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.
I Gede Agus Krisna Warmayana, Yuichiro Yamashita, Nobuto Oka "Decentralized Materials Data Management using Blockchain, Non-Fungible Tokens, and Interplanetary File System in Web3" Journal of Applied Data Sciences, 2025, Vol.6, No.1, p.742-752 https://doi.org/10.47738/jads.v6i1.380 掲載
The safety and security of e-commerce transactions are critical in today's digital landscape, where cyber risks abound. Blockchain technology's decentralized, transparent, and immutable ledger system offers a viable solution to these concerns. This study investigates the use of Distributed Ledger Technology (DLT), specifically Decentralized Identifiable Distributed Ledger Technology (DIDLT), in conjunction with the innovative Blockchain Consent Algorithm (BCA), to improve the safety and security of e-commerce transactions. Secure data storage and retrieval in e-commerce can be achieved by accurate digital signature production, key generation, blockchain building, and validation. This paper investigates how DIDLT, which combines decentralized identity management with blockchain technology, and BCA, a cutting-edge consensus algorithm designed for blockchain environments, work together to protect e-commerce transactions from identity theft, payment fraud, and data manipulation. This paper throws insight into DIDLT and BCA's potential to transform the e-commerce safety and security market by providing an in-depth analysis of their implementation. Using the incorporated DIDLT-BCA model significantly improves the safety effectiveness of the network, resulting in 98% security, shorter performance times of as much as 150 milliseconds, and mining times of up to 0.98 s.
Blockchain is a modern technology that has revolutionized the way society interacts and trades. It could be defined as a chain of blocks that stores information with digital signatures in a distributed and decentralized network. This technique was first adopted for the creation of digital crypto currencies, such as Bit coin and Ethereum. However, research and industrial studies have recently focused on the opportunities that blockchain provides in various other application domains to take advantage of the main features of this technology, such as: decentralization, persistency, anonymity, and auditability. This paper reviews the use of blockchain in several interesting fields, namely: finance, healthcare, information systems, wireless networks, Internet of Things, smart grids, governmental services, and military/defense. In addition, our paper identifies the challenges to overcome, to guarantee better use of this technology.
An all-encompassing and flexible framework that links theoretical principles and actual execution is required for the creation of hardware for Web3.0 and edge intelligence. Decentralized identification, semantic data, blockchain, and zero-knowledge proofs are just few of the concepts that are examined in depth to provide the groundwork for this technique. The main goal is to figure out what kinds of hardware are needed to implement these theoretical principles. Subsequently, the technique continues to extract the hardware requirements, distinguishing the unique demands, problems, and performance criteria important for enabling Web3.0 and edge intelligence. These needs include a wide range of topics, including performance, safety, efficiency, scalability, and even compatibility. Once the theoretical principles and hardware requirements are fully understood, the technique moves on to the design step. Key to realizing these abstract ideas is the development of specialized hardware architectures and components. Based on the outcomes of the performance assessment, iterative refinement is carried out to fix the hardware's flaws and enhance its functionality. As a result of this iterative process, hardware is kept up to date to suit the ever-shifting requirements of Web3.0 and edge intelligence. The suggested technique concludes with an emphasis on flexibility and future-proofing in light of the everchanging nature of Web3.0 and edge intelligence. Keeping up with technology developments and reevaluating the hardware design as needed are both part of this process.
This study aims to delve into the knowledge graph, functional pathways, and qualitative logic among elements such as network trust, blockchain organization, user identity, big data, and information security in the digital economy era. Through an analysis of the endogenous and exogenous explicit management logic relationships among these elements, this study innovatively constructs three primary dimensions and nine secondary dimensions of digital identity attributes for users. Additionally, it establishes a collaborative management mechanism between government organizations and non-governmental blockchain alliances corresponding to identity attributes based on the delegated proxy mechanism. Furthermore, it reconstructs the big data chain management framework for user digital identity under the zero-trust model and establishes a management process for blockchain digital identity information security protection under the zero-trust model. These efforts provide innovative solutions with both research and application value for the “virtual-real integration” global security governance of the metaverse, digital economy, and digital government.
