This study explores how blockchain technology can be strategically applied to improve data security and privacy across key sectors, including healthcare, finance, and supply chain management. Through a modular, Python-based implementation using permissioned blockchains, the research evaluates performance metrics such as transaction speed, data integrity, encryption, and anomaly detection. Results indicate that blockchain systems significantly enhance data protection by ensuring immutability, decentralization, and real-time validation. In healthcare, the system effectively flagged anomalous records; in finance, it supported secure, high-volume transactions; and in supply chains, it enabled transparent, tamper-proof tracking. Despite promising outcomes, challenges such as interoperability, scalability, and regulatory compliance remain. The study also highlights ethical considerations, particularly in balancing transparency with privacy. Overall, blockchain emerges as a robust and adaptable solution for modern data security needs. The findings provide a foundation for further research and practical guidance for organizations seeking to adopt blockchain in sensitive and high-risk data environments.
N. El Inani, Faouzia Benabbou, Khadija Sabiri, Amal Zaouch
With the advancement of blockchain technology, Solidity-based smart contracts have become essential for automating and securing digital transactions across various sectors, from finance to supply chain management. These contracts enable decentralized exchanges without intermediaries, enhancing transparency. However, their immutable nature poses security challenges: any flaw in the code becomes permanent, exposing contracts to attacks and leading to financial and reputational losses. This paper provides a comparative analysis of recent machine learning (ML) and deep learning (DL) techniques developed for detecting vulnerabilities in Solidity based smart contracts. By evaluating various approaches, we assess their effectiveness in identifying common threats such as reentrancy attacks and integer overflows. Finally, we highlight the importance of scalable, AI driven security solutions to address the growing complexity of vulnerabilities.
J. Thimmia Raja, Ashish Ashish, Sindhu Boianapalli, Soma Sabitha M · 6 authors
Blockchain technology, as a growing innovation, offers a viable solution to enhance transparency, traceability, and efficiency in global supply chains. While blockchain has great potential, several challenges remain, including scalability, integration with legacy systems, standardization, energy consumption, privacy concerns, and regulatory uncertainty. This enrollment of data is hoped to tackle these problems of dilemmas, whislt subsequent improved effective equates being more reasonable in terms of deploying blockchain applications. With the goal of offering a bridge to most projects still unsure whether to adopt Blockchain technology or to continue under what we call "Blockchain in the cloud" approach, this study introduces effective solutions on how to integrate Blockchain with current systems through innovative hybrid systems, standardized user protocols, and new consensus protocols that optimize cost of implementation and environmental impact. The other aspect of the research undertakes to design clear regulatory frameworks and change management systems that can aid in the adoption of blockchain technology for traditional supply chain stakeholders. By utilizing real-world case studies and the simplification of smart contract deployment, this work illustrates the practical benefits blockchain can provide in enhancing supply chain operations. In conclusion, the goal of this study is to pioneer a sustainable, secure, and efficient blockchain ecosystem that promotes trust, transparency, and collaboration among supply chain partners, ensuring the future viability of the technology.
In developing nations like India, the agri-food supply chain faces challenges and difficulties such as minimal traceability, limited transparency, and reliance on intermediaries, which can lead to farmer abuse and eroded customer trust. This study suggests and evaluates a paradigm that combines blockchain technology with artificial intelligence (AI) to improve agri-food systems' traceability, transparency, and equity. IoT-based sensing, IPFS for decentralised data storage, Ethereum-based smart contracts, and AI-driven analytics with a Random Forest model are all integrated into the system. Using a hypothetical onion supply chain case study, a four-layer architecture comprising user interface, AI analytics, blockchain infrastructure, and IoT data capture was created and assessed. Major performance gains over conventional systems are demonstrated by the results, which include a 35% decrease in operating expenses, 90% data consistency, and 75% increased transparency. A 93% task completion rate and a System Usability Score (SUS) of 78.2 were obtained from stakeholder usability testing. Proof-of-authority consensus has been suggested as a remedy for Sybil attacks and consensus delays, which are the issues in low-connectivity areas. A suitable roadmap for implementing blockchain-AI solutions in agriculture is presented in this paper. Multilingual interfaces, offline functionality, voice assistance, federated learning for privacy-preserving AI, and real-world scaling through platforms like Polygon or Hyperledger will be the main areas of future growth.
