In recent years, many researchers have demonstrated that privacy acumen can be applied to healthcare applications to enhance patients’ life quality using Machine Learning (ML) and Deep Learning (DL). As far as we are aware, a tiny degree of patient privacy is contained in medical data, which necessitates extreme data protection against disclosure to intruders. In machine learning and deep learning, to build accurate system data pooling done at server, causes data breach, to overcome this issue, in 2016, Google proposed an idea to share learned models rather than sharing the data called Federated Learning (FL). In this systematic analysis, we lay out FL's primary objective concerning privacy. Afterwards, we presented the primary challenges in configuring and deploying federated learning networks and introduce algorithms within advanced federated learning architectures by integrating Convolutional Neural Networks (CNNs), a blockchain-based mechanism, and a non-fungible token (NFT) for the detection of various harmful diseases. moreover, we analysed the configuration settings, performances, Limitations of different algorithms against different datasets. Ultimately, we conclude this analysis by discussing optimization and communication challenges in future FL smart healthcare systems. We believe that reading this through can be beneficial for academicians, industry experts, and neoteric alike, providing them with direction and suggestion for their next endeavours.
Distributed ledgers are common in the industry. Some of them can use blockchains as their underlying infrastructure. A blockchain requires participants to agree on its contents. This can be achieved via a consensus protocol, and several BFT (Byzantine Fault Tolerant) protocols have been proposed for this purpose. How do these protocols differ in performance? And how is this difference affected by the communication network? Moreover, such a protocol would need a timer to ensure progress, but how should the timer be set? This paper presents an analytical model to address these and related issues in the case of crash faults. Specifically, it focuses on two consensus protocols (Istanbul BFT and HotStuff) and two network topologies (Folded-Clos and Dragonfly). The model provides closed-form expressions for analyzing how the timer value and number of participants, faults and switches affect the consensus time. The formulas and analyses are validated with simulations. The conclusion offers some tips for analytical modeling of such protocols.
Blockchain technology, when used to authenticate academic credentials, introduces new approaches to dealing with age-old issues like as fraud and inefficiencies. This paper informs readers on the primary roles that education-based enterprises can perform on both Ethereum and Polygon, boosting the possibilities for developing certification systems based on NFTs. Ethereum is popular among developers and has helped to formalize the sector by establishing foundations and adopting standards such as ERC721. Polygon is a Layer 2 scaling technique designed to reduce transaction costs while also improving transaction functionality. The proposed system includes steps around creating NFTs and governing their distribution and lifecycle to ensure genuine tamper proof of academic records with the help of blockchain. This paper will explore the scalability of system and its cost-effectiveness along with simplicity for ease of choosing appropriate blockchain platform for providing secure certificates issuance and verification.
The 21st century has seen the advent of technologies. One of these technologies is the blockchain. A blockchain is a sort of digital ledger that comprises a growing list of documents, called blocks that are cryptography linked to the preceding block. A non-fungible token (NFT) is a data unit (token) kept on a blockchain that is incompatible with other digital assets. To demonstrate ownership of digital assets, such as songs, paintings, video game components, or even recipes, NFTs are utilized. Non-Fungible Tokens (NFTs) are a type of digital asset on public blockchains that are both fungible and unique, and their market has experienced explosive growth since early 2021. Our product is a platform for these NFTs to be freshly minted, stored, collected, sold, bought and resold. We feel that a robust market like environment is a must while dealing with such a disruptive technology that changes the way we look at assets. This project focuses on security, automatic price detection system and a useful recommendation algorithm that will connect the customer with the best NFTs.
