O presente artigo analisa a gestão de estado em blockchains públicas, com foco em técnicas de pruning e estratégias de arquivamento eficiente, diante do crescimento contínuo de dados e da necessidade de equilibrar segurança, disponibilidade histórica e custos de infraestrutura. Em redes baseadas em máquinas de estado, como aquelas compatíveis com a Ethereum Virtual Machine, o estado global – composto por contas, contratos e dados de armazenamento – é mantido em estruturas de dados do tipo árvore de Merkle-Patricia (MPT), que crescem monotonicamente à medida que novas transações modificam o estado, levando nós completos e de arquivo a consumirem dezenas de terabytes após alguns anos de operação em produção. Essa dinâmica impõe desafios estruturais a operadores de nós, que precisam escolher entre manter histórico completo, realizar pruning de blocos e estados antigos ou recorrer a nós especializados para consultas históricas, afetando diretamente o grau de descentralização e o custo de participação na rede. A metodologia adotada combina revisão bibliográfica de pesquisas em bancos de dados para blockchains, documentação técnica de clientes de nós – entre os quais Geth, Erigon e equivalentes em outras redes – e estudos recentes sobre statelessness, expiração de estado (state expiry), árvores de Verkle e bancos de dados forkless, discutindo as implicações dessas abordagens para a gestão de estado de longo prazo. Os resultados indicam que estratégias de pruning ao nível de blocos e de estado, combinadas a modelos de nós diferenciados (full, pruned, archive, light) e a técnicas de instantâneos (snapshotting) e arquivamento externo, permitem reduzir significativamente o volume de armazenamento exigido de nós validadores sem sacrificar a capacidade de validação e a segurança da cadeia. Por outro lado, a dependência crescente de archive nodes e de infraestruturas especializadas para consultas históricas levanta questões relevantes acerca de centralização e do custo de reconstrução do estado em cenários adversos, estimulando pesquisa em modelos de clientes sem estado (stateless clients), provas compactas de estado e bancos de dados desenhados especificamente para as cargas de trabalho de blockchains. Conclui-se que a gestão eficiente de estado constitui componente crítico da sustentabilidade de longo prazo da Web3, demandando abordagens integradas que combinem técnicas de pruning, desenho criterioso de estruturas de dados, políticas explícitas de retenção histórica e modelos econômicos que incentivem a operação de nós com diferentes perfis de armazenamento.
O presente artigo examina técnicas de compressão de dados em blocos de blockchain e sua relação com a redução de custos de armazenamento on-chain, considerando tanto o consumo de espaço em disco pelos nós da rede quanto o custo econômico de inclusão de dados em transações. Em plataformas como o Ethereum, parcela significativa do custo de transações – especialmente para rollups e aplicações que utilizam calldata de maneira extensiva – está associada ao armazenamento e à propagação de bytes de dados na camada base, o que tem motivado a adoção de estratégias de compressão de calldata, ajuste de parâmetros de gas e a introdução de novas formas de armazenamento temporário, a exemplo de blobs de dados. A metodologia empregada baseia-se em revisão bibliográfica de trabalhos sobre estratégias de armazenamento on-chain, análise de propostas de melhoria do ecossistema (Ethereum Improvement Proposals – EIPs) voltadas à redução de custos de calldata, exame de relatórios técnicos sobre compressão de dados em soluções de camada 2 e análise recente do impacto de mecanismos como o EIP-4844 (proto-danksharding) sobre a estrutura de custos de rollups. As evidências levantadas indicam que técnicas de compressão aplicadas ao payload de transações podem reduzir da ordem de cinco vezes o tamanho efetivo dos dados enviados, traduzindo-se em economias de aproximadamente 50% no gasto de gas para determinadas operações em redes de segunda camada, sem exigir mudanças disruptivas no protocolo subjacente. Paralelamente, abordagens estruturais – entre as quais a separação de dados de disponibilidade em blobs não permanentes e o emprego de rollups que publicam apenas provas e raízes de estado, em vez de dados completos – contribuem para reduzir a pressão de armazenamento permanente sobre os nós da camada 1. Conclui-se que a compressão de dados em blocos, combinada a ajustes de modelo de dados (blobs, rollups, off-chain storage) e a técnicas de compressão clássicas (run-length, delta, dicionário, entropia), constitui peça central na estratégia de escalabilidade e sustentabilidade econômica da Web3, não obstante levante desafios relevantes quanto à complexidade de implementação, compatibilidade entre clientes e preservação da verificabilidade de longo prazo.
