Search Results (1,395)

This paper aims to present proposals for improving public lending design in an economic crisis. It combines casual empirical observations, institutional analysis and normative theoretical modeling. We obtain casual evidence from the analysis of the emergency lending scheme offered by Germany’s national development bank (NDB) KfW during the COVID-19 crisis. We identify obstacles to efficient contracting in these two-tier lending relationships, involving the NDB, the participating commercial banks, and the ultimate firm borrowers. Theoretical arguments and empirical evidence based on this case study help to understand major incentive risks of subsidized public lending schemes. To counter these risks, we propose a smart set of public lending contracts which induces banks to refrain from applying for public support for financially strong firms and for non-viable zombie firms. For firms which need financial support, we propose a set of public contracts from which the firm chooses the contract which maximizes its subsidy, reveals its rating, and obtains public funds according to its crisis-induced needs. This partially revealing signaling equilibrium implies higher interest rates for firms with a need for more public funding, thereby mitigating information asymmetries. In order to ensure incentive alignment, banks should retain a share of borrower default risk. Full article

This paper examines the relationship between traditional gold (XAU) and its tokenized counterparts (PAXG and XAUT), providing an empirical assessment of how digital representations of real-world assets align with their underlying benchmarks. Using multi-year time series data, the study evaluates price deviations, tracking accuracy, correlations, and volatility across both weekday-only and 24/7 trading datasets, incorporating weekend effects and crypto-market microstructure. Results show that both tokenized assets exhibit strong long-term alignment with XAU, while short-term divergences arise from continuous crypto trading, liquidity fragmentation, and issuer-specific design features, with XAUT consistently tracking spot gold more closely than PAXG. Building on this analysis, the paper examines the role of tokenized gold within dynamic, smart contract-driven crypto portfolios that also include BTC, ETH, and cash. Portfolio simulations demonstrate that adaptive rebalancing strategies materially improve risk-adjusted performance, with XAUT serving as a stabilizing anchor and cash enabling rapid, automated repositioning during volatility spikes. The findings offer a dual contribution: they clarify the fidelity and market behavior of tokenized gold and provide evidence of its practical utility within automated, on-chain portfolio management, highlighting both its strengths and structural limitations in emerging digital financial systems. Full article

Blockchain technology is redefining the foundations of cybersecurity by introducing decentralized, tamper-resistant mechanisms for data integrity, trust management, and malware intelligence sharing. Traditional detection systems, which are dependent on centralized control and opaque validation, remain vulnerable to data manipulation and systemic compromise. The integration of blockchain transforms these paradigms because it provides verifiable provenance, distributed consensus, and autonomous enforcement through smart contracts. This review synthesizes fifteen years of progress (2010–2025) at the intersection of blockchain and malware detection and discusses core architectures, consensus protocols, and cryptographic properties that underpin decentralized defenses. The review follows a structured literature review methodology, which focuses on blockchain architectures, consensus protocols, and malware-detection pipelines reported in the cybersecurity literature. It also analyzes blockchain detection pipelines, performance tradeoffs, and data protection mechanisms in distributed learning systems and artificial intelligence models. Special attention is given to scalability constraints, regulatory compliance, and interoperability challenges that shape adoption. The review identifies three dominant design patterns: (i) decentralized threat-intelligence sharing with provenance guarantees, (ii) consensus-driven validation of malware artifacts, and (iii) on-chain trust and reputation mechanisms for detector accountability. Through the union of blockchain, artificial intelligence, edge computation, and federated learning, cybersecurity attains an auditable and adaptive architecture resilient to adversarial threats. The study concludes that blockchain provides a verifiable trust infrastructure for malware detection, but its practical deployment requires faster transaction validation and stronger protection of sensitive data; future research should address performance optimization and regulatory compliance. Full article