J ROHINI, MARIA SIJI MALAR D, S NIRAIMAARAN, DR M JOHN PAUL
A blockchain is a distributed database or ledger shared among a computer network's nodes. They are best known for their crucial role in cryptocurrency systems for maintaining a secure and decentralized record of transactions, but they are not limited to cryptocurrency uses. Blockchains can be used to make data in any industry immutable the term used to describe the inability to be altered. Because there is no way to change a block, the only trust needed is at the point where a user or program enters data. This aspect reduces the need for trusted third parties, which are usually auditors or other humans that add costs and make mistakes. Blockchain technology achieves decentralized security and trust in several ways. To begin with, new blocks are always stored linearly and chronologically. That is, they are always added to the "end" of the blockchain. After a block has been added to the end of the blockchain, previous blocks cannot be changed. Blockchain technology is still very much in its nascent stage, yet it has had a significant impact on finance. While cryptocurrencies have been responsible for bringing the technology to the forefront, the advantages of blockchain have been recognized by various industries. As a result, most modern businesses are now undergoing a sea of change as they prepare themselves to usher in this new era.
This research suggests an authentication system for encrypted payment amounts using zero-knowledge proof and encryption algorithm, with the goal of enhancing security and trustworthiness of blockchain transactions. The system effectively processes large-scale transaction data by incorporating functional modules like data input, verification, and output in a modular system architecture. Results from experiments demonstrate that the system achieves a verification precision of 99.8% when handling transaction data sets of various sizes, with processing time increasing proportionally as the transaction size grows, indicating excellent scalability. This research offers effective options for financial transactions, supply chain management, and digital asset management, guaranteeing the security and integrity of transactions while enhancing transparency and traceability in multi-party interactions. Furthermore, by examining various usage situations, the system showcases its wide range of potential and value in practical use. This research introduces fresh technological methods for verifying security in blockchain transactions and establishes a basis for future studies and implementation.
Blockchain, through its secure distributed ledger technology, has been heralded as an important enabler of consumer and business applications. In addition to cryptocurrency, there are many examples of existing and planned implementations of blockchain-enabled systems in organizations, such as in the supply chain. However, what is unclear from the literature, are the mechanisms driving the acceptance of blockchain systems and the extent to which salient models of technology acceptance are capable of explaining adoption behavior. This research employs large-scale meta-SEM analysis using 37,865 respondents from 142 data sets to examine critically a variety of competing models of blockchain technology acceptance. The results identify two new models that help to explain a significant amount of variance in behavioral intention to use blockchain systems, trust-risk model (TRM) and a TRM/UTAUT2 hybrid TRAUT. Thus, although we find support for the standard models of acceptance, the paper suggests that new models incorporating trust and risk are essential in the blockchain context. The models are robust to tests using a variety of moderators of relationships.
Zejun Lin, Bin Chen, Yi Chen, Peichang Zhang · 5 authors
A campus public resources (CPRs) sharing model is proposed by applying Decentralized Identifiers (DIDs) and Non-Fungible Tokens (NFTs) in smart campus. The integration of DIDs empowers users with enhanced autonomy and control over their digital identities, providing improved security and convenience while extending resource accessibility to off-campus individuals. Furthermore, the model employs the NFT standard of ERC4907 to achieve a clear separation between resource ownership and usage rights. Leveraging the inherent distributed and transparent nature of blockchain technology, the CPRs sharing model effectively eliminates data barriers between departments, fostering a collaborative environment. A high-performance platform based on the model is developed to meet the requirements of sharing CPRs within a university setting.
Innovation in Digital Healthcare Systems
Technology and Data Analysis
Diverse Approaches in Healthcare and Education Studies
In the blockchain architecture, there are various consensus methods, such as Proof of Work (PoW), Proof of Stake (PoS) and Delegated Proof of Stake (DPoS) for validator selection. A DPoS protocol works similarly to an election, where token holders vote for validator candidates. Elected validators generate blocks and receive block generation rewards. BNB Chain adopting DPoS has implemented a leveling mechanism to prevent the concentration of votes on a small number of validators. However, voting token holders do not always act in economically rational ways. This causes disparity in rewards to votes by token holders. We investigated the BNB Chain and factors related to the reward rates. We calculated Spearman's rank correlation coefficient between reward rates and several factors: the number of delegate-related transactions, the amount of delegated tokens, the rate of change in the amount of delegated tokens, and the average number of voting validators. As a result, we found that the number of delegate-related transactions has the strongest correlation with reward rates, and that there is a non-linear correlation between the reward rate and the average number of voting validators.