Blockchain technology has revolutionized various industries by offering transparency, security, and decentralization. The critical aspect of blockchain technology is the consensus protocol, which plays a pivotal role in ensuring the integrity and reliability of distributed ledger systems. The selection of an appropriate consensus protocol for a given blockchain application is a complex and multifaceted decision-making process, influenced by various technical, environmental, and operational factors. This paper presents an integrated multicriteria decision-making (MCDM) approach to facilitate the selection of an optimal blockchain consensus protocol. Through a comprehensive evaluation of criteria, including performance, sustainability, incentives, security, and decentralization, our approach provides a robust decision-making framework for consensus protocol assessment. The results prioritize the importance of performance and security factors in blockchain consensus protocol evaluation. The sensitivity analysis is performed to determine the impact of experts’ weight coefficients on the result. The results prioritize the importance of performance and security in blockchain consensus protocol selection.
This study investigates the role of artificial intelligence (AI) tokens in dynamic interactions, diversification, and hedging capabilities, in relation to non-fungible tokens (NFTs), decentralised finance (DeFi) tokens, and renewable energy assets. Using the Time-Varying Parameter Vector Autoregressive (TVP-VAR) model, we examine return, volatility, and higher-order spillovers across both time and frequency domains. The results show that NFTs serve as persistent channels for the transmission of return and volatility shocks, driven by their speculative nature. AI and renewable tokens primarily absorb systemic risk due to their lower liquidity and niche adoption. DeFi tokens play flexible roles, shifting between transmitters and receivers across market regimes. The results demonstrate asset-specific idiosyncrasies and that volatility spillovers are generally stronger than return spillovers. Frequency-domain analysis highlights that digital tokens dominate short-term spillovers, while renewable assets absorb shocks across horizons. However, higher-order moment results reveal that extreme risk linkages shift transmission channels. Our results also confirm that oil market (OVX) shocks drive short-term return connectedness, CBOE volatility (VIX) volatility, and policy uncertainty (EPU) significantly impact return linkages. The results of our portfolio analysis show that AI tokens form the core of diversification, NFTs provide short-term speculative hedging, and renewable assets, particularly solar-linked tokens, act as low-cost stabilisers, underscoring the need for active rebalancing under different market regimes. These findings provide meaningful implications for policymakers, regulators, and portfolio managers for strengthening systemic risk oversight and considering asset-specific idiosyncrasies in investment strategies.
Smart contracts have become a cornerstone of modern blockchain ecosystems by enabling decentralized, transparent, and autonomous execution of digital agreements. Despite their widespread adoption, smart contracts continue to suffer from two persistent challenges: inefficient execution and critical security vulnerabilities. These limitations not only increase operational costs but also undermine trust in blockchain-based systems. This research paper presents a comprehensive and plagiarism-free investigation into smart contract optimization with a strong emphasis on security-driven design principles. The study analyzes execution inefficiencies, gas consumption patterns, and architectural constraints across major blockchain platforms, alongside prevalent vulnerabilities such as reentrancy attacks, integer overflows, access control flaws, and logic inconsistencies. Building upon this analysis, the paper proposes an integrated optimization–security framework that combines code-level optimization, modular design, formal verification, automated vulnerability detection, and hybrid on-chain/off-chain computation models. The proposed approach demonstrates how efficiency and security can be jointly enhanced rather than treated as isolated objectives. The findings aim to guide developers, researchers, and practitioners in designing smart contracts that are cost-effective, secure, and resilient within rapidly evolving blockchain environments.
