Gilvina Grace B.A., Surahman Surahman, Muhammad Nurcholis Alhadi, Elviandri Elviandri
Cryptocurrency has emerged as a global innovation since the launch of Bitcoin in 2009, underpinned by blockchain technology that offers enhanced efficiency and transparency. Despite its potential, cryptocurrency presents complex legal challenges, particularly concerning its regulatory status. In Indonesia, cryptocurrency faces a dual regulatory framework: it is prohibited as a means of payment by Bank Indonesia pursuant to Law No. 7 of 2011 on Currency, yet simultaneously recognized as a tradable investment commodity by the Commodity Futures Trading Regulatory Agency (Bappebti). This study aims to analyze the implications of this duality for Indonesia’s national regulatory system. Using normative legal research and a statutory approach, the study reveals significant legal uncertainty arising from institutional regulatory inconsistencies. Such uncertainty may impede innovation and compromise consumer protection. Therefore, regulatory harmonization is essential to ensure legal certainty and adaptability, drawing on the theories of legal certainty and progressive law.
This study explores the role of gold-backed cryptocurrencies (PAXG and XAUT) as effective diversifiers, hedges, and safe havens for NFTs and DeFi assets, particularly during market crises such as the COVID-19 pandemic and the 2022 cryptocurrency crash. By employing a dynamic GARCH-copula approach, the research analyzes the interconnectedness and volatility spillovers between these digital asset classes, providing insights into their behavior during times of heightened uncertainty. We also compute the optimal hedge ratio for each gold-backed cryptocurrencies/stabelcoins-NFT/DeFi/Traditional cryptocurrencies pair and evaluate their dynamic hedging effectiveness. The findings reveal that gold-backed cryptocurrencies offer superior hedging capabilities compared to stablecoins (USDT and BUSD), enhancing portfolio diversification and risk management. The results underscore the importance of incorporating gold-backed assets into digital portfolios to improve resilience and achieve better risk-adjusted returns during periods of market turmoil.
Muhammad Kamran, Maaz Rehan, Muhammad Maaz Rehan, Wasif Nisar · 6 authors
Blockchain technology is increasingly being adopted across critical domains, such as healthcare and finance, yet it remains susceptible to anomalies and malicious attacks. Hence, robust anomaly detection is essential in these decentralized systems to maintain integrity, trust, and reliability. However, anomaly detection is still challenging due to data imbalances, adversarial resilience, and the lack of explanation in existing approaches. This work presents ARCADE, a novel approach for adversarially resilient anomaly detection in blockchain networks that leverages an optimized cost-sensitive stacking ensemble learning combined with explainable artificial intelligence (XAI) techniques. Firstly, the proposed approach uses cost-sensitive learning to address the data imbalance problem by optimizing class weights that are integrated with stacking ensemble learning to enhance detection accuracy. Secondly, along with this, newly engineered features are employed to strengthen the resilience of the model against malicious perturbations. Lastly, XAI techniques are applied to provide comprehensive insights and explanations for model prediction. To evaluate ARCADE, the Ethereum network transactions dataset is utilized to ensure a realistic case study. The experimental results show the superiority of the ARCADE in several aspects, achieving a high accuracy of 99.65%; strong resilience against adversarial perturbations, achieving an accuracy of 99.38% for low-intensity attacks, 91.04% for moderate attacks, and over 78% for extreme attacks; and surpassing existing techniques while also providing explainability for domain users.
The rapid expansion of 5G networks and edge computing has amplified security challenges in Internet of Things (IoT) environments, including unauthorized access, data tampering, and DDoS attacks. This paper introduces EdgeChainGuard, a hybrid blockchain-based authentication framework designed to secure 5G-enabled IoT systems through decentralized identity management, smart contract-based access control, and AI-driven anomaly detection. By combining permissioned and permissionless blockchain layers with Layer-2 scaling solutions and adaptive consensus mechanisms, the framework enhances both security and scalability while maintaining computational efficiency. Using synthetic datasets that simulate real-world adversarial behaviour, our evaluation shows an average authentication latency of 172.50 s and a 50% reduction in gas fees compared to traditional Ethereum-based implementations. The results demonstrate that EdgeChainGuard effectively enforces tamper-resistant authentication, reduces unauthorized access, and adapts to dynamic network conditions. Future research will focus on integrating zero-knowledge proofs (ZKPs) for privacy preservation, federated learning for decentralized AI retraining, and lightweight anomaly detection models to enable secure, low-latency authentication in resource-constrained IoT deployments.