F. B. Tebueva, V. I. Petrenko, D. Zh. Satybaldina, M. G. Ogur · 5 authors
Objective: to enhance the effectiveness of monitoring and responding to multivector attacks in a decentralized Internet of Things (IoT) environment by integrating federated learning, deep autoencoders, and the distributed IOTA ledger. The priorities include accurate attack detection, minimizing false positives, reducing response time, and preserving data privacy. Method: a problem-oriented system was developed, combining local monitoring on IoT nodes with autoencoders for anomaly detection, federated learning using the FedAvg algorithm for collective model updates, and decentralized alert dissemination via the distributed IOTA ledger. The system implements secure exchange of model parameters, digital message signing, and asynchronous response through a publish/subscribe network. Results: experimental studies on the real N-BaIoT dataset simulating multivector attacks demonstrated high detection accuracy (approximately 95%), achieving an F1-score above 94%, with false positive rates around 4%. The system's response time did not exceed 5 seconds, significantly improving operational reaction to attacks. Federated learning provided steady improvement in model quality considering data distribution and heterogeneity. The architecture proved scalable, fault-tolerant, and capable of effectively detecting complex threats across multiple system levels. Practical value: the solution is implementable in industrial IoT, smart cities, and medical networks to enhance cybersecurity while maintaining privacy and reducing network load. Scientific novelty: the study presents a comprehensive synthesis of federated learning, deep autoencoders, and distributed ledger technology for effective monitoring of multivector attacks in decentralized IoT environments. The proposed approach combines the advantages of distributed learning and blockchain mechanisms to achieve high adaptability, accuracy, and security in rapidly growing and diverse IoT infrastructures
In security and resilience, another vital issue is to maintain the secure and robust RF communication in Industrial Internet of Things (IIoT) settings in which the use of multiantenna systems is constrained by physical implausibilities.The current paper is a proposal of a Blockchain-Enhanced Secure RF Link Transmission (BESRFT) scheme targeting antennaconstrained IIoT applications.The framework for those aspects integrates adaptive tuning of the RF parameters with a lightweight blockchain distributed consensus protocol that restores assuring and even securing communication in event of a cyber-physical assault condition.The proposed structure combines a modified Proof-of-Authentication (PoA) consensus mechanism that scales well and has low overheads to communicate across the IIoT nodes via an edge and smart contracts are used to authenticate the devices, negotiate the session keys and record intrusions on the fly.To critical hardware constraints, the system dynamically adjusts transmission parameters e.g.frequency, modulation and power depending on real-time channel metrics e.g.RSSI and SINR resulting in a low degree of information loss and power overhead.The simulation results based on the tool NS-3 and Hyper ledger Besu prove the effectiveness of BESRFT as it provided a spoofing detection rate of 97.6 percent that lessened packet drop rates by 66 percent or 18.2 to 5.1 percent, and decreased jamming recovery time by 66 percent or 1.8 seconds to merely 0.6 seconds.Additionally, the validation of prototype approaches that involve TI CC2652R1 and STM32 microcontrollers also confirms that the latency (~1.2 s) and memory overhead (<5%) of blockchain synchronization are minimal, thus confirming that the solution is suitable to be deployed in the resource-constrained IIoT environments.The combined architecture does not only improve commanding situation but also increases the efficiency of an industrial system like smart grid, oil refineries, and factory automation platforms.The piece instates a decentralized trust system with the RF protection link without requiring the complicated cryptographic infrastructures and centralized control.In the future, the framework has a great potential to be scaled to reconfigurable metasurface-based antenna systems and reconfigurable IIoT architectures with 6G-enabling properties, where the secure, low-latency, and adaptive communication will play a fundamental role.
