Abstract To address issues in traditional patent valuation—such as subjective selection of dimensional metrics, weak sensitivity to high-dimensional transaction data noise, and insufficient correlation between evaluation indicators and dimensions—this study proposes a smart contract-based patent value assessment model. Firstly, existing patent valuation theories and techniques undergo systematic deconstruction and multidimensional efficacy assessment. Leveraging big data technology, a four-dimensional optimal framework integrating "technology-market-legal-risk" dimensions is constructed. Secondly, an enhanced non-negative matrix factorization algorithm (S-NMF) is designed. By incorporating diagonal matrices and fused regularization parameters, this algorithm maps the four-dimensional optimal framework into 14 quantifiable metrics using Hyperledger Fabric consortium blockchain transaction data. This addresses the core limitation of classical NMF algorithms—the inability to adjust dimension weights—enabling flexible weighting control to meet differentiated valuation needs across diverse patent application scenarios. Finally, performance analysis and simulation experiments were conducted on the patent value assessment model, comparing it with the traditional NMF algorithm. Results demonstrate that this model outperforms traditional models in both noise robustness and dimensional correlation, effectively supporting patent value assessment needs across multiple scenarios.
Block chain technology has gained significant attention across multiple domains such as finance, healthcare, education, and real estate. It serves as the foundational technology behind cryptocurrencies, enabling secure and decentralized digital transactions. Transactions are carried out using digital wallets on computing devices and are permanently recorded as blocks linked together in a distributed ledger known as the block chain. This paper presents a comprehensive study of block chain technology, its operational principles, consensus mechanisms, and real-world applications. It also explores the integration of artificial intelligence techniques to enhance security, scalability, and trust in block chain-based cryptocurrency systems.
The convergence of the Internet of Things and edge computing represents a fundamental transformation in distributed computing architecture. Traditional cloud-centric models introduce latency and connectivity dependencies flawed for time-touchy packages. Side computing addresses such constraints by positioning computational sources at network peripheries. Distributed processing paradigms restructure data pipelines through intermediate layers between endpoint devices and centralized infrastructure. Fog nodes extend cloud capabilities to locations where data originates. Tiered computation models distinguish between device-level processing, gateway computation, and cloud-based analytics. Aspect synthetic intelligence allows deployment of state-of-the-art machine learning models on resource-limited hardware. Neural network compression strategies consisting of quantization and pruning lessen version complexity while keeping accuracy. Fifth-generation wireless networks provide a connectivity fabric essential for distributed deployments. Multi-access edge computing positions processing resources at radio access network edges. Computation offloading transfers tasks from mobile devices to edge servers strategically. Security frameworks address expanded attack surfaces through zero-trust models and blockchain-based identity management. Distributed ledger architectures eliminate centralized credential repositories. Smart contracts automate security policy enforcement across edge networks reliably.
Harsha Gowda R, Sahana M Gowda, Chethan J, Gopika R · 5 authors
The healthcare industry continues to have issues regarding the transparency and trust of the financial transactions, and especially in case of handling of insurance claims and the funding of the patient. Intermediaries and centralization is generally accompanied by inefficiencies, delay and lack of accountability. To eliminate these problems, in this paper, Medicare Chain is proposed as a decentralized blockchain-based fund management system in order to ensure the secure and transparent medical transaction. The system utilizes smart contracts of the Ethereum network to automate the process of transfer of funding between the patients, doctors and donors without the need of centralized authority in the process. Data and transaction logs of nurses is set into the InterPlanetary File System (IPFS) to ensure integrity and prevent any kind of tampering. Django-based web interface allows users authentication, access control and access to the blockchain network. By introducing a framework for auditable, secure and efficient management of medical funds using the concepts of decentralization, the proposed framework shows the possibilities of decentralized systems to create more reliability and trust amongst the healthcare ecosystems.