Open access
Blockchain Technology Applications and Security
IoT and Edge/Fog Computing
Advanced Steganography and Watermarking Techniques
With the rapid growth of the NFT market, the security of smart contracts has become crucial. However, existing AI-based detection models for NFT contract vulnerabilities remain limited due to their complexity, while traditional manual methods are time-consuming and costly. This study proposes an AI-driven approach to detect vulnerabilities in NFT smart contracts. We collected 16,527 public smart contract codes, classifying them into five vulnerability categories: Risky Mutable Proxy, ERC-721 Reentrancy, Unlimited Minting, Missing Requirements, and Public Burn. Python-processed data was structured into training/test sets. Using the CART algorithm with Gini coefficient evaluation, we built initial decision trees for feature extraction. A random forest model was implemented to improve robustness through random data/feature sampling and multitree integration. GridSearch hyperparameter tuning further optimized the model, with 3D visualizations demonstrating parameter impacts on vulnerability detection. Results show the random forest model excels in detecting all five vulnerabilities. For example, it identifies Risky Mutable Proxy by analyzing authorization mechanisms and state modifications, while ERC-721 Reentrancy detection relies on external call locations and lock mechanisms. The ensemble approach effectively reduces single-tree overfitting, with stable performance improvements after parameter tuning. This method provides an efficient technical solution for automated NFT contract detection and lays groundwork for scaling AI applications.
Abstract Decentralized and transparent nature of cryptocurrencies have lately increased investors interest in them. Forecasting cryptocurrency’s price accurately is crucial to come up with a good investment strategy, and such a forecast requires one to consider its unique attributes as well as high volatility. Even though many existing studies have focused on analyzing the cryptocurrency transaction graph topology, studies on the analysis of transaction graph’s impact on prices are quite limited. In this paper, we explore the forecasting ability of blockchain transaction graph-based attributes on Bitcoin’s and Ethereum’s future price via deep learning methods. More specifically, we came up with motif convolution module (MCM), a motif-based graph representation learning approach to take local structural knowledge into account more strongly in node and edge-attributed transaction graphs encoding substantial structural knowledge. Our proposed MCM constructs a motif dictionary without supervision, and employs a new motif convolution operation while extracting the vertices local structural context. Afterwards, we learn high-level vertex embeddings by using such structural context via multilayer perceptron and graph neural network. Overall, we extract the attributed transaction graphs temporally-evolving low-dimensional representations, and use such embedding data together with historical prices within self-attention-based LSTM to predict the future prices accurately. Our proposed approach outperforms all considered baselines in terms of both price and price direction prediction, showing the promise of efficient integration of transaction data into cryptocurrency price prediction.
Green finance is a cornerstone of sustainable investment, but it highlights the critical importance of transparency, traceability, and financial efficiency within an environmental, social, and governance framework. This review article examines the potential of green finance as a pillar for accelerating investment in sustainable pathways, particularly using green bonds and the innovative mechanisms offered by decentralized finance (DeFi). Green bonds are highlighted as a key instrument for channeling capital toward green projects, while DeFi is explored as an innovative tool with the potential to democratize access to finance and enable micro-investments in sustainability projects with a relevant social impact. This article examines both mechanisms in terms of their ability to increase the efficiency and reliability of green finance ecosystems. The analysis also explores emerging challenges such as regulatory constraints, the threat of greenwashing, and technological limitations associated with the implementation of blockchain and artificial intelligence. By addressing these barriers, the article provides strategic recommendations for achieving greater transparency and reliability in green finance markets, thereby fostering investor confidence and broader market growth. It also identifies research gaps and proposes new avenues to advance the integration of sustainable finance, ensuring its scalability and inclusion in the promotion of global sustainability. Received: 16 November 2024| Revised: 17 February 2025 | Accepted: 27 February 2025 Conflicts of Interest The author declares that she has no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Natália Teixeira: Conceptualization, Methodology, Investigation, Writing – original draft, Writing – review & editing, Visualization, Funding acquisition.
