MARBIYAT TAHIR GIDADO, BASHIRU ABDULGANIYU, MOHAMMED NASIR MUSA, Umaru Umaru
The increasing digitalization of smart grids has significantly improved the efficiency, reliability, and sustainability of modern power systems. However, the integration of advanced technologies, such as artificial intelligence, the Internet of Things, and cloud computing, has introduced new cybersecurity vulnerabilities that threaten critical energy infrastructure. This study presents a blockchain-enabled privacy-preserving Artificial intelligence framework designed to enhance cybersecurity in smart grid environments, with a particular focus on Northeast Nigeria as a case study. The framework integrates blockchain technology, federated learning, differential privacy, edge computing, and artificial intelligence (AI)-driven intrusion detection into a unified architecture to provide secure, intelligent, and privacy-aware protection for smart grid systems. The proposed framework was developed using the design science research methodology and evaluated through simulation and comparative performance analysis. The framework achieved excellent detection performance with an accuracy of 96.8%, precision of 95.9%, recall of 96.4%, and F1-score of 96.1%, significantly outperforming conventional centralized AI and blockchain-only approaches. The integration of federated learning and differential privacy effectively protected consumer information with a privacy leakage rate of only 2.7% while maintaining high model utility of 94.8%. The blockchain performance evaluation showed a transaction latency of 184.6 Ms, a throughput of 421.3 transactions per second, and efficient smart contract execution. The suitability of the framework for practical deployment with moderate resource requirements by computational assessment. The findings demonstrate that combining blockchain, privacy-preserving learning, and AI provides a comprehensive, scalable, and resilient cybersecurity solution for SGIs. This study contributes to the growing body of knowledge on smart grid cybersecurity and offers practical insights for utility providers, researchers, and policymakers seeking to strengthen the security and resilience of emerging smart grid systems, particularly in developing regions with infrastructural challenges.
Rafael Hoffmann, Carlos Moratelli, Alex S. R. Pinto
ABSTRACT Background Preserving the quality and safety of perishable products requires continuous monitoring and reliable traceability. Although the Internet of Things (IoT) enables real‐time data collection, multi‐organizational supply chains lack a common mechanism for assigning data custody while maintaining transparency, integrity, and performance. Objective This study proposes and evaluates an architecture integrating IoT, edge/fog computing, and hybrid storage—an off‐chain traditional database combined with a permissioned blockchain—to monitor and trace perishable products. Methods A prototype was implemented using IoT devices and simulators, edge and fog components, and hybrid storage. High‐volume sensor data and critical records were stored off‐chain in MongoDB, while their corresponding hashes were stored on‐chain using Hyperledger Fabric. Four controlled experiments assessed insertion response time, the impact of increasing sensors and edge devices, blockchain queue performance under burst workloads, and blockchain storage consumption. The hybrid approach was compared with MongoDB‐only and Hyperledger Fabric‐only storage. Results Hybrid storage achieved insertion up to six times faster than blockchain‐only storage. Response times increased with simultaneous requests and additional edge devices, while asynchronous ordered insertion prevented transaction conflicts during bursts. The prototype achieved 18.5 transactions per second, below the 65 estimated for an illustrative supply‐chain scenario. Blockchain storage grew approximately 8 MB per 100 records, reaching about 1 GB for 12,800 hashes. Conclusion The prototype demonstrates the feasibility of combining off‐chain storage, permissioned blockchain records, and edge/fog processing to provide verifiable traceability while reducing on‐chain load. Larger‐scale, real‐world evaluations and storage‐management strategies remain necessary.