Blockchain is a distributed ledger technology that enables tamper-resistant money transfers without a trusted third party. A major application of blockchain is a cryptocurrency used for international transactions and interpersonal transactions as a low-cost and high-speed remittance method. On the other hand, blockchain has the problem that the damage of illegal transactions is more likely to be large, because the illegal transactions cannot be modified or deleted after the transactions are approved. The illegal transaction have to be detected and modified before the transaction approval to prevent the damage of it. Prior works propose the methods to detect illegal transactions through anomaly detection, because illegal transactions have different characteristics from normal transactions. Most of the prior works only focus on large-scale anomaly transactions and do not consider small-scale anomaly transactions. The accuracy of these methods for small-scale transactions is not high because there are cases where the difference in features between small-scale anomaly transactions and normal transactions is smaller than the difference between small-scale and large-scale normal transactions. However, most individual users make small-scale transactions, so preventing small-scale illegal transactions is important for system reliability and dissemination. Therefore, in this paper, we propose an anomaly detection method to detect small-scale anomaly transactions by making a subgraph that excludes the hub users who issue large-scale transaction from the user graph. We evaluate the proposed method from two perspectives, that are execution time and accuracy of anomaly detection. As a result, the execution time is shorter than the transaction approval interval, and the accuracy for small-scale anomaly transaction is improved from the existing method without decreasing the accuracy of large-scale anomaly transaction.
Manaswi Sharma, Abhishek Sharma, Deepak Shankar Ray, Saru Dhir
Blockchain technology and decentralized storage solutions have transcended their initial roles in cryptocurrency, playing pivotal roles in various sectors, redefining data management, security, and accessibility. They promote data security, interoperability, and trust across diverse domains, with applications in decentralized finance, supply chain management, and digital identity verification. Decentralized storage, as seen in the InterPlanetary File System (IPFS) and platforms like Storj, addresses content accessibility and resistance to censorship, offering a more secure, user-centric digital landscape. The integration of blockchain in cloud storage systems and patient-centric healthcare data management holds promise, providing tamper-resistant storage with improved data access control, scalability, and potential for broader adoption.
Data provenance is critical for establishing the origin, authenticity, and integrity of data in distributed environments. Traditional provenance systems often face challenges such as tampering, lack of transparency, and centralized trust issues. Blockchain technology, with its immutable ledger and decentralized consensus mechanisms, provides a promising solution to these challenges. This article explores blockchain-based cryptographic methods that enhance the security and reliability of data provenance systems. We discuss key cryptographic primitives such as digital signatures, hash chains, and zero-knowledge proofs integrated within blockchain frameworks to guarantee secure and verifiable provenance records. The study also highlights practical implementations and identifies open challenges for future research
With the growing demand for Internet of Things devices and their usage in day-to-day life, security is the prime factor that needs to be considered. In Internet of Things devices, authentication factors such as biometric factors are usually sensitive in nature. There is a need for some strong authentication mechanism that ensures negligible data breaches in a system. Zero-knowledge authentication is a modern cryptographic technique that proves knowledge without its disclosure, ultimately boosting security by avoiding any kind of storage or exposing sensitive data. This paper aims to analyze the properties of zero-knowledge proof and how it helps in preserving the security of IoT devices. The paper not only discussed the IoT architecture and analyzed the design of zero-knowledge authentication in various Internet of Things networks but also mentioned the limitations and future directions.
Chen Anqing, Lin Haiyu, Wang Ruoxue, Bin Wang · 5 authors
Energy Internet must be realized on the premise of broad consensus and trust among the participants, therefore, there is an urgent need for a technology that can not only easily and quickly construct distributed applications, but also achieve trusted interaction at a low cost. Aiming at the improvement of the underlying technology and architecture of blockchain in the current energy blockchain research field and analyzing the existing defects, this paper uses a distributed blockchain underlying architecture for energy trading scenarios. In this paper, considering the characteristics of energy transactions concentrated in geographical locations, a transaction scheme based on hierarchical construction of the general ledger by the main sub-chain is presented. The paper studied the transaction confirmation path and data fragmentation storage strategy based on concurrent construction for massive energy transaction data. In different cases, the above scheme is realized and its effect is verified. Compared with photovoltaic power generation, the situation of wind power generation would have some differences, the peak of wind power generation in the afternoon, the user's power generation at 14 o'clock., 15 o'clock peak. In this paper, the key data structure, key algorithm, technical implementation and automated distributed trading scheme in the energy trading scenario are preliminarily described, and their key performance indicators are tested. The key technologies in this paper can be better applied to the energy trading scenario.