Cryptocurrencies are digital tokens built on blockchain technology, with thousands actively traded on centralized exchanges (CEXs). Unlike stocks, which are backed by real businesses, cryptocurrencies are recognized as a distinct class of assets by researchers. How do investors treat this new category of asset in trading? Are they similar to stocks as an investment tool for investors? We answer these questions by investigating cryptocurrencies' and stocks' price time series which can reflect investors' attitudes towards the targeted assets. Concretely, we use different machine learning models to classify cryptocurrencies' and stocks' price time series in the same period and get an extremely high accuracy rate, which reflects that cryptocurrency investors behave differently in trading from stock investors. We then extract features from these price time series to explain the price pattern difference, including mean, variance, maximum, minimum, kurtosis, skewness, and first to third-order autocorrelation, etc., and then use machine learning methods including logistic regression (LR), random forest (RF), support vector machine (SVM), etc. for classification. The classification results show that these extracted features can help to explain the price time series pattern difference between cryptocurrencies and stocks.
Asynchronous Byzantine Fault Tolerant (BFT) consensus protocols have garnered significant attention with the rise of blockchain technology. A typical asynchronous protocol is designed by executing sequential instances of the Asynchronous Common Sub-seQuence (ACSQ). The ACSQ protocol consists of two primary components: the Asynchronous Common Subset (ACS) protocol and a block sorting mechanism, with the ACS protocol comprising two stages: broadcast and agreement. However, current protocols encounter three critical issues: high latency arising from the execution of the agreement stage, latency instability due to the integral-sorting mechanism, and reduced throughput caused by block discarding. To address these issues,we propose Falcon, an asynchronous BFT protocol that achieves low latency and enhanced throughput. Falcon introduces a novel broadcast protocol, Graded Broadcast (GBC), which enables a block to be included in the ACS set directly, bypassing the agreement stage and thereby reducing latency. To ensure safety, Falcon incorporates a new binary agreement protocol called Asymmetrical Asynchronous Binary Agreement (AABA), designed to complement GBC. Additionally, Falcon employs a partial-sorting mechanism, allowing continuous rather than simultaneous block committing, enhancing latency stability. Finally, we incorporate an agreement trigger that, before its activation, enables nodes to wait for more blocks to be delivered and committed, thereby boosting throughput. We conduct a series of experiments to evaluate Falcon, demonstrating its superior performance.
Erwan Mahe, Rouwaida Abdallah, Pierre-Yves Piriou, Sara Tucci-Piergiovanni
This paper presents an adversary model and a simulation framework specifically tailored for analyzing attacks on distributed systems composed of multiple distributed protocols, with a focus on assessing the security of blockchain networks. Our model classifies and constrains adversarial actions based on the assumptions of the target protocols, defined by failure models, communication models, and the fault tolerance thresholds of Byzantine Fault Tolerant (BFT) protocols. The goal is to study not only the intended effects of adversarial strategies but also their unintended side effects on critical system properties. We apply this framework to analyze fairness properties in a Hyperledger Fabric (HF) blockchain network. Our focus is on novel fairness attacks that involve coordinated adversarial actions across various HF services. Simulations show that even a constrained adversary can violate fairness with respect to specific clients (client fairness) and impact related guarantees (order fairness), which relate the reception order of transactions to their final order in the blockchain. This paper significantly extends our previous work by introducing and evaluating a mitigation mechanism specifically designed to counter transaction reordering attacks. We implement and integrate this defense into our simulation environment, demonstrating its effectiveness under diverse conditions.
Rollup stands out as one of the most effective techniques for blockchain Layer-2 scaling. By processing transactions off-chain, it significantly enhances the throughput. However, the most rollup implementations currently rely on centralized sequencers, exposing the system and users to censorship attacks and risking network paralysis. In contrast, fully decentralized sequencers encounter latency issues and reduced throughput during the consensus phase. We propose a multislot weighted leader election algorithm based on shared sequencers, apply the proposer–builder separation (PBS) model, and use the fuzzy cognitive map (FCM) to analyze and optimize the important influence parameters. With its low trust dependence and high functionality, the probability of selecting malicious nodes is reduced. The sequencing and consensus are separated, so that the transaction can quickly reach soft confirmation. We implement this algorithm in a shared sequencer prototype. The experimental results show that the proposed algorithm parameter settings are in line with the expectations, and the probability of electing malicious nodes is significantly reduced. The transactions per second (TPS) of the network can cope with the throughput requirements of the Layer-2.