Everybody throws around the term "blockchain" these days, like it's some secret sauce. But smart contracts are where things actually start to get interesting. Forget endless forms and relying on someone's handshake; smart contracts handle things automatically. They're just coded agreements that trigger themselves no middlemen, no second-guessing if someone's being honest. You know what you're getting. This review looks at how smart contracts are changing the game in two touchy areas: healthcare and voting, where trust and privacy can't be taken lightly. Dealing with healthcare is usually a hassle. People lose records, insurance companies bounce you around, and privacy feels flimsy. With smart contracts, you're in charge of your data, claims happen faster, and private info stays private. Doctors can share what they need to, without breaking the rules. Voting? It's had trust issues forever people aren't sure their votes count for anything. Smart contracts clean things up. They make voting more transparent, help stop fraud, and lock down the results. You can check your ballot and know nobody's changing numbers behind the scenes. Of course, it's not all smooth sailing blockchain slows down when things get big, laws haven't caught up, some of the interfaces are confusing, and big organizations don't like change. This paper covers what works, what needs help, and where things could go next.
The exponential growth of digital finance—encompassing online banking, digital assets, decentralized finance (DeFi), and algorithmic trading—has intensified the need for robust cybersecurity frameworks. However, the rise of quantum computing presents a dual challenge: while it enables revolutionary advances in data analytics and optimization, it simultaneously threatens the cryptographic foundations of contemporary financial systems. This research explores the emerging field of quantum cybersecurity and its implications for safeguarding financial infrastructures in the post-quantum era. Traditional encryption methods such as RSA, ECC, and Diffie–Hellman key exchange are vulnerable to quantum attacks, particularly through Shor’s algorithm and Grover’s search algorithm, which can efficiently break asymmetric and symmetric cryptographic schemes. The study evaluates quantum-resistant cryptographic protocols—including lattice-based, hash-based, and multivariate polynomial encryption—as viable solutions for ensuring financial data integrity, transaction confidentiality, and regulatory compliance in quantum-vulnerable environments. Furthermore, it investigates Quantum Key Distribution (QKD) and Quantum Random Number Generation (QRNG) as hardware-assisted techniques for achieving unconditional security in financial communications and transaction authentication. By integrating quantum cryptography, hybrid encryption, and AI-driven threat modeling, this work outlines a roadmap for financial institutions transitioning toward quantum-secure infrastructures. The findings demonstrate that quantum cybersecurity is not merely a defensive measure but a transformative enabler for resilient digital finance, aligning with global efforts to achieve technological sovereignty, financial stability, and sustainable innovation in the era of quantum computing.
Shwetha K R, Divya G S, Bhavan Pande, Darshan K · 6 authors
Due to the ever-increasing demand to use safe and reliable electronic votes, a blockchain-based secure voting system has been developed to enhance transparency, trustfulness, and voter recognition. This system eliminates such issues as voting fraud, impersonation, and manipulating the results by means of biometric verification and decentralized blockchain ledger. The voters are matched to a facial-recognition database containing previously registered voters before voting. It is authenticated by a K-Nearest Neighbors (KNN) approach as it works well on classifying facial features and is not very laborious. After the vote is successfully authenticated, it is stored and signed on a blockchain network where it cannot be altered by another party. The features of smart contracts ensure the safety of voting, the correct counting of votes, and the awareness of each network node of what is happening. The cryptography of hashing and decentralized make certain that the votes are immutable, due to the decentralized structure of blockchain and consensus mechanisms. The face-matching module ensures that only the qualified individuals are allowed to vote. The system also supports mass elections and guarantees the ease of interaction among the voters. It was designed in such a way that it is scalable and user friendly. Trust, security, and efficiency are enhanced in the system through biometrical authentication, distributed ledger technology, encryption, and classification through machine-learning. It is highly dependable in how to conduct the current digital elections.