Healthcare AI systems put a lot of importance on keeping medical data private because it is very sensitive. AI-driven diagnostic models could help doctors make better decisions, but they need a lot of different patient data sets, which are often kept separate from each other at different hospitals. Federated Learning (FL) is a decentralised way to solve this problem by letting multiple people train a model together without sharing data in one place. But conventional FL frameworks continue to encounter challenges related to trust, transparency, and data integrity. This paper puts forth a Blockchain-Enabled Federated Learning Framework to facilitate secure, privacy-preserving, and auditable medical diagnosis across decentralised healthcare systems. This system uses blockchain's unchangeable nature and smart contract features to make sure that model updates can't be changed, contributions can be tracked, and trust between the entities involved is higher. This combination makes AI-driven diagnostics possible without putting patient privacy, regulatory compliance, or institutional integrity at risk.
This study presents an ontology-driven framework, EthOn, aimed at enhancing the operational efficiency of the Ethereum blockchain. Despite Ethereum’s prominence as a decentralized platform for executing smart contracts, it still faces significant limitations such as high gas fees, low throughput, and limited semantic processing capabilities. These challenges primarily stem from the platform's reliance on syntactic data structures and the absence of context-aware mechanisms in smart contracts. The proposed framework integrates semantic ontologies to structure blockchain data and enhance smart contract logic through context-driven reasoning. By embedding domain-specific ontologies into the Ethereum layer, EthOn enables intelligent data interpretation, optimized contract execution, and improved interoperability for decentralized applications (dApps). A prototype developed using Protégé, Node.js, Web3.js, Ganache Network and Apache Jena demonstrated notable advancements in both execution speed and gas efficiency. The results demonstrated remarkable improvements: Ethereum required an average block creation time of 10–15 seconds with gas consumption fluctuating between 9.6 million and 44.4 million units, while EthOn achieved block generation within 1–2 seconds and maintained gas usage between 423,630 and 3.7 million units. This represents an overall gas reduction of nearly 90% and a substantial improvement in execution speed.These results highlight the transformative potential of semantic technologies in reshaping blockchain infrastructure, paving the way for more scalable, efficient, and intelligent decentralized systems.
The escalating energy consumption of blockchain networks has intensified concerns regarding their environmental sustainability, particularly in consensus protocols derived from Proof of Work. Although Proof of Stake improves efficiency, existing mechanisms remain static and lack responsiveness to dynamic network and energy conditions. This paper presents an Energy-Adaptive Consensus Mechanism (EACM) that integrates real-time workload awareness with energy-sensitive validator selection to optimize power utilization without compromising security. The proposed model introduces a multi-factor adaptive control layer that adjusts validation intensity based on transaction throughput, node availability, and energy profiles. A carbon awareness incentive function is incorporated to prioritize validators operating on renewable or low-carbon energy sources. Prototype implementation is developed on a private Ethereum-based test network, and comparative experiments are conducted against conventional Proof of Stake under variable workloads. Results indicate measurable reductions in energy consumption while maintaining competitive throughput, latency, and fault tolerance. The findings demonstrate that adaptive consensus design can enhance blockchain sustainability and provide a viable pathway toward carbon-efficient distributed ledger infrastructures.
Bitcoin has two cryptographic layers. SHA-256 secures mining and the hash chain — it has no periodicity, no rhythm, nothing for the quantum Fourier transform to detect. ECDSA secures signatures — it has rhythm, and Shor's algorithm breaks it. The foundation is quantum resistant. The signatures are not — but signatures are a software upgrade. The hash rate is not. Bitcoin is a shadow-mirror coupling: single hash (shadow, local, independent) coupled to blockchain (mirror, global, irreducible) through proof of work, with a self-regulating coupling constant κ ≈ 128,748 measured across 2,906 days and 12 orders of magnitude. Extended construction from Shadow & Mirror: Complementarity of Computation and Consciousness (Ross, 2026).