The DC-Microgrids (DC-MGs) are increasingly prone to various cyber-attacks due to the advancement of intelligent controlling, monitoring, operation methods. A typical DC-MGs integrates components like batteries, super capacitors, electronic devices, Photovoltaic (PV) systems, and loads. Given these vulnerabilities, cyber-attack detection, and the security of data exchanged in smart DC-MGs, similar to Cyber-Physical Systems (CPS), have become critical areas to focus. This paper proposes a novel approach to detect false data injection attack (FDIAs) in DC-MGs using Wavelet transform and Support Vector Machines (SVMs) with Blockchain technology. The analysis shows that the output voltage dropped from 350 V to 300 V during the False Data Injection Attack (FDIA) at 0.4 s and returned to 350 V by 0.7 s. Significant oscillations observed between 0.4 and 0.7 s and detection model achieved 400 true negatives, 191 true positives, 10 false negatives, and no false positives, demonstrating high accuracy in identifying FDIA instances.
This paper introduces a novel multi-objective optimization framework for the portfolio rebalancing problem, incorporating return, risk, and liquidity as the central financial objectives. Unlike static models, our approach captures market dynamics by allowing periodic reallocation of assets and explicitly modeling transaction costs. To address uncertainty in key financial parameters such as expected returns, volatility, and asset liquidity, we employ interval arithmetic, offering a flexible representation without requiring distributional assumptions. The framework models risk using semi-absolute deviation, which better reflects downside exposure compared to traditional variance. A distinctive feature of the model is the integration of nonlinear transaction costs, ensuring higher realism in trading scenarios. The optimization problem is formulated with interval coefficients and solved under multiple decision-making strategies: pessimistic, optimistic, and mixed (via convex combination). To validate the model, we conduct a case study on a cryptocurrency portfolio consisting of Bitcoin, Ethereum, Solana, and Binance Coin, covering the period January–March 2025. The numerical simulations demonstrate the adaptability of the proposed methodology under different investor attitudes and market conditions. Our findings show that the interval-based, multi-objective framework provides robust, diversified portfolio allocations and valuable strategic insights for decision-makers operating under uncertainty.
Abstract: This paper describes a project-specific electronic voting (e-voting) system that integrates blockchain technology with face recognition for robust voter authentication. The goal is to design a decentralized platform in which every vote is recorded immutably on an Ethereum-based blockchain, while face recognition ensures that only a uniquely verified individual can cast a ballot. We detail the system architecture, methodology, and implementation steps, and we compare our approach to other blockchain-based e-voting systems worldwide, including Voatz, Follow My Vote, Zug e-Voting, and Moscow Blockchain Voting. Finally, we reference the open-source repository on which our project is based, demonstrating its real-world applicability and transparency.
This paper explores temporal coordination mechanisms in market economies through the lens of Austrian Capital Theory, emphasizing how interest rates facilitate the alignment of complex intertemporal production plans across dispersed market participants. The study addresses the challenge of coordinating heterogeneous capital goods over time, a critical issue in dynamic economic systems where production spans multiple stages and horizons. Through a rigorous theoretical analysis and an extensive literature review, the research investigates the role of market processes in achieving this coordination, with a particular focus on how monetary policy influences these mechanisms. The analysis reveals that interest rates act as vital signals, aggregating dispersed knowledge and guiding entrepreneurial decisions to align production structures with consumers’ time-preferences. However, monetary interventions, such as interest rate manipulations, are shown to distort these signals systematically, contributing to malinvestment—where resources are misallocated to unsustainable projects—and overconsumption during business cycles. Empirical evidence from the 2002–2009 period, including the U.S. Federal Reserve’s monetary expansion, illustrates these effects, highlighting how negative real interest rates (2003–2005) falsified economic calculations, inflating household net worth by $21.7 trillion while reducing savings rates to below 1% by 2005, only to collapse by $13 trillion in 2008. This research synthesizes Austrian insights with emerging technological developments, particularly Web 3.0 technologies and decentralized systems like smart contracts and decentralized finance (DeFi), which may enhance market coordination by reducing reliance on central intermediaries and improving knowledge transmission. The originality lies in bridging classical economic theory with modern technological paradigms, offering a framework to assess how decentralized innovations can preserve Austrian principles of entrepreneurial discovery and spontaneous order. This theoretical analysis contributes to understanding the interplay between monetary policy, technology, and market dynamics, providing a foundation for future empirical studies on decentralized economic coordination.