Background: Blockchain technology has emerged as a transformative communication solution for securing distributed systems. However, several vulnerabilities exist during transactions, including latency and network congestion issues during mempool processing, topology weaknesses, cross-chain bridge exploits, and cryptographic weaknesses. These vulnerabilities have led to attacks that have threatened system integrity, including Block Extractable Value (BEV) attacks, Maximal Extractable Value (MEV) attacks, sandwich attacks, liquidation, and Decentralized Finance (DeFi) reordering attacks, among others. Thus, implementing a robust security framework based on the Confidentiality, Integrity, and Availability (CIA) triad remains critical for addressing modern blockchain technology threats. Objective: This paper examines blockchain technology, its various vulnerabilities, and attacks to determine how criminals exploit the system during transactions. Further, it evaluates its impact on users. Then, implement a blockchain attack in a “MasterChain” virtual environment to demonstrate how vulnerable spots can be practically exploited and discuss the application of the CIA security triad through modern cryptographic primitives. Methods: The approach considers Hevner’s design science framework, which emphasizes creating innovative artifacts that address identified problems while contributing to the knowledge base through rigorous evaluation. Furthermore, we developed a MasterChain tool using Python with Flask for distributed node communication, utilizing the Elliptic Curve Digital Signature Algorithm (ECDSA) with the Standards for Efficient Cryptography Prime 256-bit Koblitz curve 1 (secp256k1) for digital signatures and Secure Hash Algorithm 3 (SHA-3) (Keccak-256) hashing for block integrity. Results: show how the CIA has been implemented to provide secure communication through ECDSA-based transactions, SHA-3 chain integrity verification, and a multi-node distributed architecture, respectively. The performance analysis shows that ECDSA provides 256-bit security with 64-byte signatures compared to 2048-bit Rivest–Shamir–Adleman (RSA)’s 256-byte signatures, achieving a 75% reduction in bandwidth overhead. SHA-3 provides immunity to length extension attacks while maintaining equivalent collision resistance to SHA-256. Conclusions: The MasterChain framework provides a practical foundation for implementing blockchain security that addresses both classical and emerging vulnerabilities. The adoption of ECDSA and SHA-3 (Keccak-256) positions the system favourably for modern blockchain applications, while providing insights into the cryptographic trade-offs between performance, security, and compatibility. Full article

This paper explores the role of blockchain technology in reducing information asymmetry and improving trust in the context of circular construction. In the construction industry, especially when applying circular economy principles, many stakeholders suffer from lack of transparency and poor data sharing. This often causes mistrust and inefficient collaboration. Blockchain, as a decentralized and secure technology, can solve this problem by creating transparent, traceable, and immutable records of material flows, contracts, and processes. However, there is a lack of scientific evidence in understanding the blockchain’s role in building trust and reducing information asymmetry in circular construction. This study reviews recent literature where the blockchain is applied to circular construction, finds the current gaps, and proposes future directions for research on this topic. It identifies critical technical, organizational, and legal barriers while outlining a clear pathway for overcoming them. By addressing these challenges, the blockchain can evolve into a central enabler of circular, sustainable, and trustworthy construction, offering a decisive reference point for guiding adoption, standardization, and industry-wide implementation. Full article

Electronic Medical Records (EMRs) are mandatory in Indonesia following the Ministry of Health regulation, which raises significant challenges in data security and patient-centric access control. Current implementations rely on centralized healthcare systems or third-party vendors, creating risks of unauthorized access, data leakage, and uncertain data integrity. To address these issues, this study proposes DecMed, a decentralized EMR management framework built on IOTA Distributed Ledger Technology (DLT). DecMed integrates Capability-Based Access Control (CapBAC), Proxy Re-Encryption (PRE), and the InterPlanetary File System (IPFS) to enforce patient ownership of medical data. Patients actively grant or revoke access, define access duration, and selectively share data with healthcare personnel. The system is implemented using smart contracts in the Move programming language on the IOTA ledger, while encrypted clinical data is stored on IPFS. Evaluation through unit testing of various unauthorized access scenarios demonstrates that DecMed effectively enforces fine-grained access rules, preserves data confidentiality and integrity, and ensures compliance with national healthcare requirements. Full article

Static analysis is a critical methodology for ensuring the quality, security, and safety of embedded, cyber-physical, and electronic software systems, particularly as such systems become increasingly complex and tightly coupled with hardware and real-time constraints. Through a systematic study of the literature, this paper summarizes the State-of-the-Art in static program analysis. We develop a comprehensive taxonomy of fundamental techniques, including model checking, abstract interpretation, data-flow analysis, and symbolic execution, and examine their application in modern analysis tools used in electronic and safety-critical systems. The survey thoroughly reviews applications across key domains, including vulnerability detection, automotive and embedded software verification, smart contract auditing, and AI-enabled electronic systems. We also critically analyze persistent challenges, including tool integration, scalability limitations, and the trade-off between analysis precision and soundness. Finally, by discussing emerging trends and future research directions—such as machine-learning-enhanced analysis and hybrid static–dynamic techniques—this work provides a structured framework to guide future research and industrial practice in the development of reliable electronic systems. Full article