Federated Learning (FL) is an approach that allows numerous users to train a single machine learning model with the oversight of a central server, and with their training data stored locally on their devices. The approach is relevant in alleviating the risks associated with violations in data privacy. It is a process by which a pool of clients collaborates towards solving machine learning problems, with a central coordinator being the one who coordinates the entire process. The paper will review the latest advances in privacy-preserving federated learning and discuss it in the context of machine learning. It assesses privacy-related solutions, which are already in existence, such as secure aggregation, meta-learning, blockchain technology, decentralized training, searchable encryption, and data privacy mechanisms and zero-knowledge proofs. Federated learning (FL) is an emerging technology that can be used in the realm of the intelligence of the Internet of Things. However, the information that is model-related can be shared in FL and reveal the sensitive data of the participants. In this regard, we propose a new privacy-preserving FL framework, which is founded on a new chained secure multiparty computing technique, which we call chain-PPFL. The scheme we are proposing is based mostly on two mechanisms: 1) a single-masking mechanism, which protects the information that is exchanged between participants in a serial chain frame and 2) a chained-communication mechanism, which allows the masked information to be communicated between participants in a serial chain frame. We run large-scale experiments with respect to simulation by comparing the training accuracy and the leak defence to other state-of-the-art schemes with two publicly available data sets (MNIST and CIFAR-100). We established data sample distributions (IID and NonIID), and training models (CNN, MLP and L-BFGS) in our experiments. The experiment results show that the chain-PPFL scheme can offer a realistic privacy preservation (which is the same as the various privacy with ϵ to near zero) to FL at the cost of communication, and without compromising the accuracy and convergence rate of the training model.
This paper systematically reviews the research foundation, core technologies, and practical applications of cryptography in the blockchain field. Algorithms, and data immutability relies on cryptographic hash functions and Merkle tree structure; the balance between transparency and privacy in block chain relies on the encryption technique of zero-knowledge proofs, ring signature, homomorphic encryption. Therefore, every part of block chain is based on cryptography; without the mathematical guarantee of cryptography, the trust decentralized by block chain is meaningless. The security of block chain mainly relies on the encryption techniques such as hash functions, digital signatures and encryption algorithms, and traditional cryptographic methods will have vulnerabilities when facing quantum computing, because quantum computer may be used to break currently commonly used algorithms such as RSA, ECC eventually. This “security paradox" requires us to pay more attention to block chain technologies, because block chain technology needs to advance in tandem with cryptography. Traditional blockchain technologies can’t be used indefinitely. Against this background, researching block chain －based crypto is of great theoretical significance and practical value: on the one hand, researching on new cryptographic methods applicable to block chain can extend the area of cryptosystems and give people a new way of solving the security problems in block chain; on the other hand, we should not neglect the possibility of breaking the block chain by combining quantum computing with cryptanalysis research.
Federated learning represents a paradigm shift in distributed machine learning by enabling collaborative model training across decentralized nodes while maintaining data privacy at source locations. It helps bridge the gap between artificial intelligence-driven development guidelines and the regulatory mandates laid down by data protection legislation. A decentralized architecture transmits only the model updates to aggregation servers; this reduces privacy breach exposure and compliance violation risks and also eliminates raw data centralization. Federated learning helps build production-ready systems across healthcare, finance, and edge computing environments, owing to the maturities that have occurred in cloud infrastructure. This is a transition from the erstwhile theoretical frameworks it used to have. Architectural advantages are supplemented by privacy-preserving mechanisms like differential privacy and secure aggregation protocols, which facilitate organizations to leverage collective intelligence without exposing sensitive information. Robust platforms for privacy-critical applications can be synthesized by the integration of cloud-native security services, cryptographic enhancements, and edge computing optimization. Courtesy of emerging solutions that cater to model fairness, communication efficiency, and data heterogeneity, federated learning's practical applicability across diverse organizational contexts and regulatory domains continues to advance.
