malay and CryptoSentiTweets-Malay, which are extracted from news and tweets, respectively. Further works to enhance Malay news and tweets corpora on cryptocurrency-related issues will be expended with implementing the proposed XLNet Bi-GRU deep learning model for greater financial insight. Keywords: Cryptocurrency, Deep learning model, Malay text, Sentiment analysis, Sentiment regression model
In the rapidly evolving domain of distributed ledger technology, scalability and interoperability have become paramount challenges for both academic and industry sectors. In this paper, we introduce a comprehensive formal model to address atomic composability across multiple rollups on Ethereum. The proposed model incorporates mechanisms like buffering, dependency management, concurrency control, and the groundbreaking zero-knowledge proofs. Moreover, we evaluate its practical repercussions, strengths, and weaknesses, ensuring resilience against manipulative or erroneous actions. The application of the proposed model to shared sequencers and other existing solutions accentuates its versatility and universality.
The study addressed the problem of developing a mechanism for introducing blockchain technology into the system of accounting and analytical support for public sector entities. In the course of the research, the volumes and types of financial violations by public sector entities were analyzed. Identified violations were committed by public sector entities, and their significant share fell on the system of accounting and analytical support, in particular, financial reporting, budget execution. Legal restrictions on the introduction of blockchain technology for public sector entities were outlined. The absence of a number of acts in the field of digitization, protection of state secrets, cyber security, international standards, etc. in the legal field was established. The characteristics and mechanisms of blockchain technology were described, the types of blockchains based on permission models (open, closed) were defined. The technological characteristics of the use of blockchain platforms in the system of accounting and analytical support of public sector entities have been determined. The need to use blockchain applications such as smart contracts was indicated. It is noted that the use of smart contracts can be developed on blockchain platforms such as Ethereum, NXT, and Hyperledger; their key characteristics were described. A comparative description of transactions of state funds in the traditional accounting system and with the use of blockchain systems is given. The problem of the materiality of the transaction cost when transferring large volumes of data was outlined using the Ethereum platform as an example; its shortcomings were identified. The development of a mechanism for introducing blockchain technology into the system of accounting and analytical support using the Hyperledger platform, which has an open-source community focused on the development of a set of stable frameworks, tools, and libraries for deploying enterprise-level blockchains, was described. The advantages and disadvantages of introducing blockchain technology into the system of accounting and analytical support have been determined.
Sushruta Mishra, Soham Chakraborty, Kshira Sagar Sahoo, Muhammad Bilal
The advent of the Internet of Things (IoT) has resulted in significant technical development in the healthcare sector, enabling the establishment of Medical Cyber-Physical Systems (MCPS). The increased number of MCPS generates a massive amount of privacy-sensitive data, hence it is important to enhance the security of devices and data transmission in MCPS. Earlier several research studies were undertaken in order to enhance security in healthcare, but none of them could adapt to changing behaviors of data attacks. Here the role of blockchain and Reinforcement Learning (RL) comes into play since it can adjust itself to the nature of changing attacks, thus preventing any kind of attacks. This work proposes a solution, named Cogni-Sec, which employs a decentralized cognitive blockchain and Reinforcement Learning architecture and addresses the security issue. Blockchain is incorporated in the approach for data storage and transmission to increase the degree of security in the MCPS modules. Hyperledger Fabric is applied as the blockchain base which shows transaction query results with nearly 10% increased throughput, 69% less memory consumption, and 15% lower CPU usage when compared to Ethereum. Further security risk at the block mining level within a blockchain network is reduced by introducing distributed Reinforcement Learning architecture in replacement for the miner nodes, which imitates the cognitive behavior of miners in a distributed environment. Different multi-agent learning systems have been evaluated for building the mining agent. Among these, the a3c agent in distributed learning setup yields the optimum cumulative reward with a median value of 54.5 and minimizes the maximum number of data threats.
The rapid growth and integration of the Internet of Things (IoT) emphasizes the crucial need for effective data governance. This research unveils a novel framework, capitalizing on blockchain and smart contracts, aimed at decentralizing data governance in the IoT sphere. Our approach allows stakeholders to formulate and enforce data governance collaboratively, ensuring a balance between transparency, adaptability, and flexibility. Using the Ethereum platform and Solidity as our smart contract language, we constructed a demonstrative proof-of-concept. Our comparative evaluations highlighted our system's superiority, outpacing previous works with a scalability score of 95%, flexibility at 90%, and an unmatched transparency score of 100%. This framework presents a transformative paradigm for organizations and individuals working with IoT data, offering an efficient, transparent, and robust data governance mechanism.
Existing blockchain system face scalability issues when processing massive amounts of data.These issues primarily arise due to their consensus based block generation methods.Sharding has emerged as a promising on chain solution to enhance the scalability of blockchain.This technology increases throughput by dividing the main network into several sub-networks, called shards, which can process transactions in parallel.However, implementing sharding in blockchain system presents two significant challenges: Cross shard transactions and load imbalance between different shards.Cross shard transaction refers to transactions generated between accounts belonging to different shards.Load imbalance occurs when specifical one shard processes a disproportionately higher transaction load than others.These challenges can lead to increased network delay, confirmation time, latency, and fees due to complicated inter-shard communication, thereby reducing blockchain throughput.To address these challenges, this paper proposes an innovative account relocation scheme.This scheme aims to optimize load balancing in blockchain sharding using a round robin algorithm.To validate the effectiveness of our approach, we utilized a simulator that incorporates real Ethereum data.We then compared the degree of load balancing achieved by our method against existing methods, such as schemes that use no-relocation and random relocation.Our results indicate a significant improvement in load balancing performance compared to previous approaches.