Non-Fungible Tokens (NFTs), recognized for their uniqueness and irreplaceability, serve as an effective mechanism for copyright protection of digital works. Utilizing NFTs to build trading platforms facilitates copyright authentication, monitoring, and circulation of artworks. To alleviate the immense storage pressure on blockchain networks, NFT artworks are often stored in the InterPlanetary File System (IPFS). However, as IPFS is a decentralized file system lacking encryption mechanisms for data, the absence of privacy protection could render copyright protection measures ineffective if malicious users access and utilize the data outside the trading platform. This paper proposes a tri-layered protection mechanism for NFT artwork data, encompassing access control, image watermarking, and data encryption. The proposed scheme ensures artwork protection against theft without compromising the circulation and transaction of works. Experimental results demonstrate that the scheme meets the privacy and security needs of the artworks efficiently and at a low cost.
With the rapid development of blockchain technology, smart contracts, as its core component, are widely used in various fields. However, with the increase in the number and complexity of smart contracts, their security has become a key issue. Currently, fuzzy testing is the mainstream dynamic security testing technique in the field of Ethereum smart contracts, generating a large number of test cases and executing them to discover vulnerabilities. However, due to the difficulty in covering the deep branching code of smart contracts, vulnerability detection is not comprehensive enough. In order to solve the problem of the difficulty of deep branch code coverage of smart contracts, this paper proposes a fuzzy testing method for smart contracts based on MDP and simulated annealing algorithm, i.e., VMFUZZ. This method first models the execution process of smart contracts as MDP, and then combines with the simulated annealing algorithm to generate the transaction sequences that are prone to triggering vulnerabilities in order to comprehensively cover the execution situation of the contract. Finally, a large number of new test cases are generated through fuzzy testing to detect vulnerabilities. The experimental results show that VMFUZZ is improved in code coverage compared to ILF and has a higher detection rate in vulnerability detection capability.
A Study on the Application of Financial Analytics in the Field of Blockchain Technology Authors- Professor Dr.K.Baranidharan, Research Scholar K. Sabitha, Research Scholar V. Deepika Abstract-An important new field of research at the crossroads of blockchain and distributed ledger technologies ... Read More »
Diverse Approaches in Healthcare and Education Studies
This study conducts a comprehensive analysis of Bitcoin-related tweets to understand sentiment trends and patterns using TF-IDF vectorization and K-means clustering. The dataset, comprising 1,544 unique tweets, was collected via the Twitter API and preprocessed to remove duplicates and clean the text. Sentiment analysis revealed a distribution of 53.7% neutral, 29.7% positive, and 16.6% negative tweets, indicating a predominant neutral sentiment in the discourse. Keyword analysis identified frequent terms such as 'bitcoin' (479 occurrences), 'new' (46), 'good' (43), 'crypto' (39), and 'trade' (39). Visualizations through word clouds highlighted the specific language associated with each sentiment category, with positive tweets focusing on opportunities and innovation, while negative tweets emphasized risks and scams. Cluster analysis using K-means, with the optimal number of clusters determined by the elbow method, resulted in three distinct clusters. Cluster 0, comprising 1,346 tweets, was characterized by neutral and informative content, focusing on market updates and trading strategies. Cluster 1, with 163 tweets, contained a higher concentration of positive sentiment, highlighting positive developments and investment opportunities. Cluster 2, the smallest with 35 tweets, focused on negative sentiment, reflecting concerns about market volatility and fraudulent activities. These clusters provided a nuanced understanding of the thematic composition of Bitcoin-related tweets. The study's findings have practical implications for investors, traders, and market analysts by providing insights into market mood and sentiment trends. The integration of these findings into predictive models can enhance market prediction accuracy and develop more effective trading strategies. Despite the study's contributions, limitations such as the dataset's language and scope suggest areas for future research, including real-time sentiment analysis and the incorporation of multimodal data sources. This research advances the field of sentiment analysis in financial markets, particularly within the context of cryptocurrencies, by offering a detailed and longitudinal examination of social media sentiment.