The advent of 5G networks has introduced a paradigm shift in communication infrastructure, facilitating ultra-low latency and high-speed data transmission. Despite this, this progress is accompanied by a spike in diverse and sophisticated cyberattacks, for which there is no comprehensive, foolproof defence strategy. In order to address the Scalability Trilemma—achieving decentralization, scalability, and trust—and security concerns, this study proposes a robust security framework that combines blockchain technology with Zero Trust Architecture (ZTA). The proposed framework presents an end-to-end coherent workflow in four successive stages: (i) Access Request Initiation with contextual metadata, (ii) Decentralized identity verification via blockchain-based Decentralised Identifiers (DIDs) and Verifiable Credentials (VCs), (iii) Context-aware Dynamic Access Control enforced through smart contracts, risk scoring, and cryptographic mechanisms such as Zero Knowledge Proofs (ZKPs) and Multi-Factor Authentication (MFA), and (iv) Time-bound, least-privilege access provisioning with continuous session monitoring and immutable logging. The model, which is proposed to be strategically implemented at the 5G network's device (access) layer, affirms real-time enforcement while maintaining accountability, privacy, and verifiability. Our research delivers a fully decentralized, tamper-resistant, and scalable architecture capable of dynamically mitigating advanced cyber threats, while ensuring secure delivery of 5G services across diverse use cases.
<div> Large Language Models (LLMs) are transforming 1 blockchain security and analytics, yet a system-2 atic evaluation of their capabilities remains limited. 3 This survey provides a comprehensive, AI-centric 4 assessment of LLM-based methods across over 70 5 recent studies spanning 11 application domains, 6 such as security auditing, transaction fraud de-7 tection, and cryptocurrency portfolio management. 8 Our unified taxonomy standardizes task formula-9 tions and evaluation practices to enable a com-10 parison of six LLM roles across domains. For 11 each domain, we review input representations tai-12 lored to blockchain data; LLM architectures, learn-13 ing and inference paradigms, e.g., fine-tuning, 14 retrieval-augmented generation, and agentic strate-15 gies. Our review analyzes the strengths, limita-16 tions, and emerging patterns of LLM roles observed 17 in current systems. Finally, we provide practi-18 cal guidance for selecting LLMs for specific roles 19 and outline promising research directions. The on-20 line resources of this survey are available on https: 21 //llmblockchain.github.io/LLMBlockchain/. 22 1 Introduction 23 Large Language Models are increasingly incorporated into 24 blockchain systems for both security and financial analyt-25 ics, including smart contract auditing, transaction monitoring, 26 fraud detection, market analysis, and decentralized finance 27 </div>
Cloud storage systems have become an essential component of modern data management, enabling users to store and access data remotely. However, traditional cloud storage architectures rely on centralized servers, which introduce critical challenges such as single-point failure, redundant data storage, high storage costs, and security vulnerabilities. In earlier systems, data was stored in centralized data centers where duplicate files were often saved multiple times, leading to inefficient utilization of storage resources. Although basic deduplication techniques were used, they frequently compromised data confidentiality and lacked transparency in metadata management. Moreover, failure of the central server could result in permanent data loss. To overcome these limitations, this research system integrates blockchain technology, InterPlanetary File System (IPFS), Convergent Encryption (CE), and heuristic-based chunking techniques to create a secure and decentralized storage framework, hereafter named Blockchain-enabled Heuristic Optimized Deduplication Model (BHODM). In this system, files are divided into optimized chunks using a heuristic method based on file size. Each chunk undergoes CE, where the encryption key is derived from the hash of the data itself, enabling secure deduplication without exposing plaintext information. Duplicate chunks are identified using hash comparison, ensuring that only unique data is stored. The encrypted chunks are stored in IPFS, a decentralized peer-to-peer storage network that eliminates reliance on a single server. Metadata such as file names, block numbers, and hash values are securely stored in an Ethereum blockchain smart contract, ensuring immutability and transparency. The system is implemented using Django for the web application, Web3 for blockchain interaction, IPFS Application Program Interface (API) for distributed storage, and Advanced Encryption Standard in Counter Mode (AES-CTR) encryption for security. By combining decentralized storage, blockchainbased metadata management, and secure deduplication, the proposed model effectively reduces storage overhead, enhances data integrity, and mitigates single-point failures. The system is further evaluated using storage utilization and computation time analysis, demonstrating improved efficiency compared to traditional approaches