Digital supply chain payments increasingly rely on automated and distributed platforms, yet existing solutions struggle to balance transparency with the confidentiality required by commercial and financial stakeholders. While blockchain-based smart contracts enable tamper-evident settlement and traceability, they often expose sensitive transaction metadata, contractual terms, and risk indicators, limiting adoption in multi-party supply chain environments. This paper presents a privacy-preserving smart and secure contract framework for digital supply chain payments that separates correctness verification from information disclosure. The proposed model combines a permissioned or consortium blockchain with off-chain encrypted data storage, cryptographic commitment schemes, and zero-knowledge proofs to ensure that payment obligations, milestone fulfillment, and financing conditions can be verified without revealing proprietary business details. Tokenized payment obligations represent invoices and receivables on the ledger, while milestone-based smart contracts coordinate delivery confirmation, early financing, dispute resolution, and settlement. Sensitive financial data and documents remain off-chain, anchored to the ledger only through hashes, commitments, and succinct proofs. Optional confidential computing components further enable secure evaluation of dynamic pricing or credit logic. A comprehensive security analysis demonstrates resistance to unauthorized state modification, double financing, insider misuse, and inference attacks under both honest-but-curious and malicious adversary models. Performance evaluation shows that the computational and communication overhead introduced by privacy-preserving mechanisms remains practical for real-world supply chain payment workflows, with low latency, efficient storage growth, and scalable operation across multi-tier ecosystems. The results indicate that the proposed framework provides a viable foundation for secure, privacy-aware, and auditable digital supply chain finance.
Jamil Raja, D Hema Ruba, Sai Krishna, K Manasa · 6 authors
Blockchain technology has become a disruptive force that is quickly reshaping supply chain management by providing greater transparency, security, and efficiency. As promising as the Blockchain is, previous research fails to provide solutions for bringing Blockchain into real-world practice, for getting it up to mass scale, and for ensuring that it meets the regulators’ requirements and these factors limit a more widespread adoption of Blockchain. By offering a review of blockchain-based supply chain management initiatives, their pros and cons, and addressing under-researched topics related to optimized consensus mechanisms, interoperability solutions, and AI-driven solutions integration, this research fills the gaps to decrease inefficiencies in supply chains. As part of the latter, the study presents zero-knowledge proofs, decentralized identity verification and cross-chain protocols as potential solutions to address security concerns and enhance interoperability among multiple chains. Also, a deeper implementation roadmap is provided, enabling pragmatic applicability in real business operations across the global supply chain. By analyzing cases, the study emphasizes the practical contributions of blockchain in traceability, fraud prevention, inventory optimization, and automated contract execution. The results highlight blockchain as a scalable, secure, and legally compliant technology for solving modern supply chain problems, filling the gap between theory and practical adoption.
The Internet of Things (IoT) technology is widely employed in several fields, such as smart cities, intelligent transportation systems, smart power grids, smart homes, and digital healthcare solutions, to enhance the quality of life, promote sustainability, and foster social interactions. The significant challenges related to IoT encompass the legitimacy verification of intelligent devices, handling cryptographic keys for edge devices with limited resources, guaranteeing the confidentiality and integrity of transmitted IoT data, and securely storing that data in the cloud system. The Blockchain system, characterized by its distributed, decentralized, and irreversible ledger, has gained recognition for its efficacy in ensuring security for sensitive smart home applications. This paper presents a comprehensive security solution for smart homes, comprising smart sensor authentication, key management, data confidentiality, data integrity, secure storage, and access control for cloud-based data. A robust and secure end-to-end structure for smart homes is developed in two phases. During the initial stage, a private Blockchain system is established on the fog nodes to facilitate the authentication and access control mechanisms for smart home edge devices. This private Blockchain can distribute and manage keys, enabling it to securely transfer data from intelligent sensors to the cloud environment using fog nodes. A consortium Blockchain is established during the next phase to simulate the cloud service providers. This Blockchain enables the provision of secured storage and data access control within the cloud, enhancing secure cloud storage even in a hostile environment. The novel contribution of this article relies on the integration of private and consortium Blockchain networks with a streamlined collaboration. This amalgamation achieves the efficient and distributed control of multiple Blockchain validators. A secure prototype has been developed to visualize and engage with a smart home environment. This prototype effectively displays data using various graphical and interactive techniques. The performance metrics, including attack resilience, latency and throughput of the network proves the efficiency of proposed framework as compared to state-of-the-art works. In future, we focus on the cross-chain interactions in multi-Blockchain environments and address the existing limitations such as inconsistencies, interoperability issues, and inefficient security measures.