With the rapid development of the Industrial Internet of Things, a vast amount of data generates, collects, and processes, playing a crucial role in industrial production and operations. Addressing issues of privacy leakage, identity forgery, and data security during data transmission in IIoT, this paper proposes a data security transmission and privacy protection scheme based on blockchain technology. The scheme employs zero-knowledge proof algorithms for identity authentication of terminal devices to ensure the privacy of device information is not compromised, effectively preventing identity forgery and attacks. Additionally, it uses attribute-based encryption to secure the data's confidentiality. To ensure efficient data storage and the security of encryption keys, the scheme utilizes IPFS for on-chain querying and off-chain storage of data. The security of the proposed scheme is analyzed theoretically, and its computational overhead is evaluated experimentally. The results show that this scheme has lower computational overhead compared to other schemes, while significantly enhancing security and reliability.
The rapid development of Bitcoin and blockchain technology is shocking. In the development of Bitcoin and other industries, machine learning has contributed a lot and has unlimited potential. It can not only analyze the data in the transaction process, but also bring security and predict the development trend of the market. The combination of multiple technologies promotes the efficiency of Bitcoin transactions, and provides effective support for making correct decisions, which is enough to show that financial technology can still undergo unpredictable changes in the next stage of development. In this research, the application of machine learning technology in the development of Bitcoin is analyzed in depth, especially in improving efficiency, improving intelligent contracts, monitoring transactions and so on. Through the analysis, efficiency and prediction accuracy of the model will change positively because of the application of algorithm and data processing technology. This study also points out the significance of protecting user privacy and enhancing data security, which brings effective strategies for the development of Bitcoin technology, the wide use of encryption technology and the improvement of regulatory efficiency, and fully taps the potential of machine learning.
Analyzing social media trends can create a win-win situation for both creators and consumers. Creators can receive fair compensation, while consumers gain access to engaging, relevant, and personalized content. This paper proposes a new model for analyzing Bitcoin trends on Twitter by incorporating a 'liquid democracy' approach based on user reputation. This system aims to identify the most impactful trends and their influence on Bitcoin prices and trading volume. It uses a Twitter sentiment analysis model based on a reputation rating system to determine the impact on Bitcoin price change and traded volume. In addition, the reputation model considers the users' higher-order friends on the social network (the initial Twitter input channels in our case study) to improve the accuracy and diversity of the reputation results. We analyze Bitcoin-related news on Twitter to understand how trends and user sentiment, measured through our Liquid Rank Reputation System, affect Bitcoin price fluctuations and trading activity within the studied time frame. This reputation model can also be used as an additional layer in other trend and sentiment analysis models. The paper proposes the implementation, challenges, and future scope of the liquid rank reputation model.
Blockchain is a decentralized network in which data blocks are linked.Through a decentralized peer-to-peer network, users can create shared databases, resulting in a trustworthy and aggregated database known as a blockchain that enhances reliability and security.The distributed nature of the blockchain enables data to be stored on multiple nodes, eliminating the need for a central server or platform.This disintermediation significantly reduces the transaction and administrative costs.The blockchain is particularly valuable in applications where reliability and stability are critical because it establishes an open database that ensures data integrity, making it virtually impossible to tamper with or falsify data.This study explores the diverse applications of the blockchain technology in virtual assets, such as cryptocurrency, decentralized finance, central bank digital currency, nonfungible tokens, and metaverses.In addition, it analyzes the potential prospects and developments driven by these innovative technologies.
With the advancement of blockchain technology and growing concerns about the vulnerabilities and mistrust in centralized financial services, decentralized finance (DeFi) and decentralized exchanges (DEXs) have emerged as promising alternatives. This paper delves into the challenges and issues within DeFi, with a particular focus on Uniswap. We highlight the susceptibility to Maximal Extractable Value (MEV) attacks, providing a background on the current state of DeFi and DEXs. Our approach includes a detailed transaction analysis on Uniswap to identify and analyze MEV attack patterns, alongside a method for detecting bots. The results offer critical insights into the nature of various attacks in DEXs and the correlation between internal and external blockchain events and MEV attack patterns. This research provides valuable guidelines for enhancing DEX security and mitigating MEV risks, serving as an essential resource for stakeholders in the DeFi ecosystem.