The rapid evolution of cloud computing has revolutionized digital data storage and sharing, enabling users to access information anytime and anywhere. Despite these advantages, cloud-based systems face major challenges related to data security, privacy protection, and trust management, particularly when handling sensitive user information. Conventional cloud storage solutions operate on centralized architectures, where a single cloud service provider manages and controls the stored data. This centralized model introduces significant risks, including single points of failure, unauthorized data access, data manipulation, and limited visibility into data-sharing activities. In many traditional systems, data protection mechanisms rely on basic encryption methods without strong auditing or verification features, leaving them vulnerable to insider attacks and external cyber threats. Furthermore, the absence of immutable transaction records and robust key management practices reduces accountability and weakens user confidence in cloud environments. To address these shortcomings, the proposed system presents a secure cloud data sharing framework that combines Elliptic Curve Cryptography (ECC) with blockchain technology. In this approach, user files are encrypted using ECC before being uploaded to the cloud, ensuring strong data confidentiality and protection against unauthorized access. Simultaneously, blockchain technology is employed to record file metadata and transaction details in a decentralized and tamper-resistant ledger, enabling transparent and verifiable audit trails. The decentralized architecture eliminates reliance on a single authority, enhances trust, and prevents unauthorized modification of stored records. Additionally, secure authentication and controlled access mechanisms further reinforce system security. By integrating advanced cryptographic encryption with decentralized verification, the proposed solution enhances data integrity, improves transparency, and establishes a reliable and accountable framework for secure cloud data sharing.
This paper introduces Context-Bounded Sovereign Intelligence (CBSI) — a framework for training and deploying small language models exclusively within the operating environment they inhabit. Rather than training models on all human knowledge, CBSI trains models on one world only: the sovereign infrastructure they operate within. The paper demonstrates that a 3-billion parameter model with deep contextual knowledge of its operating environment outperforms general large language models on every bounded task — with lower latency, lower cost, greater privacy, and zero hallucination on in-context operations. Includes empirical foundation from 2026 research literature, architectural patterns validated through live deployment of Project Chimera across three continents, and implications for distributed sovereign AI infrastructure. Proof of concept deployed in 48 hours by one person for $2.88. Built with love. Given away freely.
Blockchain technology has evolved from its initial application in cryptocurrencies such as Bitcoin to a versatile decentralized infrastructure supporting decentralized finance (DeFi), digital identity systems, smart contracts, and Web3 ecosystems. Despite its transformative potential, the rapid expansion of blockchain platforms has significantly increased the security attack surface, exposing networks to threats such as double-spending, Sybil attacks, smart contract vulnerabilities, transaction laundering, and large-scale financial fraud. At the same time, the emergence of quantum computing introduces a fundamental challenge to classical cryptographic mechanisms particularly Elliptic Curve Digital Signature Algorithm (ECDSA) and RSA that form the backbone of blockchain authentication and transaction verification. This paper presents a comprehensive study of Machine Learning (ML) techniques and Post-Quantum Cryptographic (PQC) frameworks for strengthening blockchain security and threat detection. The study reviews supervised, unsupervised, and deep learning models used for fraud detection, anomaly identification, smart contract vulnerability analysis, and blockchain transaction monitoring. In parallel, it examines quantum-resistant cryptographic algorithms emerging from the NIST post-quantum standardization process, including lattice-based, hash-based, and code-based schemes, and evaluates their suitability for blockchain environments. Furthermore, the paper analyzes the limitations of ML-based security mechanisms and the practical challenges of integrating PQC into decentralized infrastructures, including scalability, key size overhead, and performance trade-offs. A comparative analysis highlights that ML enhances adaptive behavioral threat detection, while PQC ensures long-term cryptographic resilience against quantum attacks. Therefore, the study emphasizes the importance of a hybrid ML–PQC security model that combines intelligent anomaly detection with quantum-resistant cryptographic protection. Finally, the paper identifies key research challenges and outlines future directions toward building scalable, adaptive, and quantum-secure blockchain ecosystems capable of supporting next-generation decentralized applications.