This paper presents a privacy-preserving intrusion detection architecture tailored for smart home environments, addressing the dual challenge of maintaining data confidentiality while enabling accurate anomaly detection. The proposed system replaces conventional raw data analysis with a proof-driven mechanism leveraging Zero-Knowledge Proofs (ZKPs). Behavioral patterns from smart devices such as motion sensors, door contacts, and environmental monitors are abstracted into cryptographic representations, which are then processed by a zk-SNARK-compatible machine learning model. Inference results are accompanied by cryptographic proofs verifying the correctness of each decision without disclosing the input data. A private blockchain layer, implemented using Ethereum smart contracts, records event hashes, proof metadata, and decision outcomes to ensure tamper-evident logging and automated response handling. Experimental simulations on synthetic home automation datasets demonstrate that the architecture achieves over 92% anomaly detection accuracy while ensuring zero exposure of raw sensor streams. The system also exhibits low-latency proof generation (~400 ms) and end-to-end response time under 1.2 seconds, confirming its suitability for real-time smart home applications.
This paper proposes the design of electronic medical record system supported by multimedia communication based on blockchain technology. Blockchain technology ensures the secure storage and sharing of patient information through distributed ledger and smart contract algorithm. In this system, smart contracts are used to automatically execute cross-institutional data access control and audit functions to ensure the transparency and compliance of data access. At the same time, this paper introduces multimedia communication technology to support the efficient transmission and sharing of medical data, especially in diagnostic images, videos and voice. Simulation results show that the electronic medical record system based on blockchain has significantly improved data processing efficiency, security and reliability compared with traditional systems.
Blockchain technology has emerged as a transformative solution for ensuring transparency, security, and immutability in student feedback management within OBE frameworks. Traditional feedback systems often suffer from inefficiencies, data manipulation risks, and lack of trust, necessitating the integration of decentralized and tamper-proof mechanisms. This book chapter explores the potential of blockchain and smart contracts in addressing these challenges by establishing a secure, transparent, and immutable student feedback system. The study examines the scalability limitations of blockchain networks and investigates advanced optimization techniques, including Layer 2 scaling solutions, sharding mechanisms, and hybrid storage models, to enhance performance and efficiency. The chapter highlights energy-efficient consensus protocols to improve sustainability in educational blockchain applications. Data availability challenges in off-chain storage and interoperability issues with existing LMS are also analyzed to ensure seamless adoption. By leveraging blockchain’s decentralized architecture, cryptographic security, and automated validation mechanisms, institutions can enhance the reliability and accountability of student feedback systems. The findings contribute to the ongoing discourse on blockchain applications in education, offering a scalable and efficient model for feedback management in OBE.
Lo‐Yao Yeh, P. C. Chiu, Guan Yu Chen, Jiun‐Long Huang · 5 authors
Distributed Denial of Service (DDoS) attacks have emerged as a significant concern for numerous platforms, websites, and servers in recent years. Employing a blacklist access control mechanism stands out as a viable defense strategy against such attacks. However, there exists a hesitation among some Security Operation Centers (SOC) engaged in gathering threat intelligence to share their knowledge, primarily due to a lack of incentives. Therefore, advocating for a fair and effective method of sharing threat intelligence emerges as a promising research area. In this paper, we put forth a proposal for a Web3-based mechanism, outlining a threat intelligence sharing scheme that incorporates incentives while ensuring transparency through the maintenance of exchange records on smart contracts for fair reward. To uphold data confidentiality, the suggested system incorporates a proxy re-encryption scheme, which maintains data in an encrypted form during exchange, thereby preventing data leakage to unauthorized persons. Additionally, our innovative scheme incorporates a novel non-false-positive-based double filter that operates without generating false positives. This advancement serves to amplify the efficiency of data requesters, enabling them to seamlessly search for pertinent information while effectively excluding irrelevant data sets and safeguarding data privacy. According to our simulation results, the execution time of proxy re-encryption in our proposed scheme demonstrates a significant improvement of approximately 65%-70% compared to other existing schemes. This enhancement underscores the efficiency and robustness of our proposed approach in mitigating the impact of DDoS attacks.