This study is based on proliferation of IoT devices has created demand for high-rate, low-cost microtransactions, yet conventional blockchains impose fees, latency, and throughput limits that hinder scalable IoT finance. This study aimed to evaluate a hybrid architecture that places microtransactions on a lightweight DAG(IOTA/Tangle-style) plane while periodically anchoring compact commitments to Ethereum to reconcile performance with public auditability. The study implemented a reproducible simulation that ingests IoT telemetry (TON_IoT-style), a DAG transaction and tip-selection model at the edge and samples Ethereum fee/confirmation priors from Google BigQuery public datasets to generate realistic on-chain settlement costs and latencies. Scenario attacks (flooding, replay, recipient-entropy) were injected, and detection models (sequence + +graph features) were evaluated. DAG operation yielded near instant local confirmation (≈1.4 s) and ≈1200 tx/s throughput versus ≈13.2 s and ≈14 tx/s on Ethereum. Periodic anchoring reduced amortized per-payment cost from ≈$0.72 (naive on-chain) to ≈$0.0144 with modest finality delay (median ≈600s), a ≈98% cost reduction. F1 detection was high (≈0.85-0.91) with an acyclic graph (DAG). Ethereum is a practical approach for transparent, secure IoT financial transactions since it preserves DAG performance while providing immutable auditability at negligible amortized cost. Exploring privacy-preserving anchors as well as multi-gateway resilience has become a stepping stone for future research.
Abstract: The evolution of monetary systems has transformed human civilization from simple barter exchanges to sophisticated digital financial ecosystems powered by blockchain technology. This review examines how barter systems evolved into con-temporary virtual currencies across history and assesses how cryptocurrencies fit into the circular economy. The study explores the shortcomings of conventional monetary systems and looks at how decentralized, transparent, and effective forms of economic transaction have been made possible by digital currencies like Bitcoin. Additionally, the study examines how blockchain technology might be used to support waste reduction, sustainability, resource efficiency, and transparent supply chain management. The study also assesses the difficulties posed by virtual currencies, such as market volatility, cybersecurity threats, regulatory ambiguity, and environmental issues pertaining to cryptocurrency mining. The review identifies significant research gaps and future prospects for incorporating virtual currencies into sustainable economic systems by synthesizing the body of existing work. The results indicate that through openness, decentralization, and technological innovation, blockchain-enabled financial systems have a great deal of potential to promote circular economy goals. Keywords: Virtual Currency, Cryptocurrency, Bitcoin, Blockchain, Circular Economy, Sustainable Finance, Digital Economy, Decentralization, Green Finance, FinTech, Supply Chain Management
Blockchain interoperability remains a major challenge because heterogeneous blockchain networks cannot securely and efficiently exchange cross-chain data and transactions. Existing interoperability solutions often rely on central relays or trusted intermediaries, creating security vulnerabilities, limited fault tolerance, and a single point of failure. To address these limitations, this paper proposes VeriMesh, a decentralised mesh-based interoperability framework that combines trust-adaptive routing, multi-path relay verification, and Zero-Knowledge Proof (ZKP)-based validation for secure cross-chain communication. VeriMesh models relay nodes as a trust-weighted graph in which routing decisions dynamically adapt based on node behaviour and delivery reliability. Multi-path routing improves resilience against adversarial relay nodes, while transport-layer ZKP verification enables privacy-preserving validation without exposing sensitive information. The framework was implemented using Python relay nodes, Solidity smart contracts, and an Ethereum (Ganache) environment. Experimental evaluation using structured event-driven workloads demonstrated stable latency below 34 ms and delivery success rates above 85% up to 40% malicious node presence. Comparative evaluation against single-path and random multi-path relay baselines showed improved fault tolerance and routing reliability. The results demonstrate favourable scalability and robustness within the evaluated network range ( N = 10–30), while larger-scale evaluation remains future work. All experiments were conducted in a controlled local Ganache blockchain environment rather than on a public Ethereum testnet or mainnet, so the reported latency, gas, and delivery figures characterise protocol-layer behaviour under controlled conditions and should not yet be interpreted as representative of performance under public-network conditions such as real gas markets, block propagation delays, or network congestion.
The tokenization of Real-World Assets (RWAs) represents a paradigm shift in bridging traditional financial instruments with decentralized infrastructures. However, as the market transitions from proof-of-concept to institutional scale, it faces a critical structural bottleneck: the "walled garden" liquidity crisis. Driven by stringent regulatory requirements, tokenized assets are currently deployed across fragmented, permissioned blockchain networks utilizing static, hard-coded compliance logic. This siloed architecture inherently restricts cross-chain mobility, fracturing secondary market liquidity and necessitating redundant authentication processes across jurisdictions. This paper proposes a comprehensive architectural framework to resolve the interoperability trilemma inherent in regulated digital assets. By synthesizing recent advancements in cross-chain messaging protocols and Zero-Knowledge Proofs (ZKPs), we present a model for dynamic compliance. This framework utilizes Decentralized Identifiers (DIDs) and off-chain verifiable credentials to decouple regulatory logic from underlying asset ledgers, enabling seamless asset transfer across heterogeneous blockchains without compromising privacy or jurisdictional adherence. Ultimately, this research provides a technical and regulatory roadmap for policymakers and protocol developers to foster a unified, globally liquid market for tokenized RWAs.