The adoption of blockchain-based smart contracts for the trading of goods and services promises greater transparency, automation, and trustlessness, but also raises challenges related to payment integration and modularity. While business analysts (BAs) can express business logic and control flow using BPMN and decision rules using DMN, payment tasks that involve concrete transfers (on-chain, off-chain, cross-chain, or hybrid) require careful implementation by developers due to platform-specific constraints and semantic richness. To address this separation of concerns, we introduce a methodology within the context of the smart contract-as-a-service (SCaaS) approach that supports (1) identifying and mapping generic payment tasks in BPMN to pre-deployed payment smart contracts, (2) augmenting BPMN models with matching payment fragments from a pattern repository, and (3) automatically transforming the augmented models into smart contracts that invoke the appropriate payment services. Our approach builds on prior work in automated BPMN-to-smart contract transformation using Discrete Event–Hierarchical State Machine (DE-HSM) multi-modal modeling to capture process semantics and nested transactions, while enabling payment service reuse, extensibility, and the separation of concerns. We illustrate this methodology via representative use cases spanning conventional, DeFi, and cross-chain payments, and discuss the implications for modular contract deployment and maintainability. Full article

The rapid expansion of the Internet of Things (IoT) has led to billions of interconnected devices generating and exchanging sensitive data across diverse domains, which introduces challenges in identity management (IdM) regarding privacy, scalability, and verifiability. While blockchain technology provides decentralization and tamper resistance, its transparency and increasing on-chain storage demands make it unsuitable for large-scale IoT identity ecosystems. To overcome these challenges, IoT-SBIdM is proposed as a lightweight, privacy-preserving, and stateless blockchain-based identity management framework designed for IoT environments. This framework incorporates Elliptic Curve Cryptography (ECC)-based accumulators and Zero-Knowledge Proofs (ZKPs) to facilitate selective disclosure, enabling entities to prove credential authenticity without exposing sensitive identity information. Furthermore, the framework adopts W3C-compliant Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs) to promote interoperability and user-controlled identity ownership. The experimental results indicate that IoT-SBIdM achieves efficient smart contract execution by reducing gas costs through optimized registry logic. Moreover, the system maintains a compact block size of only 45 MB at higher block heights, outperforming comparable schemes in storage efficiency by achieving a 55% reduction relative to recent models and an approximate 94% reduction relative to older systems, thereby demonstrating superior scalability and storage efficiency, making it suitable for identity management solutions for IoT environments. Full article

Design of large-scale power systems is getting increasingly complex nowadays from an operational and reliability standpoint due to the uncertainties associated with the injection of renewables and consumption of load. These uncertainties pose a great challenge in gauging and subsequently obtaining reliable system-level assurances from subsystem-level guarantees, particularly in mission-critical systems such as those seen in data centers. We propose a formal and modular framework of probabilistic assume–guarantee contracts (PAGCs) for compositional reasoning and control of uncertain power systems, motivated by the need for resilient and verifiable operation in data center power networks. In contrast to classical contracts, which require absolute satisfaction of assumptions and guarantees, PAGCs allow for high-probability satisfaction under system uncertainty and variability. We formalize the syntax and semantics of PAGCs, develop soundness and compositionality theorems, and demonstrate their applicability to power grid components such as generators, transformers, circuit breakers, and loads. Given the current approval bottlenecks in interconnection requests, a growing number of data center operators are opting for islanded generation configuration. A case study on such a modular islanded data center power system is presented to validate the proposed theory. The proposed PAGC application in power networks is promising in several aspects to solve several existing open problems in distributed systems, particularly in future large-scale smart power networks. Full article

The dominance of centralized artificial intelligence architectures raises significant concerns regarding privacy, data ownership, and control. These limitations have motivated the development of decentralized learning paradigms that aim to remove reliance on a central authority during model training. While federated learning represents an intermediate step by allowing distributed training without raw data exchange, it still depends on a centralized server which could lead to single-point vulnerabilities. Beyond this, a fully decentralized learning in general faces challenges in security vulnerabilities, absence of governance, and lack of incentive alignment. Recent advances in blockchain technology offer a promising foundation for addressing these issues. This paper provides a systematic analysis of blockchain’s mechanism-level roles in security, consensus, smart contract, and incentives to support decentralized learning. By reviewing state-of-the-art approaches, this paper suggests that appropriately designed blockchain architectures have the potential to enable practical, secure, and incentive-compatible decentralized learning as technological capabilities continue to evolve. Full article