This paper investigates the integration of blockchain technology into core systems within institutions of higher education, utilizing the National Institute of Standards and Technology’s (NIST) Cybersecurity Framework as a guiding framework. It supplies definitions of key terminology including blockchain, consensus mechanisms, decentralized identity, and smart contracts, and examines the application of secure blockchain across various educational functions such as enrollment management, degree auditing, and award processing. Each facet of the NIST Framework is utilized to explore the integration of blockchain technology and address persistent security concerns. The paper contributes to the literature by defining blockchain technology applications and opportunities within the education sector.
Machine learning (ML) techniques have gained prominence in effectively managing Electronic Health Record (EHR) systems within the context of blockchain-cloud integration. This study presents a hybrid Machine Learning approach that combines logistic regression (LR) and random forest (RF) techniques for EHR management, leveraging the data stored in a blockchain-cloud integrated system. The tamper-resistant nature of blockchain ensures the authenticity and security of the stored patient information, serving as a reliable source for learning. The proposed LR+RF model is evaluated against other algorithms, considering various performance metrics. The analysis reveals that the LR+RF model achieves an impressive accuracy rate of 98.37%, indicating its efficacy in accurately classifying EHR data and facilitating effective management. Furthermore, the study compares the performance of blockchain-cloud-based decentralized storage with blockchain-based storage and peer-to-peer storage in terms of latency and throughput. The results demonstrate that the blockchain-cloud integrated decentralized storage surpasses other storage methods, achieving an average throughput of 6.8 units and a latency of 4.7 units. These findings highlight the potential of the proposed LR+RF model for EHR management within a blockchain-cloud integrated environment. The use of blockchain as a secure storage environment ensures the integrity of patient information, while Machine Learning techniques enhance the accuracy of classification.
The lack of authentication and ant forge mechanisms severely compromises the security details of the authorizations issuing the certification. We employ blockchain technology to verify people in a way that is akin to a digital signature with their identity and access authorization, so resolving the issue of certificate forgery. Blockchain technology is an open distributed ledger that ensures that every transaction cannot be altered and holds unquestionable information in a highly secure and encrypted manner. A high standard for the procedure that may guarantee that the data in such a certificate is authentic indicates that the document is authentic and has not been forged, having come from a reliable source. The interplanetary file system uses the content address as the only means of uniquely identifying every file inside a global namespace that includes all computing devices. A bi-dimensional barcode that provides data as black and white dots is called a Quick Response (QR) code. The system consists of black squares that can be photographed by a camera or other image device, arranged in a square framework on a white background.
Leonardo T. Kimura, Felipe K. Shiraishi, Ewerton R. Andrade, Tereza Cristina Melo de Brito Carvalho · 5 authors
The bioeconomy, an industrial production model based on biological resources and sustainable development, can be considered an emerging opportunity for biodiversity-abundant regions, such as the Amazon rainforest. However, existing genomic repositories lack data traceability and economic benefit-sharing mechanisms, resulting in limited motivation for data providers to contribute. To address this challenge, we present an implementation of Amazon Biobank, a community-driven genetic database. By leveraging blockchain and peer-to-peer (P2P) technologies, we enable distributed and transparent data sharing; meanwhile, by using smart contracts directly registered in the system, we enforce fair benefit-sharing among all system participants. Moreover, Amazon Biobank is designed to be auditable by any user, reducing the need for trusted system managers. This paper aims to validate this model, by describing the implementation of a prototype using Hyperledger Fabric and BitTorrent and evaluating its performance. Our results show that the prototype can support at least 400 transactions per second in a small network, a number that can be further improved by adding new nodes or allocating additional computational resources. We conclude that the Amazon Biobank proposal is technically feasible, and its real-world deployment has the potential to foster sustainable development in high-biodiversity regions, in addition to promoting collaborative biotechnology research.