Abstract Smart contracts hold billions of dollars worth of digital currencies, and hacking attacks can not only cause users to lose their assets but also destabilize the blockchain ecosystem.Vulnerability detection in smart contracts remains a major challenge in blockchain security. Existing methods typically rely on a fixed expert mode, which leads to low accuracy. Moreover, GNN-based models fail to effectively differentiate the significance of various interaction information, while transformer models suffer from high computational complexity. To solve this problem, we propose the GAT-Mamba framework, named GMVD, to perform the smart contract vulnerability detection task. The approach first extracts expert-defined vulnerability patterns from smart contract functions. Then, the graph features are extracted by GAT. Finally, Mamba is used to model the high-dimensional vector expression of expert mode features to improve the calculation efficiency of the model. Subsequently, graph features are extracted through GAT, and Mamba is then employed to model the high-dimensional vector representation of expert pattern features, thereby enhancing computational efficiency. Experimental results on three common vulnerabilities, reentrancy, timestamp dependency, and infinite loop, demonstrate that our framework significantly outperforms existing cutting-edge technologies. Specifically, our method achieves 94.29% accuracy in detecting reentrancy, 93.71% in timestamp dependency, and 82.49% in infinite loop detection.
This study develops and evaluates a hybrid Compliance–AI cybersecurity model for unified protection of traditional banking and decentralized finance (DeFi) systems in Brazil. Using publicly available data from the NIST Cybersecurity Framework, DeFi exploit repositories (REKT and DeFiLlama), Elliptic crypto-transaction graphs, IEEE-CIS fraud data, DARPA Transparent Computing datasets, and Monte Carlo–simulated cross-domain attack scenarios, the research applies hierarchical clustering, supervised learning, Markov chain modeling, and stochastic simulation. Results show that 45% of banking controls are transferable or hybridizable to DeFi, that embedding machine-readable compliance features improves ROC–AUC from 0.842 to 0.914 and reduces false positives by nearly 47%, and that bidirectional orchestration lowers escalation probability by over 54%. Monte Carlo analysis further indicates a 62% reduction in tail financial risk under the hybrid architecture. The study recommends machine-readable regulation, compliance-aware AI deployment, orchestrated enforcement layers, and expanded RegTech and SupTech adoption to strengthen systemic financial cybersecurity. Keywords: Compliance–AI Integration, Financial Cybersecurity, Decentralized Finance, Machine-Readable Regulation, Systemic Cyber Risk.
The proliferation of misinformation in real-time digital media demands innovative solutions for verifiable journalism. This paper introduces SolanaNet-Journal, a pioneering framework leveraging Solana's high-throughput blockchain and multi-agent AI networks to enable immutable, real-time news dissemination with embedded credibility assurance. Autonomous agents, specialized in sourcing, cross-verification, and provenance tracking, collaborate via Solana smart contracts to process breaking stories at over 2,000 verifications per second, achieving sub-second finality unattainable on legacy blockchains. Key innovations include a hybrid proof-of-history consensus fused with agent Byzantine agreement, cryptographic hashing for tamper-evident content streams, and a dynamic credibility scoring model that adapts to evolving narratives using stake-weighted incentives. Implemented on Solana devnet, the system demonstrates 92% accuracy in fact-checking live datasets from global events, outperforming centralized tools by 4x in latency and resilience to adversarial inputs. Evaluations across scalability, security, and real-world case studies affirm its robustness against deepfakes and viral falsehoods. By decentralizing trust, SolanaNet-Journal redefines journalistic integrity in hyper-dynamic media landscapes, paving the way for ethical, scalable AI-blockchain hybrids in inclusive communication ecosystems.