Bitcoin is the world's first decentralized cryptocurrency, using blockchain technology to secure and verify transactions. A hybrid model based on stochastic configuration network (SCN) with conformal prediction is proposed in this study. Initially, an SCN model is built, and predictions are generated by the model. Subsequently, the predicted values from SCN are fed into the conformal prediction model, resulting in the generation of confidence intervals that validate the reliability of these values. Finally, the dataset of historical Bitcoin prices sourced from Wikipedia have been utilized. The results indicate that the SCN-conformal prediction combination enhances prediction reliability.
Udayveer Singh Virk, Devansh Verma, Gagandeep Singh, Prof. Sheetal Laroiya Prof. Sheetal Laroiya
Abstract—This project aims to develop a web3 platform that stores user credentials on the blockchain, providing high levels of security and privacy. Using a range of tools and technologies, including Metamask, RemixIDE, Ganache, Node.js, Solidity for smart contracts, HTML, and CSS, the platform offers a user-friendly interface that enhances the user experience. Smart contracts are used to ensure that user credentials are only visible to the individual user, providing a high level of security and privacy. This platform has the ability to revolutionize how users interact with online services and manage their digital identities, reducing costs, increasing trust, and improving expandability. The implementation of this project has demonstrated the overall benefits of blockchain and smart contracts in virtual identity management, including increased security, improved privacy, and enhanced user experience. The platform has the potential for further development and expansion, including the integration of biometric authentication, artificial intelligence and machine learning algorithms, and the expansion to include a range of online services. Overall, this project has demonstrated the significant potential of blockchain technology and smart contracts in digital identity management and has the ability to shift the way users communicate with online services, offering a one-stop-shop for their online needs. Keywords—Block chain, metamask, ganache, remix ide, solidity
Shamsa Kanwal, Saba Inam, Zara Nawaz, Fahima Hajjej · 6 authors
IoT enables the emergence and implementation of smart devices to address real-world problems and challenges. Today we are surrounded by various smart devices, such as smart phones, smart homes, smart cars, smart televisions, and smartwatches that assist us in making our lives easier and smoother. IoT is a conglomeration of multiple technologies at various layers to impart the best of ubiquitous and pervasive computing to deliver several benefits in a variety of application sectors such as medicine, agriculture, and industry. In an IoT setting, blockchain technology is used for addressing security challenges and eradicating third-party participation. Utilizing public networks for storing or transmitting health care images poses risks of eavesdropping, data breaches, and unauthorized access. Before uploading medical data to the decentralized network, encryption is required to prevent unauthorized access. The aim of this study is to integrate technologies to ensure transactions are conducted safely and securely. We proposed an IoT-Blockchain system based on chaos encryption scheme using Tinkerbell mapping to ensure medical data integrity and authenticity. The suggested approach is examined to assess performance parameters such as, key space analysis, key sensitivity analysis, Information Entropy (IE), histogram, correlation of adjacent pixels, Number of Pixel Change Rate (NPCR), Unified Average Changing Intensity (UACI), Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE) and Structural Similarity Index (SSIM). These findings demonstrated that the suggested method is extremely efficient in avoiding security breaches and guaranteeing information integrity.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Biometric authentication has been used in applications in various environments as a secure authentication method in computing systems.When combined with blockchain technology, the security of the biometric authentication system can be further enhanced.In this paper, we propose a biometric authentication system that does not expose the original biometric information during the user's biometric authentication process by utilizing a fully homomorphic encryption.In addition, our proposed authentication system utilizes Ethereum's smart contract, which is one of the most famous public blockchains, to record the authentication log between the user and the service provider in a distributed ledger to enhance accountability and traceability.The system is designed to be used only after obtaining the consent of the biometric data subject(user) to comply with the privacy law represented by the European General Data Protection Regulation (GDPR).Finally, we show that the proposed system can process biometric information while maintaining confidentiality, integrity, and accountability of users via security analysis.The cost of maintaining the proposed system is acceptable by analyzing computation time and blockchain maintenance cost.