The increasing demand for trustworthy and privacy-preserving credit reporting systems has exposed the limitations of both centralized and existing blockchain-based solutions, including scalability bottlenecks, weak privacy protection, and insufficient incentive mechanisms. To address these challenges, we propose LightCred, a novel consortium blockchain-based personal credit management framework that integrates lightweight nodes, Merkle proofs, multi-role smart contracts, and privacy-preserving cryptographic techniques. LightCred features a five-layer architecture that efficiently collects, verifies, stores, and serves credit data while ensuring data integrity, confidentiality, and regulatory compliance. Specifically, it (i) employs a low-cost and traceable data reduction mechanism through lightweight nodes and Merkle proofs to minimize storage and improve verifiability; (ii) introduces a multi-role smart contract model that enforces dynamic access control and fair incentive distribution based on participant reputations; and (iii) integrates zero-knowledge proofs and homomorphic encryption to support privacy-preserving credit scoring and querying. Experimental results demonstrate that LightCred achieves superior performance compared to five baseline methods, delivering up to 5% higher throughput, 3–5% lower privacy leakage, and 10–15% reduced storage costs, while maintaining competitive latency and auditability. These findings validate LightCred as a robust, scalable, and privacy-aware credit management solution, offering a viable alternative for modern credit reporting systems.
Agnes Nalini Vincent, Nassirah Laloo, Mohammad Sameer Sunhaloo, Uhoze Bagurubumwe
Digital elevation model (DEM)-based terrain analysis is an important geographic information system (GIS) methodology that serves as the core aspect for spatial analysis applications in geomorphological studies. These analyses can be performed using cloud-based platforms like Google Earth Engines or ArcGIS online, or using a local GIS platform called quantum GIS (QGIS). The resulting terrain data must be disseminated. Geospatial data sharing and dissemination are crucial for promoting cooperation, effectiveness, efficiency, and optimized decision-making in a variety of industries. Geospatial terrain data plays a crucial role in site selection and industrial planning, automated logistics, autonomous vehicle navigation, and infrastructure resilience in manufacturing ecosystems. However, existing literature states that traditional systems lack mechanisms to detect tampering in elevation models, land surveys, or hydrological data. Because of this, manufacturing systems face risks such as flawed factory site selection, disrupted supply routes, or unsafe autonomous vehicle navigation, data tampering in production logs, 278 counterfeit parts in supply chains, and a lack of real-time traceability. Hence, to manage the limitations of conventional terrain data storage and handling, this study proposes a blockchain-based framework to secure QGIS-processed terrain data, ensuring immutability and traceability for smart manufacturing applications. Blockchain distributed ledgers can permanently store high-resolution terrain data, minimize the chance of unintended alterations, and promote transparent, unrestricted collaboration. Using the country of the Republic of Mauritius as a case study toward tropical island states, this work demonstrated how elevation, slope, and aspect data extracted via QGIS can be securely stored and verified on a distributed ledger. Furthermore, this study incorporates an integration layer into the framework. The purpose of this integration layer is to enable real-time terrain alerts, smart contract-driven compliance checks, and to arrive at closed-loop feedback from IoT sensors. Thus, this proposed framework bridges blockchain-secured terrain data with manufacturing execution systems (MES) and IoT-enabled logistics networks.
Imagine the hunt to predict Bitcoin&s;s wildly swinging price as a high-stakes competition among four clever computer programs, because investors really need to know where it&s;s headed to make smart plans. Our study pitted these programs—the classic ARIMA, the modern Facebook Prophet, the powerful XGBoost, and the deep-learning LSTM network—against each other to see which could best guess future Bitcoin prices. Using two main report cards, the MAE and RMSE scores, we found that Prophet and ARIMA were neck-and-neck, but the XGBoost model completely missed the mark, proving highly inaccurate with very high error scores. However, the true champion turned out to be the LSTM neural network, which blew the others out of the water by delivering the lowest error scores on both test and training data, essentially making it the most reliable tool for anyone looking to build a winning strategy in the tricky world of crypto trading.