Abstract The pharmaceutical supply chain faces critical challenges in maintaining drug integrity, ensuring regulatory compliance, and preventing medication waste. This research presents a comprehensive quantum-resilient monitoring system integrating federated machine learning, blockchain with Proof-of-Quality consensus, hyperspectral sensing, and zero-knowledge protocols. The Temporal Attention-Enhanced Federated Ensemble (TAFE) algorithm enables privacy-preserving collaborative learning achieving 94.3% detection sensitivity, identifying degradation 45 days before conventional methods. Edge architecture with ARM Cortex-M7 reduces cloud dependency by 87% with sub-500ms latency. Blockchain achieves 1,200 + TPS with 2.8s finality. Zero-knowledge proofs enable compliance verification in 1.6s without data disclosure. Field deployment across 25 locations validates 99.7% uptime with 87% user satisfaction.
Blockchain technology has evolved incredibly into various domains other than cryptocurrencies such as healthcare, genomics application, agriculture, government schemes, land asset distribution, DeFi, IoT, supply chain management due to its decentralized and secured nature. Consensus mechanism in blockchain networks serves as the backbone to ensure data integrity, provenance, immutability and security. Traditional consensus mechanism faces many challenges like utilization of high energy or carbon, excessive computational resources, staking of cryptocurrency, high reputation of nodes, maximum votes received, scalability and security issues. To tackle this concerns many researchers has proposed solutions and given a comparative analysis of the performance of these algorithms. This paper gives the survey reviews of the consensus mechanism used so far with a comparative analysis on the performance metrics like scalability, latency, and throughput, degree of decentralization, energy and resources efficiency etc. We have divided the consensus algorithms based on two categories i.e Proof based and Acquiescence based. The study highlights critical trade-offs among scalability, energy efficiency, decentralization, fault tolerance, and security resilience. Furthermore, this paper sheds the light on recent innovations addressing mitigation strategies like sharding, off-chain solutions, checkpoint mechanism, and integration of machine learning for anomaly detection, prediction of attack vectors. By systematically comparing consensus protocols and identifying open research challenges, this review aims to provide researchers and practitioners with a clear understanding of current consensus landscapes and provide valuable guidance to the selection and design of suitable mechanisms for next-generation blockchain systems.
This chapter explores the intersection of data governance, data quality, and blockchain technology, presenting a paradigm shift from traditional centralized data management to decentralized architectures. As data solidifies its role as a critical asset, ensuring its integrity and trustworthiness has become paramount. We begin by establishing the principles of data governance and quality, highlighting the limitations of conventional systems that rely on trusted intermediaries, which introduce single points of failure and censorship risks. Blockchain technology emerges as a compelling alternative, offering a decentralized, immutable, and transparent ledger that fundamentally enhances data integrity and trust. Through an analysis of its core components – including cryptographic hashing, consensus mechanisms, and distributed networks – we examine the inherent advantages and disadvantages of blockchain. The chapter delves into the functional extensions of blockchain, such as smart contracts and Decentralized Autonomous Organizations (DAOs), which enable automated, transparent, and autonomous governance models. However, the transition to blockchain is not without its challenges. We critically assess issues of scalability, data privacy, the “oracle problem,” and the “garbage in, garbage out” principle, which persist in decentralized environments. The chapter concludes that the “quality” of blockchain as a data management solution is not absolute but is contingent upon the specific requirements of the use case, demanding a careful evaluation of its trade-offs.