This paper presents an innovative application of blockchain technology utilizing smart contracts to offer crop recommendations tailored to the agricultural community.Leveraging Ethereum's solidity language, we developed a decentralized system enabling farmers to access real-time data on crop selections made by their peers.The system aggregates information on crop preferences and land allocations, facilitating informed decision-making for individual farmers.Our implementation ensures data integrity and transparency, crucial for fostering trust among users.Through empirical evaluation, we demonstrate the efficacy of our approach in providing accurate and timely crop recommendations, thereby enhancing agricultural productivity and sustainability.Our findings highlight the potential of blockchain technology to revolutionize traditional agricultural practices by fostering collaboration and data-driven decision-making.We conclude by discussing future avenues for research and adoption, emphasizing the scalability and accessibility of our proposed solution in addressing broader agricultural challenges.
Fictitious products have emerged as a substantial challenge in the manufacturing sector, inflicting adverse consequences on a company's financial health, reputation, and overall prosperity. Thankfully, blockchain technology provides an effective remedy for discerning fake items and verifying the legitimacy of authentic ones, all within a decentralized and widely distributed digital ledger. Quick Response (QR) codes serve as pivotal tools in the fight against fictitious goods, as each product is now furnished with a QR code that acts as a direct bridge to the blockchain system, essentially bestowing each item with a digital identification card. QR code scanners communicate with the blockchain to promptly validate whether a product is genuine or fictitious. Moreover, customers can harness this technology to track a product's journey through the supply chain and authenticate ownership details, akin to a digital breadcrumb trail that securely preserves product information and unique codes as database blocks. Considering the globalized business landscape and the perpetual advancements in technology, industrial manufacturers and distributors are wholeheartedly committed to optimizing their supply chain processes, ensuring they remain one step ahead of fictitious product proliferation and fortifying their operations against the dissemination of spurious items. In this paper we compare Different Fake product identification Methods like Barcodes, QR Codes, RFID Tags, Serial numbers with methods using Blockchain technology which is having high security, transferability & traceability throughout Supply Chain.
Abstract: This paper investigates the integration of an Intrusion Detection System (IDS) within the context of blockchain technology. The objective is to enhance the security posture of blockchain networks by detecting and mitigating potential intrusions. Through a meticulous examination of the current threat landscape and the unique challenges posed by blockchain systems, this research proposes a robust IDS framework tailored to the specific requirements of decentralized and distributed ledger environments. The study employs [specific methodology/approach] to assess the effectiveness of the proposed IDS, presenting conclusive findings that contribute to the ongoing discourse on securing blockchain ecosystems. The implications of this research extend to bolstering the resilience of blockchain networks against emerging threat.
This article has been retracted by Hindawi, as publisher, following an investigation undertaken by the publisher [1]. This investigation has uncovered evidence of systematic manipulation of the publication and peer-review process. We cannot, therefore, vouch for the reliability or integrity of this article. Please note that this notice is intended solely to alert readers that the peer-review process of this article has been compromised. Wiley and Hindawi regret that the usual quality checks did not identify these issues before publication and have since put additional measures in place to safeguard research integrity. We wish to credit our Research Integrity and Research Publishing teams and anonymous and named external researchers and research integrity experts for contributing to this investigation. The corresponding author, as the representative of all authors, has been given the opportunity to register their agreement or disagreement to this retraction. We have kept a record of any response received.