Abstract - The rise of digital technology has led to an increase in cybercrime. This has made the management of digital forensic evidence more complicated. Traditional evidence management systems utilize manual methods and centralized databases. Methods like these are vulnerable to data tampering, unauthorized access, and human error. These issues threaten the integrity of the evidence and the chain of custody during the investigation process. In this paper, we introduce a system that utilizes blockchain technology, smart contracts, and a decentralized system for the tracking of forensic evidence. Security and transparency will be guaranteed. In our system, evidence records are stored as ERC-721 Non-Fungible Tokens. A private Ethereum blockchain was developed using Ganache and combined with wallet-based authentication and Role-Based Access Control to ensure that only authorized personnel have the ability to view and manage evidence. Smart contracts facilitate the registration, verification, transfer, and auditing of evidence, thus, considerably reducing the manual work and greatly increasing the trustworthiness of the system. We proposed a hybrid system of storage whereby evidence and its forensic files are stored off chain, and the evidence metadata and its forensic files are stored on chain. This paper presents the design and architecture of the system,implementation and evaluation are in progress.Our system will be a trusted, efficient, and effective system of evidence management.
Background: Maritime container shipping carries over 80% of global trade, yet compliance verification creates a confidentiality–verifiability conflict: carriers treat telemetry as commercially sensitive, while regulators, insurers, and port authorities require verifiable proof that cargo remained within specification. The EU Ecodesign for Sustainable Products Regulation (ESPR) mandates Digital Product Passports (DPPs), but no standardised DPP architecture exists for the multi-stakeholder maritime domain. Methods: We present Ocean DPP, a blockchain-anchored platform combining GS1 EPCIS 2.0, oneM2M, IOTA, and Groth16 zero-knowledge proofs (ZKPs), letting stakeholders verify compliance predicates without revealing raw sensor values; Merkle-tree batching reduces anchoring costs. We evaluate it in 16 experiments on a single-host testbed using synthetic workloads and a local IOTA network. Results: The platform achieved 95th-percentile latency of 48 ms without ZKP and 500 ms with proof generation, throughput of 7 events/s per host, 304 ms mean proof generation and 9.8 ms verification, 100% EPCIS 2.0 compliance, and zero permanent message loss across four failure-injection scenarios; horizontal scaling reduced the median latency by 37%. Conclusions: To the best of our knowledge, Ocean DPP is the first implemented, quantitatively evaluated platform integrating EPCIS 2.0, oneM2M, IOTA, and Groth16 ZKPs for privacy-preserving maritime DPPs; broader multi-host and public-network validation remains for future work.
Cryptocurrencies have emerged as a prominent asset class characterized by rapid price fluctuations, growing institutional participation, and continuing debate over whether their price movements are random or predictable. This study examines the randomness and weak-form market efficiency of the top ten cryptocurrencies by market capitalization—Bitcoin, Ethereum, Tether, Binance Coin, XRP, USD Coin, Solana, TRON, Dogecoin, and Hype liquid—using daily closing price data from April 2016 to March 2026 (subject to data availability for each coin). Daily log returns were tested using Descriptive Statistics, the Jarque–Bera test of normality, the Wald–Wolfowitz Run Test, and the Autocorrelation Test. The results show that daily returns for all selected cryptocurrencies are non-normally distributed, exhibiting excess kurtosis and skewness. The Run Test results indicate that seven of the ten cryptocurrencies—Bitcoin, Ethereum, Tether, Binance Coin, XRP, USD Coin, and Dogecoin—do not follow a random walk, while Solana, TRON, and Hype liquid exhibit randomness consistent with weak-form efficiency. However, the Autocorrelation Test reveals strong positive serial correlation across all ten cryptocurrencies, indicating that the market falls short of weak-form efficiency. The study concludes that the cryptocurrency market provides mixed and largely inefficient evidence with respect to the Random Walk Hypothesis, implying that historical price information may retain some predictive value for investors.