Bitcoin is the leading cryptocurrency with the highest market value among digital currencies. Therefore, predicting the value of Bitcoin can help to understand the entire cryptocurrency market. However, Bitcoin has had a lot of price fluctuations since its inception. In this paper, we are going to forecast the price of Bitcoin using news headline analysis, technical analysis indicators, and historical financial data. The news headlines used in this article are scraped from the Cointelegraph news website, which contains 3988 news headlines related to Bitcoin between 2/7/2020 and 3/8/2021. A transformer pre-trained model on cryptocurrency-related texts called CryptoBERT, which is a BERT-based sentiment analysis model, has been used to analyze the textual data. Also, a novel hybrid 2DCNN-GRU deep learning model has been used to predict the price. To adjust the parameters of this model, a parameter tuning method based on orthogonal arrays called the Taguchi method has been employed. Finally, to examine the proposed model’s efficiency, the obtained results have been compared with other deep learning models from the literature review that used text data to predict bitcoin prices. The results show that this model outperformed other models in terms of MAE criterion, while in the other three criteria, namely MSE, RMSE and MAPE, it still demonstrated good results.
In healthcare systems, blockchain technology plays a crucial role in transmitting COVID-19 data among multiple entities.Over time, various blockchain-based medical applications have emerged to handle medical information confidentially.One such system is the Scalable eHealthChain system (SeHealthChain), which utilizes a sharding scheme consisting of transaction chain and reputation chain structures to enhance throughput and security.However, the system employs a modified Raft-based Synchronous Consensus Scheme (RSCS) for generating the transaction blockchain, which can potentially introduce illegitimate transactions to the Hyperledger fabric network if a rogue node transfers them to the orderer.This poses a significant security risk in the worst-case scenarios.Additionally, as the hash rate fluctuates exponentially, the generation period of transaction blocks and computation difficulty increase.To address these issues, this article proposes an Optimized SeHealthChain (OSeHealthChain) system.It integrates a Tuna Swarm Optimization Algorithm (TSOA) with the modified RSCS to dynamically adjust the blockchain parameters in response to significant changes in the hash rate.The TSOA optimizes two variables, namely the Block Interval (BI) and Difficulty Adjustment Interval (DAI) of the Proof-of-Work (PoW) for the transaction blockchain, based on objective functions that consider the Standard Deviations (SD) of the mean BI and difficulty.By selecting appropriate variables, the system generates new transaction blocks with minimal nodes and overhead, effectively validating transactions and blocks to enhance the security level.Extensive simulations show that the OSeHealthChain achieves a throughput of 3918tps and a user-perceived latency of 63.8s for 1000 nodes, outperforming the SeHealthChain, eHealthChain, Permissionless Proof-of-Reputation-X (PL-PoRX), and hybrid Proof of Stake-Practical Byzantine Fault Tolerance (POS-PBFT) algorithms in blockchain systems.It also achieves throughputs of 7051tps, 6418tps, and 6290tps for simple, camouflage, and observe-act attacks, respectively, with 1000 nodes and a shard dimension of 200 during 20 epochs.
This research work presents a decentralized parallel blockchain-based agricultural product traceability system, which aims to enhance information security and data model efficiency, and achieve real-time tracking of agricultural products. Through an in-depth analysis of decentralized blockchain applications, the system adopts distributed ledger processing for low-cost transactions. The Digital Promise technology ensures the credibility of the data transmission and privacy protection. The proposed decentralized parallel blockchain structure shows significant traceability results, ensuring transparent traceability of agricultural products. Experimental results demonstrate the effectiveness of the system, improving food safety and quality management. The decentralized nature reduces the risk of data tampering, thereby enhancing public trust. Experimental results show short query response times, efficient data transfers, and zero failures, highlighting the system's excellent performance in handling traceability queries. These results underscore the remarkable advantages of blockchain technology in increasing the efficiency and reliability of traceability systems.