The Internet of Things (IoT) system is a complex environment where various entities and devices communicate. IoT is used in fields like transportation, healthcare, and monitoring, linking smart devices with sensors through the Internet to collect data from the physical world. To protect this sensitive data from hackers, an IoT platform or architecture that ensures end-to-end privacy and security is essential. Although there are many security and privacy solutions for IoT that offer basic security needs like confidentiality, integrity, and authentication, traditional solutions fall short for the large-scale IoT paradigm due to its heterogeneous nature and resource-constrained devices. Hence, this research focuses on decentralized architectures. Block chain (BC) technology is gaining attention for addressing security and authoritarian issues. In this approach, a block chain smart contract is updated with an attack detection contract.
Cognitive radio (CR) is the best way to improve the efficiency of spectrum consumption for wireless multimedia communications. Spectrum sensing, which allows legitimate secondary users (SU) to find vacant bands in the spectrum, plays a vital role in CR networks. When cooperative sensing is used in CR networks, spectrum availability must be taken into account. In many ways, the shared cooperative spectrum sensing (CSS) data among SU. The presence of a malicious user (MU) in the system and sending false sensing data can degrade the performance of cooperative CR. The sharp rise in mobile data traffic causes congestion in the licensed band for the transmission of signals. Handling this security issue in real time, on top of spectrum sharing, is a challenge in such networks. In order to manage the spectrum and identify MU, blockchain-based CSS is developed in this article. To gauge the efficiency of the proposed topology, performance metrics like sensitivity, node selection, throughput measurement, and energy efficiency are used. This work suggests a unique, easier-to-use CSS method with MU suppression that outperforms the current one. According to simulation studies, the suggested topology can increase the likelihood of MU detection by roughly 15% when 40% of system users are malicious.
B. Sindhusaranya, R. Yamini, Manimekalai Dr.M.A.P., Geetha Dr.K.
The proliferation of Internet and Communication Technologies (ICTs) has ushered in a period often referred to as Industry 5.0. The subsequent development is accompanied by the healthcare industry coining Healthcare 5.0. Healthcare 5.0 incorporates the Internet of Things (IoT), enabling medical imaging technologies to facilitate early diagnosis of diseases and enhance the quality of healthcare facilities' service. Nevertheless, the healthcare sector is currently experiencing a delay in adopting Artificial Intelligence (AI) and big data technologies compared to other sectors under the umbrella of Industry 5.0. This delay may be attributed to the prevailing concerns about data privacy within the healthcare domain. In recent times, there has been a noticeable increase in the use of Machine Learning (ML) enabled adaptive Internet of Medical Things (IoMT) systems with different technologies for medical applications. ML is an essential component of the IoMT system, as it optimizes the trade-off between delay and energy consumption. The issue of data fraud in classical learning models inside the distributed IoMT system for medical applications remains a significant research challenge in practical settings. This paper proposes Federated Learning and Blockchain-Enabled Privacy-Preserving (FL-BEPP) for Fraud Prevention and Security (FPS) in the IoMT framework. The system incorporates numerous dynamic strategies. This research examines the medical applications that exhibit hard constraints, such as deadlines, and soft constraints, such as resource consumption, when executed on distributed fog and cloud nodes. The primary objective of FL-BEPP is to effectively detect and safeguard the confidentiality and integrity of data across many tiers, including local fog nodes and faraway clouds. This is achieved by minimizing power use and delay while simultaneously meeting the time constraints associated with healthcare workloads.
The Internet of Things (IoT) technology in various applications used in data processing systems requires high security because more data must be saved in cloud monitoring systems. Even though numerous procedures are in place to increase the security and dependability of data in IoT applications, the majority of outside users can decode any transferred data at any time. Therefore, it is essential to include data blocks that, under any circumstance, other external users cannot understand. The major significance of proposed method is to incorporate an offloading technique for data processing that is carried out by using block chain technique where complete security is assured for each data. Since a problem methodology is designed with respect to clusters a load balancing technique is incorporated with data weights where parametric evaluations are made in real time to determine the consistency of each data that is monitored with IoT. The examined outcomes with five scenarios process that projected model on offloading analysis with block chain proves to be more secured thereby increasing the accuracy of data processing for each IoT applications to 89%.