When we consider the PoW (proof-of-work) in the Bitcoin blockchain, how is the work calculated? How does this work convert to energy quantities? This paper demonstrates that in the Bitcoin blockchain, "Proof-of-Work" (PoW) is not a complex calculus equation, but rather a probabilistic brute-force search. Miners repeatedly run block header data through a cryptographic hash function, tweaking variables until they output a number that meets a strict network threshold. In the Bitcoin blockchain, Proof-of-Work (PoW) is a probabilistic brute-force search where miners repeatedly run block headers through a double SHA-256 hash function to find an output below a global target threshold. The mathematical "work" is quantified by the network Difficulty (D), requiring roughly D × 2³² expected hashes per block. To convert this cryptographic effort into physical energy, the global network hashrate is first derived by dividing total block hashes by Bitcoin’s 10-minute target block time (600 seconds). This computational rate is then bridged to the physical world using hardware efficiency—measured in Joules per Terahash (J/TH)—multiplied by operational time. Because modern semiconductor ASICs operate roughly seven orders of magnitude above the absolute thermodynamic limits outlined by Landauer's principle, nearly all electricity consumed by this cryptographic pipeline directly converts into waste heat. The calculation of this work, how it translates mathematically to network metrics, and how those metrics convert into physical energy quantities is the discussion of this paper.<b>Part 1: How the "Work" is Calculated</b><b>1. The Hashing Puzzle (Double SHA-256)</b>A miner constructs a block header containing transaction data, a timestamp, the hash of the previous block, and a changing variable called a nonce. They pass this header through the SHA-256 algorithm twice:<br>H(x) = SHA-256(SHA-256(Block Header))The resulting output is a 256-bit unsigned integer, typically represented as a 64-character hexadecimal string.<b>2. The Target (</b><b>T</b><b>)</b>The network enforces a global threshold called the Target (T). For a block to be accepted, the hash output interpreted as a massive 256-bit integer must satisfy:<br>Hash Output ≤ T<br>Because the output of a cryptographic hash function is completely random and uniformly distributed, miners cannot predict the output. Finding a valid hash is essentially a Bernoulli trial (like rolling a die with an astronomical number of sides).<b>3. Mathematical Definition of Difficulty (D)</b>Because the Target T is a massive 256-bit number that changes every 2,016 blocks, Bitcoin uses a human-readable metric called Difficulty (D), scaled relative to a baseline "genesis" target (T<sub>max</sub>).<br>T<sub>max</sub> = 0x00000000FFFF0000000000000000000000000000000000000000000000000000The difficulty formula is D = T<sub>max</sub>/TAs the network gains more miners, T drops (becomes smaller), making hashes harder to find, which increases D.<br>The expected number of hashes E[hashes] required to find a valid block at a given difficulty is proportional to D:E[hashes] = D × 2³² × T/T<sub>max</sub> (scaled to baseline expectations)<br>More simply, the total expected hashes per block is roughly:Expected Hashes ≈ D × 4.295 × 10⁹<b>Part 2: From Computational Work to Energy Quantities</b>Energy consumption is a byproduct of hardware efficiency operating over a span of time to execute these hash attempts. There is no direct algorithmic conversion from a hash to Joules in the protocol code; instead, the conversion bridges cryptographic operations and thermodynamic hardware efficiency.<b>Step 1: Calculate Total Network Hashrate (H</b><sub><strong>net</strong></sub><b>)</b>The global hashrate represents the total number of hashes computed per second across all active machines globally. It is derived directly from the current difficulty (D) and Bitcoin's target block time (t = 600 seconds or 10 minutes):<br>Hashes per block = D × 2³²<br>Network Hashrate (H<sub>net</sub>) = D × 2³²/600 [hashes/second or H/s]<b>Step 2: Factor in Hardware Efficiency (EF)</b>ASIC (Application-Specific Integrated Circuit) miners dominate Bitcoin mining. Their electrical efficiency is measured in Joules per Terahash (J/TH) or Watts per Gigashash. Let the aggregate hardware efficiency of the network be denoted as EF (expressed in Joules per