Smart home technologies have revolutionized modern living by enhancing convenience, efficiency, and security. In contrast, many interconnected devices introduce significant security and privacy challenges. This comprehensive review investigates the integration of blockchain technology as a robust solution for secure access control in smart home environments. The decentralized and tamper-resistant nature of blockchain technology effectively solves important problems, including device authentication, data integrity, and access management, through the use of cryptography and distributed ledgers. The study synthesizes findings from 52 research papers, categorizing them into three thematic areas: blockchain in access control systems, its applications in IoT, and specific implementations for smart homes. It highlights the transformative potential of blockchain in mitigating vulnerabilities inherent in centralized systems, fostering trust, and enhancing security frameworks. Despite its promising applications, challenges such as scalability, interoperability, and energy consumption persist, warranting further research. This paper stresses the necessity of collaboration to tackle these limitations and enhance blockchain-based access control solutions for smart homes, setting the stage for more secure and user-focused smart environments.
Edge Artificial Intelligence (Edge AI) represents a transformative shift in the way data is processed, analyzed, and acted upon. By combining the capabilities of artificial intelligence with the decentralized architecture of edge computing, Edge AI enables faster decision-making, enhanced data privacy, and improved operational efficiency. This manuscript explores the essential aspects of Edge AI, including its real-time responsiveness, cost efficiency, and sustainability benefits. It also highlights the wide range of industrial applications — from healthcare and manufacturing to autonomous systems — while addressing key challenges such as limited device computing power and integration complexities. As organizations increasingly adopt Edge AI solutions, the technology is paving the way toward a hybrid future where edge and cloud systems coexist, driving innovation, scalability, and intelligence across every connected ecosystem.
Modern demand for blockchain scaling demanded the developmentof sharding as a viable solution that facilitates parallel processingwhile supporting cross-shard communications. The implementation ofsharding provides excellent scalability to decentralized systems, but itentails enormous complexities in maintaining integrity of smart contracts across different shards. This study analyzes pivotal deploymentslike Ethereum 2.0, NEAR protocol, and Polkadot. This innovationidentifies vulnerabilities on atomicity, consistency, and validator security when executing decentralized applications (dApps). This researchreviews contemporary literature and emerging technologies to identifykey security risks such as replay attacks, shard takeover, and data unavailability events. Various methodologies for reducing vulnerabilities,including atomic commit protocols, dynamic validator assignment,zk-SNARKs, and SP-Chain architecture, are assessed in this report.This study evaluates the implementation of an Escrow smart contract that utilizes Practical Byzantine Fault Tolerance (PBFT) and Proofof-Stake (PoS) coordination protocols through evaluation tests. Thisresearch work analyzes code using both static and dynamic methods to detect security weaknesses in contracts and then recommendssolutions that enhance the robustness of these contracts.The intelligent contract is tested for performance in single-shardmode as well as cross-shard operations. It experiences quick responsetimes and effective data processing with single-shard execution butfaces latency, receipt verification issues, and synchronization difficulties with cross- shard operations. This study findings are that organizations require more effective systems for communication of databetween shards. This research suggests future improvement throughthe addition of zero-knowledge proofs, dynamic re-sharding procedures, and decentralized arbitration secure and scalable smart contractdeployment methods. The proposed solution is applicable to use casessuch as freelancing, crowdfunding, and supply chain processes, withdemonstrations using real-life examples.As a result of the comprehensive assessment, improved safe smartcontract frameworks for next generation blockchain systems are developed, making it easier to implement decentralized applications widelyin scale network contexts.