Permissionless blockchain operates as a fully decentralized, transparent, and immutable ledger. Preserving privacy in such systems is a complex challenge, as privacy cannot rely on restricting access or deleting data. Bitcoin, the first application of blockchain technology, was initially praised as an anonymous digital currency, but the transactions on the network have been shown to be relatively easy to trace. This realization has led to the development of advanced privacy-enhancing mechanisms with stronger anonymity guarantees. This thesis offers a comprehensive overview privacy-preserving techniques for permissionless blockchain through a systematic tertiary review of existing surveys. It identifies and categorizes key techniques such as zero-knowledge proofs, ring signatures, homomorphic encryption, secure multi-party computation and decentralized mixing protocols. Their capabilities to mitigate risks of linkability and information leakage, as well as limitations like computational overhead, are examined. Furthermore, unresolved challenges and research interests in the field are analyzed. By consolidating fragmented insights into a coherent and accessible resource, this work aims to support the privacy-aware development and adoption of blockchain applications. The findings highlight a fundamental trade-off between the privacy capabilities, efficiency, and trust assumptions of existing techniques. Privacy in permissionless blockchain often requires computationally complex cryptographic methods, leading to significant delays and increased costs for users. Efficiency can be improved by assuming some level of trust in entities or hardware, but this may conflict with the principle of decentralization. Although many powerful techniques exist, there is no universal solution, and the best results are achieved with combining techniques on a case-by-case basis. Current research on permissionless blockchain privacy focuses on improving efficiency, interoperability and usability of privacy preserving techniques. Additionally, regulatory compliance and accountability are critical concerns, as the technology must comply with privacy regulations while preventing anonymity in illegal activities such as money laundering.
Thanks to developments in artificial intelligence (AI), cloud computing, cyber security, and other game-changing technologies, the information technology (IT) landscape is changing quickly. With an emphasis on artificial intelligence (AI), multi-cloud strategies, cybersecurity resilience, quantum computing, the Internet of Things (IoT), DevOps, blockchain, and Environmental, Social, and Governance (ESG) activities, this article offers a thorough examination of current IT developments influencing companies in 2024. Business operations are being completely transformed by artificial intelligence (AI) and machine learning (ML), which improve automation and personalization while bringing up moral questions of justice and transparency. Organizations are using AI-driven threat detection and zero-trust architecture more frequently as cyber threats increase in order to strengthen cybersecurity resilience. As cloud computing advances toward multi-cloud and hybrid models, businesses can benefit from increased scalability, flexibility, and reduced vendor lock-in. Though they are still in the experimental stage, emerging technologies like quantum computing offer promising improvements in computational capacity and the ability to solve complicated problems. Furthermore, the integration of 5G with IoT devices is improving real-time data processing in a number of industries, including logistics and healthcare. Initially restricted to cryptocurrencies, blockchain technology is increasingly being used in secure data management and decentralized finance (DeFi) applications. Lastly, companies are embracing green IT solutions and sustainable practices as part of the growing popularity of ESG activities. This paper clarifies these important IT trends through a detailed literature study, industry research, and expert insights, offering a thorough picture of how companies should strategically navigate the continuing digital revolution.
Usman Khalil, Mueen Uddin, Owais Ahmed Malik, Wee-Hong Ong
The blueprint of the proposed Decentralized Smart City of Things (DSCoT) has been presented with smart contracts development and deployment for robust security of resources in the context of cyber-physical systems (CPS) for smart cities. Since non-fungibility provided by the ERC721 standard for the cyber-physical systems (CPSs) components such as the admin, user, and IoT-enabled smart device/s in literature is explicitly missing, the proposed DSCoT devised the functionality of identification and authentication of the assets. The proposed identification and authentication mechanism in cyber-physical systems (CPSs) employs smart contracts to generate an authentication access code based on extended non-fungible tokens (NFTs), which are used to authorize access to the corresponding assets. The evaluation and development of the extended NFT protocol for cyber-physical systems have been presented with the public and private blockchain deployments for evaluation comparison. The comparison demonstrated up to 96.69% promising results in terms of execution cost, efficiency, and time complexity compared to other proposed NFT-based solutions.