Hash, J/H):EF = Total Power Consumption (Watts)/Hashrate (H/s)<b>Step 3: Energy Derivation Formula</b>To calculate the total energy consumed by the entire Bitcoin network over a specific timeframe (e.g., 1 second, 1 day, or 1 year), we multiply the network hashrate by the hardware efficiency and time (t):<br>Energy (E) = H<sub>net</sub> × EF × Δ tSubstituting H<sub>net</sub> into the equation:<br>E = (D · 2³²/600) × EF × Δ t<br>For example, assume a network difficulty (D) of roughly 80 × 10¹² (80 trillion). Also, assume an average fleet hardware efficiency (EF) of 25 Joules per Terahash (25 × 10⁻¹² J/H). Calculate energy consumed over 1 day (Δ t = 86,400 seconds):Hashes/sec = 80 × 10¹² × 4,294,967,296/600 ≈ 5.72 × 10²⁰ H/sPower (Watts) = (5.72 × 10²⁰ H/s) × (2.5 × 10⁻¹¹ J/H) ≈ 14,300,000,000 W = 14.3 GWEnergy over 1 day = 14.3 GW × 24 hours ≈ 343.2 GWhThe summary of the conversion pipeline may be expressed as<br>Target (T) ⟶ Difficulty (D) ⟶ Network Hashrate (H<sub>net</sub>) ⟶× Hardware Efficiency (J/H)⟶ Power (Watts) ⟶× Time⟶ Energy (Joules/kWh)<b>Part 3: Thermodynamic Limits and Efficiency Bounds (Landauer's Principle)</b>To fully connect cryptographic work to physical energy, we can look at the theoretical minimum energy required by the laws of physics to perform computation.<b>1. Landauer's Principle</b>Landauer's principle establishes the minimum possible amount of energy required to erase or irreversibly manipulate a bit of information at a given temperature (T<sub>temp</sub>):<br>E<sub>min</sub> = k<sub><em>B</em></sub> T<sub>temp</sub> ln(2)k<sub><em>B</em></sub> is the Boltzmann constant (1.380649 × 10⁻²³ J/K).T<sub>temp</sub> is the absolute temperature of the environment (e.g., 300 K).For a single bit modification at room temperature, this absolute thermodynamic floor is roughly 2.8 × 10⁻²¹ Joules per bit.<b>2. Comparing SHA-256 to the Thermodynamic Limit</b>A single SHA-256 calculation involves processing a 512-bit message block through 64 rounds of complex logical operations (bitwise additions, rotations, and shifts), manipulating hundreds of thousands of bits cumulatively.Theoretical minimum energy per hash: Factoring in the sheer number of bit operations inside SHA-256, even a reversibly ideal computer would require thousands of bit manipulations, putting a strict physical floor on a single hash well above Landauer's limit (roughly on the order of 10⁻¹⁹ to 10⁻¹⁸ Joules per hash under optimal theoretical conditions).Actual ASIC efficiency: Modern state-of-the-art ASIC miners (like the Bitmain Antminer S21 series) operate around 15 to 20 J/TH (1.5 × 10⁻¹¹ Joules per hash).Comparing real-world hardware (10⁻¹¹ J/H) to absolute physical limits (10⁻¹⁸ J/H) reveals that current silicon-based semiconductor technology is roughly 7 orders of magnitude away from theoretical thermodynamic efficiency—meaning nearly all energy put into Bitcoin mining converts directly into waste heat.<b>Part 4: Complete Comprehensive Master Equation</b>Combining all components into a single macro-equation, the total daily electrical energy (E<sub>day</sub>) consumed by the global Bitcoin network can be calculated directly from the network's current Difficulty (D) and the average hardware efficiency fleet-wide (EF<sub>avg</sub> in J/TH):E<sub>day</sub> = (D · 2³²/600) × (EF<sub>avg</sub> × 10⁻¹²) × 86,400<br>Where:<br>D · 2³² / 600 yields the Network Hashrate (hashes/sec).EF<sub>avg</sub> × 10⁻¹² scales Joules-per-Terahash down to Joules-per-Hash.86,400 converts seconds into one full day.This mathematical coupling ensures that as network security (Difficulty D) scales up over time to attract more capital and hashpower, energy consumption scales linearly with it, modulated only by the parallel improvement rate of semiconductor manufacturing efficiency (EF<sub>avg</sub>).To recap the end-to-end framework:The Work: Quantified by the difficulty D and scaled via 2³² to determine total expected hashes per block.The Hashrate: Derived by dividing total hashes per block by the target 10-minute block time (600 seconds).The Energy Conversion: Bridged physically using the hardware's efficiency metric (Joules per Terahash, or J/TH) multiplied over time.The Physical Bound: Bounded by thermodynamic limits like Landauer's principle, explaining why modern ASICs produce the massive amounts of waste heat characteristic of the Bitcoin network.