Luigi Pavarini de Lima, Liliam Sayuri Sakamoto, Jair Minoro Abe, Marcelo Borges Rocha · 11 authors
Considering the growing technological innovation with the use of the Metaverse as an environment for educational, corporate, and governmental interaction, in contrast to the risk of cyberattacks, there is an urgent need to strengthen its security, especially when there is the possibility of transacting assets with NFTs—Non-Fungible Tokens—which are high-value objects acquired and traded through blockchain technology. The objective of this article is to propose a research framework to optimize the security of these NFT assets using DLP—Data Loss Prevention—and Paraconsistent Logic to identify threats not only preventively but also by actively detecting loss, theft, misuse, and leakage of these types of assets during the use of the Metaverse. With a literature review on the Metaverse, DLP—Data Loss Prevention, Evidential Annotated Paraconsistent Logic Eτ, Artificial Intelligence techniques, NFTs—Non-Fungible Tokens, and data protection, the study will employ a Python program to conduct applied research using data from a transportation company, which shows a 37% data loss rate in its analysis. Through this Artificial Intelligence process and statistical concepts, compared to the minimization of data loss in the analysis using Evidential Annotated Paraconsistent Logic, Eτ resulted in 23%, indicating a significant difference of 15%, complemented as a tool to improve decision-making accuracy
Abstract: Blockchain technology is a transformative distributed ledger paradigm that enables secure, transparent, and tamper-resistant data management without centralized authorities. At its core lies the consensus mechanism-the protocol through which distributed nodes agree on a single canonical transaction history. This paper presents a structured review of major blockchain consensus schemes including Proof-of-Work (PoW), Proof-of-Stake (PoS), and Practical Byzantine Fault Tolerance (PBFT), as well as emerging hybrid models such as Avalanche and Polkadot. The analysis evaluates sustainability, scalability, security, and decentralization characteristics, offering a comprehensive comparison across these mechanisms. The findings highlight inherent trade-offs related to energy consumption, throughput, finality, validator governance, and fault tolerance. The study concludes by identifying open research challenges important for designing next-generation blockchain systems capable of supporting large-scale, mission-critical applications. Keywords: Avalanche, Blockchain, Consensus Mechanisms, PBFT, PoS, PoW, Scalability, Security, Sustainability. Title: Study and Comparative Analysis of Blockchain Consensus Mechanisms Author: Dr. N. R. Ananthanarayanan, Mr. Suresh Subbu International Journal of Recent Research in Mathematics Computer Science and Information Technology ISSN 2350-1022 Vol. 12, Issue 2, October 2025 - March 2026 Page No: 1-15 Paper Publications Website: www.paperpublications.org Published Date: 25-November-2025 DOI: https://doi.org/10.5281/zenodo.17711245 Paper Download Link (Source) https://www.paperpublications.org/upload/book/Study%20and%20Comparative%20Analysis%20of%20Blockchain-25112025-2.pdf
N Sherrin Sophia, Prisha Gupta, M Sharma, Mohona Ghosh · 5 authors
The transformation of fundraising through the use of crowdfunding platforms has come with obstacles such as fraud, verification and misuse of funds, and lack of transparency. The use of smart contracts in conjunction with Zero-Knowledge Proofs (ZKPs) of er advanced privacy and security through the validation of identity concealment during participant contributions. This privacy enhances trustworthiness and motivates user engagement. The model provides adequate allocation of funds which improves overall transparency. The solution provides a better distribution of challenges that are posed in mainstream models of crowdfunding by integrating enhanced security, eficiency, and accountability while performing fundraising through the examined approach.