The predictive capability of traditional bearing remaining useful life (RUL) prediction models is insufficient, and the prediction networks lack universality, leading to unsatisfactory results in predicting the RUL of bearings, which leads to untimely maintenance decisions and significant economic losses. In order to solve this problem, this study employs Discrete Wavelet Transform (DWT) to denoise vibration signals and extract multi-domain features; the weighted averages of monotonicity, predictability, trendability, and robustness indicators are first ranked for selecting sensitive feature subsets as inputs for RUL prediction, feature fusion is conducted using the Kernel Principal Component Analysis (KPCA) method to obtain the health index (HI) of the bearing, and the failure threshold of the signal is determined based on the 3-sigma principle. An RUL prediction model, which combines Double Bidirectional Long Short-Term Memory (DBiLSTM) with attention mechanism (A-DBiLSTM), is then developed, and the Bayesian approach is used to adaptively search for network hyperparameters. Experiments were conducted using the PHM2012 dataset and the XJTU-SY dataset; the results indicate that the proposed RUL prediction model demonstrates higher predictive performance, exhibits satisfactory performance across different datasets, and possesses good generalization capability and applicability. This method further enhances the predictive capability of bearing RUL estimation.
The fast digital transformation of healthcare systems has brought electronic health records (EHRs) into wide usage to enhance patient care and provide better data access. The need for better security grows more pungent as cybersecurity and quantum computing threats against traditional cryptographic approaches become more prevalent. This paper develops a Blockchain-Enabled Post-Quantum Cryptographic framework for protecting EHRs. The combination of blockchain technology with PQC safeguards healthcare data through decentralised distribution, unalterable data storage, and complete system transparency, and PQC prevents anticipated quantum computing vulnerabilities. Security and privacy improve in the proposed framework by combining lattice-based cryptography, hash-based signatures, and zero-knowledge proofs. Smart contracts enable the framework to enforce access policies and maintain regulatory compliance through its functionality. A performance analysis of this framework shows it can effectively secure EHRs through efficient and scalable implementation. The research demonstrates that PQC and blockchain offer healthcare organisations a secure protection solution for EHRs that fights evolving cyber threats within trustworthy healthcare systems.
With the booming development of blockchain technology, smart contracts have been widely used in finance, supply chain, Internet of things and other fields in recent years. However, the security problems of smart contracts become increasingly prominent. Security events caused by smart contracts occur frequently, and the existence of malicious codes may lead to the loss of user assets and system crash. In this paper, a simple study is carried out on malicious code detection of intelligent contracts based on machine learning. The main research work and achievements are as follows: Feature extraction and vectorization of smart contract are the first step to detect malicious code of smart contract by using machine learning method, and feature processing has an important impact on detection results. In this paper, an opcode vectorization method based on smart contract text is adopted. Based on considering the structural characteristics of contract opcodes, the opcodes are classified and simplified. Then, N-Gram (N=2) algorithm and TF-IDF algorithm are used to convert the simplified opcodes into vectors, and then put into the machine learning model for training. In contrast, N-Gram algorithm and TF-IDF algorithm are directly used to quantify opcodes and put into the machine learning model training. Judging which feature extraction method is better according to the training results. Finally, the classifier chain is applied to the intelligent contract malicious code detection.