Abstract This research examines the adoption of blockchain and fintech innovation in emerging markets, focusing on the drivers, barriers, and regulatory dynamics. Using a cross-sectional quantitative survey of 114 fintech leaders and entrepreneurs across 60 emerging-market countries, the research examines perceptions of blockchain’s role in cost reduction, efficiency, and financial inclusion through decentralized finance (DeFi), tokenized assets, and digital wallets. Findings show respondents broadly agree that blockchain fosters new business models and competitive advantage, perceive strong benefits in transparency, cost reduction, and efficiency, and hold favorable views of regulatory support, clear guidelines, and sandboxes, while still recognizing regulatory, organizational, and technological barriers to adoption. Fintech leaders reported significantly higher familiarity and stronger belief in blockchain’s potential than entrepreneurs. The study applies Institutional Theory, the Technology–Organization–Environment framework, and Disruptive Innovation Theory to highlight policy, organizational, and technological implications. Because the sample was purposive and responses were uniformly positive, the findings describe the perceptions of engaged practitioners rather than statistically generalisable or audited adoption outcomes.
Abstract The exponential growth of IoT networks has made the supply of trustworthy digital IDs more important than ever. Limitations in scalability, transparency, and resilience to single points of failure are some of the inherent issues with modern centralised identity management systems. These problems are exacerbated in distributed IoT systems since there is no central authority to rely on for communication and trust establishment among the many devices and various parties involved. By introducing SecureChain-ID, a system that uses blockchain technology to provide distributed identity issuance, authentication, and lifecycle management, this article aims to solve the restrictions that now exist. For resource-constrained Internet of Things (IoT) devices, the system's use of elliptic curve cryptography (ECC) provides digital signatures that are both lightweight and efficient. Credentials may be verified with zero-knowledge proofs (ZKPs) as they do not divulge any personally identifiable information. The blockchain's smart contracts streamline the authentication and registration processes, enabling the widespread agreement on the smooth addition of new administrators and devices. The persistent documentation of all identity-related transactions ensures auditability and safeguards against manipulation. This includes updates, revocations, and access events. Unlike conventional approaches, SecureChain-ID establishes network-wide accountability and paves the way for decentralized governance to be aligned with administrative control, enabling flexible permissioning. Many Internet of Things (IoT) systems can benefit greatly from the proposed approach due to its emphasis on data security, low trust assumptions, and interoperability. This research successfully bridges the conceptual gaps between blockchain, identity, and Internet of Things (IoT) systems by introducing an efficient design that improves future cyber-physical systems' secure identification infrastructure.
As the blockchain and decentralized finance (DeFi) ecosystems continue to expand and mature, rug pull scams involving meme coins are occurring with increasing frequency, posing a threat to the security of investors' assets and the healthy development of the industry. Rug Pull scams are characterized by extremely low deployment costs, covert execution, rapid fund transfers, and high detection difficulty. Traditional manual reviews or fixed rules struggle to meet real-time early warning requirements, and existing detection methods generally suffer from issues such as a single feature dimension, inadequate handling of class imbalance, and weak model generalization and interpretability. To address these shortcomings, this paper focuses on the detection of Ethereum-based rug pull scams. First, we clarify their definitions, types, and harm mechanisms, and construct a multi-dimensional feature system based on dimensions such as malicious smart contract design, on-chain transaction anomalies, liquidity manipulation, and social media disclosures. Next, using the "Second Uncle Coin"(token symbol: BOBU) case as an example, we reconstruct the attack process and derive quantitative detection metrics. Subsequently, a risk detection model based on a Multi-Layer Perceptron (MLP) is designed. We employ a combined strategy of SMOTE oversampling and Focal Loss to address the issue of sample imbalance, dynamically search for optimal thresholds to balance precision and recall, and incorporate gradient pruning and early stopping to enhance training stability. Experiments show that the model achieves an accuracy of 0.927, an F1 score of 0.787, and an AUC-ROC of 0.952 on the test set, outperforming traditional methods. Finally, a visualizable web-based detection system is developed using the Flask framework, enabling batch risk assessment, high-risk ranking display, and result export functions.