Trustless tracking of Resident Space Objects (RSOs) is crucial for Space Situational Awareness (SSA), especially during adverse situations. The importance of transparent SSA cannot be overstated, as it is vital for ensuring space safety and security. In an era where RSO location information can be easily manipulated, the risk of RSOs being used as weapons is a growing concern. The Tracking Data Message (TDM) is a standardized format for broadcasting RSO observations. However, the varying quality of observations from diverse sensors poses challenges to SSA reliability. While many countries operate space assets, relatively few have SSA capabilities, making it crucial to ensure the accuracy and reliability of the data. Current practices assume complete trust in the transmitting party, leaving SSA capabilities vulnerable to adversarial actions such as spoofing TDMs. This work introduces a trustless mechanism for TDM validation and verification using deep learning over blockchain. By leveraging the trustless nature of blockchain, our approach eliminates the need for a central authority, establishing consensus-based truth. We propose a state-of-the-art, transformer-based orbit propagator that outperforms traditional methods like SGP4, enabling cross-validation of multiple observations for a single RSO. This deep learning-based transformer model can be distributed over a blockchain, allowing interested parties to host a node that contains a part of the distributed deep learning model. Our system comprises decentralised observers and validators within a Proof of Stake (PoS) blockchain. Observers contribute TDM data along with a stake to ensure honesty, while validators run the propagation and validation algorithms. The system rewards observers for contributing verified TDMs and penalizes those submitting unverifiable data.
The sixth generation (6G) wireless cellular networks are anticipated to include the most recent advancements in network infrastructure and new technological discoveries.In addition to exploring more spectrum at high-frequency bands, it will bring together cutting-edge technical trends like blockchain, artificial intelligence (AI), and connected robotics.6G and Next-Generation Internet: Under Blockchain Web3 Economy by Abdeljalil Beniiche explores the human-centeredness of blockchain and Web3 economy for the 6G era.
Rafael Belchior, Dimo Dimov, Zahary Karadjov, Jonas Pfannschmidt · 6 authors
The field of blockchain interoperability plays a pivotal role in blockchain adoption. Despite these advances, a notorious problem persists: the high number and success rate of attacks on blockchain bridges. We propose Harmonia, a framework for building robust, secure, efficient, and decentralized cross-chain applications. A main component of Harmonia is DendrETH, a decentralized and efficient zero-knowledge proof-based light client. DendrETH mitigates security problems by lowering the attack surface by relying on the properties of zero-knowledge proofs. The DendrETH instance of this paper is an improvement of Ethereum’s light client sync protocol that fixes critical security flaws. This light client protocol is implemented as a smart contract, allowing blockchains to read the state of the source blockchain in a trust-minimized way. Harmonia and DendrETH support several cross-chain use cases, such as secure cross-blockchain bridges (asset transfers) and smart contract migrations (data transfers), without a trusted operator. We implemented Harmonia in 9K lines of code. Our implementation is compatible with the Ethereum Virtual Machine (EVM) based chains and some non-EVM chains. Our experimental evaluation shows that Harmonia can generate light client updates with reasonable latency, costs (a dozen to a few thousand US dollars per year), and minimal storage requirements (around 4.5 MB per year). We also carried out experiments to evaluate the security of DendrETH. We provide an open-source implementation and reproducible environment for researchers and practitioners to replicate our results.
Joshua Priest, Cameron Cooper, S. Dan Lovell, Yong Shi · 5 authors
As technology continues to develop, there is a growing need to find sustainable solutions in all industries, including cryptocurrency. Due to the high energy consumption that cryptocurrencies are known for, there have been efforts to reduce waste consumption and in turn minimize the carbon footprint. We support the trends for creating an environment-friendly crypto token using the ERC-20 standard on the Ethereum blockchain. We outline the various aspects that make a token more sustainable and highlight the potential benefits of such tokens. Our proposal involves the design of a smart contract that incorporates eco-friendly features such as lower energy consumption, carbon offsetting, and more efficient methods or algorithms. We also discuss the importance of transparency and accountability in the design and implementation of such tokens. This paper discusses not only the practical tools and steps necessary in creating a crypto token but also highlights the challenges associated with creating a more sustainable token.