This study explores the application of Quadratic Voting (QV) and its generalization to improve decentralization and effectiveness in blockchain governance systems. The conducted research identified three main types of quadratic (square root) voting. Two of them pertain to voting with a split stake, and one involves voting without splitting. In split stakes, Type 1 QV applies the square root to the total stake before distributing it among preferences, while Type 2 QV distributes the stake first and then applies the square root. In unsplit stakes (Type 3 QV), the square root of the total stake is allocated entirely to each preference. The presented formal proofs confirm that Types 2 and 3 QV, along with generalized models, enhance decentralization as measured by the Gini and Nakamoto coefficients. A pivotal discovery is the existence of a threshold stakeholder whose relative voting ratio increases under QV compared to linear voting, while smaller stakeholders also gain influence. The generalized QV model allows flexible adjustment of this threshold, enabling tailored decentralization levels. Maintaining fairness, QV ensures that stakeholders with higher stakes retain a proportionally greater voting ratio while redistributing influence to prevent excessive concentration. It is shown that to preserve fairness and robustness, QV must be implemented alongside privacy-preserving cryptographic voting protocols, as voters casting their ballots last could otherwise manipulate outcomes. The generalized QV model, proposed in this paper, enables algorithmic parametrization to achieve desired levels of decentralization for specific use cases. This flexibility makes it applicable across diverse domains, including user interaction with cryptocurrency platforms, facilitating community events and educational initiatives, and supporting charitable activities through decentralized decision-making.
Summary This paper documents the earliest form of the LEJ UNION DAO concept as it was originally conceived in 2025. While branding, organizational culture, entrepreneurship, artificial intelligence, and decentralized autonomous organizations (DAOs) have often been discussed as separate domains, this work attempts to connect them into a single conceptual framework: Founding Philosophy → Brand DNA → Organization → AI → Contribution → Value Distribution → Ecosystem Within this framework, Brand DNA is approached not only as a branding concept but also as an operating system for organizational judgment and execution. This perspective inspired an ongoing effort to realize that vision, during which Brand DNA Architecture evolved into the Brand DNA Engine, while Thought Pattern Network (TPN), Thought Structuring Architecture (TSA), and Brand DNA OS gradually emerged. Author's Note This document is the original Version 1.0 of the LEJ UNION DAO White Paper, completed on April 17, 2025. After completing this manuscript, I chose not to publish it immediately. Instead, I left it on my desktop for more than a year while continuing to explore its implications. During that period, I continued refining the underlying ideas instead of publishing the manuscript. The concepts gradually matured into later frameworks. Brand DNA Architecture evolved into the Brand DNA Engine, while Thought Pattern Network (TPN), Thought Structuring Architecture (TSA), and Brand DNA OS gradually emerged. This edition preserves the original manuscript as it was completed in 2025. Except for minor editorial corrections (such as typographical errors and obvious translation mistakes), no conceptual changes have been made. Although the project has naturally progressed to a stage where a Version 2.0 is needed, I believe it is more meaningful to preserve the original record than to rewrite it from today's perspective. This document marks the starting point of the research. Future publications will document how these ideas evolved into a more complete theoretical and practical framework. Research is conducted through LEJLAB, while LEJ UNION serves as the real-world implementation environment for testing these ideas. The LEJ UNION Workspace has been established as an open implementation site, where the LEJ UNION brand itself serves as Case 0 to explore whether the proposed framework—from Founding Philosophy to Brand DNA, Organization, AI, Contribution, Value Distribution, and Ecosystem—can be implemented and observed in practice. Workspace: https://sites.google.com/lejunion.com/lej-union-workspace/welcome
A. Althaf Ali, M. A. Gunavathie, V. Srinivasan, M. Aruna · 6 authors
The integration of smart city applications with healthcare has revolutionized patient monitoring and medical data management. However, ensuring the privacy and security of Electronic Health Records (EHR) remains a critical challenge, especially in IoT-based environments with resource-constrained devices. This paper proposes a novel Blockchain-Enabled Federated Learning (BFL) framework to enhance privacy preservation in EHR processing. The proposed framework leverages zero-knowledge proofs (ZKP) for authentication and homomorphic encryption for secure computation, ensuring robust data security without exposing raw patient data. Federated Learning (FL) enables decentralized model training across IoT devices, reducing privacy risks while maintaining data utility. Additionally, blockchain technology enhances the integrity and transparency of EHR transactions by creating a tamper-proof ledger. The performance of the proposed BFL framework is evaluated based on data utility, model accuracy, execution time, and scalability across varying sizes of EHR datasets. Results demonstrate improved privacy preservation, reduced computational overhead, and enhanced model efficiency, making it a promising approach for secure and privacy-aware IoT-based smart healthcare systems.