Blockchain technology, initially developed as a backbone for cryptocurrencies, has rapidly expanded into broader domains of finance and business. Its unique attributes – transparency, decentralization, immutability, and enhanced security – offer solutions to persistent challenges in financial services. This chapter examines blockchain applications beyond cryptocurrency, focusing on its role in transforming financial services such as Know Your Customer (KYC), cross-border payments, and compliance. A case-based exploration of blockchain-enabled KYC demonstrates how distributed ledgers can streamline identity verification while ensuring trust and efficiency. The chapter also outlines benefits, limitations, and potential applications, contributing to a holistic understanding for policymakers, financial institutions, researchers, and practitioners.
This study examined blockchain technology's potential to enhance audit efficiency in selected service firms. Traditional auditing is often hindered by data manipulation, limited transparency, time-consuming verification, and high costs challenges that blockchain's decentralised, immutable, and transparent ledger system can plausibly address. The research assessed blockchain's role in improving audit efficiency, focusing on automation and realtime auditing, distributed ledger effects, and consensus mechanisms. Data was gathered through questionnaires, observation, and a technical readiness survey, and analysed using descriptive statistics, inferential statistics, and multiple regression. The study evaluated current auditing practices to identify the benefits and barriers of blockchain implementation, examining existing audit challenges, blockchain's capacity to resolve them, and the implications for service firms. Respondents were drawn from service firms with interest or prospects in adopting blockchain for audit activities. Findings showed marked improvements in audit accuracy, transparency, and overall efficiency, though adoption barriers, cost, and the need for regulatory structures were also identified. The study contributes to the growing body of knowledge on blockchain's practical application in auditing, offering guidance to service firms, audit practitioners, and policymakers on successful implementation. By addressing these challenges and leveraging blockchain's opportunities, service firms can achieve cleaner, safer, and more efficient audit practices. The research confirms that blockchain technology plays a significant role in enhancing audit efficiency within service firms.
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Blockchain Technology Applications and Security
Innovations and Analysis in Business and Education
Ensuring transparency and traceability in horticultural supply chains is difficult due to complex logistics, seasonal variability, and multiple intermediaries.We propose a framework that couples b-local irregular vertex coloring (b-LIVC) with a blockchain architecture to enable end-to-end verification of production and trade.On the Jember Regency subdistrict graph, we compute the b-local irregular chromatic number and obtain χ b-lis (J) = 6, yielding six planting color classes that schedule sowing and harvests to distribute output across the year.The local irregularity induces distinct neighborhood weights, which we use as cryptographic features for unique, verifiable batch identifiers.We implement the pipeline on a public blockchain: harvest lots are tokenized as video NFTs with QR links to a verification page and on-chain records.The integration of discrete mathematics and distributed ledgers provides auditable provenance and transaction history, practical scheduling that reduces harvest clustering, and a low-overhead mechanism for farmer-level transparency.
This paper assesses the adequacy of technology-neutral privacy frameworks in addressing quantum threats to zero-knowledge proofs (ZKPs) and other privacy-enhancing technologies (PETs) in global data protection regimes. Challenging assumptions that cryptographic innovation inherently bolsters privacy rights, the analysis demonstrates how post-quantum migration, absent binding regulatory duties, risks entrenching a ‘quantum divide’ in access and liability. Grounded in legal frameworks and actual deployments, including Zcash’s classical Zero-Knowledge Succinct Non-Interactive Arguments of Knowledge (ZK-SNARKs) and NantHealth Inc.’s quantum-aware homomorphic encryption systems, the paper contends that access to PETs is becoming ever more determined by institutional capability and geopolitical factors, as illustrated by comparative case studies. This research evaluates the efficacy of statutes such as the European Union’s (EU) General Data Protection Regulation (GDPR) (Article 32), the California Consumer Privacy Act (CCPA) (§ 1798.150), and the Health Insurance Portability and Accountability Act (HIPAA) (45 C.F.R. § 164.308) in imposing liability for quantum vulnerable systems, using the cases to illustrate gaps in mandating equitable post-quantum migration. The conclusion reflects upon legal gaps enabling unequal protections, advocating reforms including mandatory quantum risk assessments. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.