With the rapid development of the Internet of Vehicles (IoV), the explosive growth of data traffic within the system has led to a surge in demand for spectrum resources. However, the strict limitations on spectrum supply make the construction of an efficient and reasonable resource allocation scheme crucial for IoV. To maximize social benefits and improve security in the resource allocation process under IoV spectrum scarcity, this paper proposes a dynamic spectrum allocation (DSA) scheme based on a consortium blockchain framework. In this scheme, we design a demand-based vehicle priority classification method and propose a novel hybrid consensus mechanism—PhDPoR—which integrates practical byzantine fault tolerance (PBFT) and Hierarchical Delegated Proof of Reputation. Furthermore, we construct a multi-leader, multi-follower (MLMF) Stackelberg game model and utilize smart contracts to implement an immutable on-chain record of spectrum resource allocation, thereby deriving the optimal spectrum pricing and purchase strategy. Experimental results show that the proposed scheme not only effectively optimizes the utility of both supply and demand sides and improves overall social benefits while ensuring efficiency, but also significantly outperforms baseline algorithms in identifying and mitigating malicious nodes, thus verifying its feasibility and application value in complex IoV environments. Full article

Blockchain technology, with special features of decentralization, immutability, consensus mechanisms, and smart contracts, has been integrated into different areas of digital applications recently. However, its abstract concepts present a steep learning curve for beginners, especially in the absence of online resources that offer dynamic, hands-on learning experiences. In response to this problem, we developed a digital interactive teaching tool using the OwlSpace platform to explain what blockchain truly is in its four core foundational concepts. Interactive operations, guided workflows, and visual simulations are applied in the system to assist the learner in interpreting decentralized architectures, immutability of data interactively, the consensus formation process, and the mechanics behind smart contract operation. The system has also put a focus on conceptual understanding and gamified experiences rather than competitive ones, providing a practical and engineering-focused tool for introductory information engineering students. Full article

Against the deep integration of digital transformation and AI, cross-institutional collaborative modeling hinges on efficient data circulation, yet data silos and privacy regulations hinder traditional centralized training. Federated Learning (FL) keeps data local but faces issues like weak centralized trust, inadequate privacy protection, and poor robustness in edge networks. Existing improvements, including via differential privacy (DP) and blockchain, among others, still suffer from centralized budget allocation, low consensus efficiency, or single-point-of-failure addressing, failing to jointly optimize trust, performance, and privacy. The limitations are exacerbated in high-frequency, resource-constrained edge environments. To tackle these challenges, this paper proposes BE-DPFL, a blockchain-enhanced differentially private FL framework that integrates on-chain trusted supervision and off-chain efficient training. It builds a lightweight blockchain trust layer with FL-PBFT consensus and smart contracts, introduces Random Projection–ADMM optimization, and designs a multi-objective adaptive gradient clipping/noise injection strategy. Experiments on CIFAR-10 and ChestX-ray14 demonstrate that BE-DPFL outperforms mainstream methods in consensus efficiency, communication overhead, privacy-accuracy balance, and robustness. It reduces communication costs by over 97%, achieves 100% privacy compliance, and maintains stable performance even under high disturbances. Ablation studies confirm the significant contributions of core components. Full article

Blockchain technology, with its characteristics of decentralization, immutability, auditability, and traceability, has gradually become a core infrastructure in the digital economy era, demonstrating great potential in fields such as finance, government services, and the Internet of Things (IoT). However, as the scale of blockchain networks expands and data volumes surge, issues such as full-node storage redundancy, limited transaction throughput, and inefficient synchronization of historical data have become increasingly prominent, severely restricting the large-scale application of blockchain systems. The storage scalability problem faced by blockchain is therefore becoming more critical. To address the challenge in which on-chain storage expansion still cannot meet the demand for large-scale data storage, a storage method combining the InterPlanetary File System (IPFS) with blockchain, referred to as IPFS-BC, is proposed. In IPFS-BC, large-scale raw data are stored in the decentralized and content-addressable IPFS network, while the blockchain only retains the unique content identifier (CID) hash and related metadata. Through smart contracts enabling dynamic permission management and fine-grained access control, efficient interaction and collaborative storage between on-chain and off-chain systems are achieved. In this work, file upload simulation experiments were conducted, and two evaluation indicators—storage space consumption and storage performance (file read/write time and speed)—were used to compare three storage approaches: Distributed Hash Table (DHT)-based off-chain storage, Financial Blockchain Shenzhen Open Source (FISCO BCOS) on-chain storage, and the IPFS-BC on-chain/off-chain collaborative storage model. Experimental results show that the IPFS-BC model reduces storage space consumption by approximately 75% compared with FISCO BCOS blockchain storage when storing file data, significantly decreasing data redundancy. Moreover, IPFS-BC ensures system security during the on-chain process, and through the automated management and auditing provided by smart contracts, it effectively enhances system security and realizes scalable on-chain/off-chain collaborative storage. Full article