Mr. Amar More, Mr . Karan More, Mr . Nikhil Neavse, Mr. Prasanna Deokar · 6 authors
This project introduces a decentralized file storage system that leverages blockchain technology to create a secure, immutable, and tamper-resistant platform for file sharing. By storing files within blocks on a blockchain, the system ensures that once data is uploaded, it cannot be altered or deleted, making it ideal for applications where data integrity is critical. Users interact with the platform through a web interface, allowing them to upload, download, and share files across a peer-to-peer network. The blockchain structure used in this project employs a Proof of Work (PoW) consensus mechanism, requiring peers (miners) to solve cryptographic puzzles to validate blocks and add them to the chain. Two different PoW methods are used: one generates nonces at random, while the other increases the nonce value one after the other. By comparing the effectiveness and security of different methods, the project finds that random nonce generation outperforms them at higher difficulty levels, providing quicker block validation and more robust defense against possible assaults. On the other hand, the incremental approach is less secure over time because it is simpler to foresee. The project also covers the advantages of on-chain storage, which involves storing files directly inside blockchain blocks. This approach offers better security but comes at the expense of more processing power. Furthermore, it investigates alternatives such as off-chain blockchain architectures for more effective file storage in subsequent iterations and Proof of Stake (PoS) for lowering resource use
Paulo Caetano da Silva, Djalma Oliveira Costa Filho, Daniel Jose Diaz, José Alan da Silva Teixeira
Blockchain has received a lot of attention for multiple use cases and applications since the first works emerged about 15 years ago, with its use targeting cryptocurrencies. During this period, a wide variety of platforms (e.g. Ethereum, Hyperledger, and others web3 Blockchain platforms), tools, programming languages and other resources such as smart contracts were proposed. With the aim of better understanding the use of smart contracts in Blockchain-based systems, this article presents a systematic review of the literature on Blockchain application architectures that make use of smart contracts, applied in different areas. It is expected to bring together approaches for the design and implementation of smart contracts on the Blockchain.
This study examines security risks, emerging technologies, and cryptographic techniques in cloud-based digital currency transactions using a quantitative research approach. Data was sourced from the REKT Database, Web3 Security Report, and Elliptic Open Dataset, employing descriptive statistical analysis, regression modeling, and time-series analysis to assess security vulnerabilities, fraud reduction trends, and regulatory compliance effectiveness. Findings reveal that AI-driven security measures reduced fraud cases by 55% from 2022 to 2025, while illicit transactions declined from 12.5% in 2019 to 6.1% in 2023, demonstrating the impact of cryptographic advancements and regulatory interventions. However, cybercriminals are shifting toward high-value, precision-based attacks, necessitating an integrated security framework. This study contributes to AI-driven security, cryptographic resilience, and regulatory compliance in cloud-based digital transactions. By analyzing emerging threats and security frameworks, it offers valuable insights for researchers, policymakers, and financial institutions. The findings aid in developing robust fraud detection, post-quantum cryptography, and regulatory strategies to enhance digital financial security. This research bridges technological advancements with compliance measures, ensuring a more resilient and secure cloud-based cryptocurrency ecosystem. The study recommends enhancing AI fraud detection with cryptographic security models, adopting post-quantum cryptography, strengthening regulatory compliance, and implementing zero-trust security models to ensure long-term resilience in cloud-based financial ecosystems.
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.
The advent of sixth-generation (6G) technology is poised to revolutionize connectivity, particularly by enhancing the integration of Internet of Medical Things (IoMT) devices. This advancement offers ultra-fast data transmission, low latency, and high mobility but also brings the challenge of ensuring secure and energy-efficient communication. To solve these challenges, this paper introduces a novel hybrid greylag goose-based optimized clustering (HGGOC) algorithm. It merges the efficiency of greylag goose optimization with the precision of the golden sine strategy. The Lévy flight mechanism guides the algorithm to optimize cluster head selection in 6G-enabled IoMT networks. The integration of blockchain technology further strengthens data security and transparency. Simulation results show that HGGOC surpasses existing methods, delivering up to 64% improvement in network stability, a 47% increase in node lifetime, and a 59% boost in energy efficiency and data throughput. These findings position HGGOC as a promising solution for sustainable communication in 6G-enabled IoMT environments.