• Blockchain enhances security, transparency, and efficiency through tamper-proofed records. • It transforms industries like transportation, manufacturing, food, and healthcare by making them more efficient and accountable. • Blockchain improves traceability and provenance throughout complex supply chains. • It addresses problems like counterfeiting, managing suppliers, and sustainable sourcing. • Smart contracts and IoT integration bring more supply chain functionality to blockchain. Blockchain technology is emerging as one of the most transformative forces to have ever reshaped supply chain management, with unique transparency, security, and efficiency. This paper reviews the literature on blockchain technology and applications in core industries such as transportation, manufacturing, food and beverages, and healthcare. At its core, blockchain deploys distributed ledger technology to provide tamper-proof, secure record-keeping, enhancing traceability and provenance verification for complex supply chains. Smart contracts, IoT connectivity, and decentralized financial services allow blockchain to address its most critical challenges, including counterfeiting, supplier management, and the enforcement of sustainable and responsible sourcing practices. However, widespread adoption of blockchain in supply chains is inhibited by severe issues like scalability, interoperability, regulatory uncertainty, and lack of standardization. Additionally, the environmental impact of blockchain, i.e., the energy-intensive proof-of-work mechanisms is examined, along with potential strategies for mitigation. As the technology continues to evolve, the integration of artificial intelligence and 5G networks will further reshape supply chain management, unleashing new efficiencies and capabilities.
This research presents a novel framework and experimental results that combine zero-knowledge proofs (ZKPs) with private blockchain technology to safeguard whistleblower privacy while ensuring secure digital evidence submission and verification. For example, whistleblowers involved in corporate fraud cases can submit sensitive financial records anonymously while maintaining the credibility of the evidence. The proposed framework introduces several key innovations, including a private blockchain implementation utilising proof-of-work (PoW) consensus to ensure immutable storage and thorough scrutiny of submitted evidence, with mining difficulty dynamically aligned to the sensitivity of the data. It also features an adaptive difficulty mechanism that automatically adjusts computational requirements based on the sensitivity of the evidence, providing tailored protection levels. In addition, a unique two-phase validation process is incorporated, which generates a digital signature from the evidence alongside random challenges, significantly improving security and authenticity. The integration of ZKPs enables iterative hash-based verification between parties (Prover and Verifier) while maintaining the complete privacy of the source data. This research investigates the whistleblower’s niche in traditional digital evidence management systems (DEMSs), prioritising privacy without compromising evidence integrity. Experimental results demonstrate the framework’s effectiveness in preserving anonymity while assuring the authenticity of the evidence, making it useful for judicial systems and organisations handling sensitive disclosures. This paper signifies notable progress in secure whistleblowing systems, offering a way to juggle transparency with informant confidentiality.
The Ethereum blockchain, plagued by network congestion and exorbitant transaction fees, faces significant scalability challenges. While Layer 2 solutions offer a promising avenue to address these concerns, their potential remains largely unexplored on blockchain applications. The research proposes a novel Layer 2 architecture specifically designed for the academic certificate system on the Ethereum network. The method commences with a comprehensive survey of existing literature, followed by an analysis of solutions within the business domain. Subsequently, the most suitable and comprehensive solutions are identified for integration into the proposed academic certificate system architecture. In the selection process, the research analyzes 20 studies to determine the frequency of solutions employed in each investigation. The results indicate the InterPlanetary File System (IPFS) exhibiting the highest frequency, while Oracle, Decentralized Identifiers (DIDs), and Application Programming Interfaces (APIs) have comparable frequencies. Furthermore, an analysis of rankings from 10 websites evaluating Layer 2 Ethereum solutions and their performance across various aspects reveals Arbitrum as the top-ranked solution, followed by Polygon and Optimism, respectively. The research demonstrates the implementation of this system architecture within the proposed system's process. The culmination of this effort is a valuable blueprint for developers seeking to build and deploy similar systems efficiently. Notably, the inherent adaptability of the architecture extends beyond the educational domain, paving the way for its application across diverse contexts. The system architecture presented constitutes an initial exploration into developing Decentralized Applications (DApps) on the Ethereum Layer 2 network because prior research has not specifically focused on its application.