Purpose This study aims to examine the spillover effects between cryptocurrencies, stablecoins and Islamic stock indices, focusing on their interactions during crises. Islamic stocks have historically been more resilient during crises due to their adherence to Shariah principles. Time-varying parameter vector autoregression (TVP –VAR) method will allow the study to look at the spillover over time. Design/methodology/approach Using the TVP–VAR model, the study analyzes daily return data from January 1, 2018 to September 30, 2024. The data includes Bitcoin (BTC), Ethereum (ETH), Tether (USDT) and large-, mid- and small-cap Islamic stock indices. Findings The findings reveal that cryptocurrencies such as BTC and ETH exert stronger spillover effects on Islamic stock indices compared to stablecoins like USDT). However, Islamic stocks remain largely insulated from crypto spillovers, with any spillovers predominantly flowing from stocks to cryptocurrencies. Large-cap Islamic stocks are more susceptible to cryptocurrency spillovers, whereas mid- and small-cap stocks tend to transmit more volatility to crypto assets. Crises amplify these effects, with COVID-19 causing a sharp but short-lived impact, while the 2022 crypto crash led to more prolonged and intense spillovers, particularly affecting large-cap stocks. Notably, stablecoins exhibited no measurable spillover effects on Islamic stocks during the 2022 crypto crash, reinforcing their role as stabilizers in volatile markets. This reinforces the role of stablecoins and Islamic stocks as a relatively stable investment during crypto-related crises. Practical implications The findings suggest that Islamic stock investors face lower exposure to cryptocurrency crisis volatility. Furthermore, stablecoins could be a valuable addition to Islamic investment portfolios. Originality/value This study expands the limited understanding of how stablecoins influence Islamic stock markets. It adds further depth by examining spillover effects across different market capitalizations.
Portfolio optimization is a cornerstone of modern financial decision-making, tradition-ally based on the mean–variance model introduced by Markowitz. However, this framework relies on restrictive assumptions—such as normally distributed returns and symmetric risk preferences—that often fail in real-world markets, particularly in volatile and non-Gaussian environments such as cryptocurrencies. To address these limitations, this paper proposes a novel multi-objective model that combines expected return max-imization, mean absolute deviation (MAD) minimization, and entropy-based diversifi-cation into a unified optimization structure: the Mean–Deviation–Entropy (MDE) model. The MAD metric offers a robust alternative to variance by capturing the average mag-nitude of deviations from the mean without inflating extreme values, while entropy serves as an information-theoretic proxy for portfolio diversification and uncertainty. Three entropy formulations are considered—Shannon entropy, Tsallis entropy, and cumulative residual Sharma–Taneja–Mittal entropy (CR-STME)—to explore different notions of uncertainty and structural diversity. The MDE model is formulated as a tri-objective optimization problem and solved via scalarization techniques, enabling flexible trade-offs between return, deviation, and en-tropy. The framework is empirically tested on a cryptocurrency portfolio composed of Bitcoin (BTC), Ethereum (ETH), Solana (SOL), and Binance Coin (BNB), using daily data over a 12-month period. The empirical setting reflects a high-volatility, high-skewness regime, ideal for testing entropy-driven diversification. Comparative outcomes reveal that entropy-integrated models yield more robust weightings, particularly when tail risk and regime shifts are present. Comparative results against classical mean–variance and mean–MAD models indicate that the MDE model achieves improved di-versification, enhanced allocation stability, and greater resilience to volatility clustering and tail risk. This study contributes to the literature on robust portfolio optimization by integrating entropy as a formal objective within a scalarized multi-criteria framework. The proposed approach offers promising applications in sustainable investing, algorithmic asset allo-cation, and decentralized finance, especially under high-uncertainty market conditions.
Mario Norberto Sevilio de Oliveira Junior, http://lattes.cnpq.br/0962672305508249
O presente trabalho realiza uma análise comparativa de desempenho de contratos inteligentes implementados em duas arquiteturas distintas de blockchain: a Ethereum Virtual Machine (via Binance Smart Chain) e a Bitcoin Virtual Machine (via Bitcoin Satoshi Vision). O estudo teve como objetivo avaliar o impacto das diferenças arquiteturais no custo de transações, no tempo de execução e na ocupação de rede, a fim de identificar vantagens e limitações práticas de cada plataforma. Para alcançar esse propósito, foram desenvolvidos dois contratos inteligentes de referência: um contrato de mensagem, representativo de operações simples de armazenamento, e um contrato de contador, concebido para simular cenários de maior complexidade lógica e de estresse computacional. Ambos foram implementados em linguagens especÃficas das plataformas (Solidity para BSC e sCrypt para BSV) e executados em ambientes de teste controlados. A metodologia incluiu a configuração de infraestrutura experimental, a realização de testes de desempenho, a medição de custos, bem como a execução de funções de ataque para avaliar a escalabilidade sob alta carga.
Muhammad Farooq Shaikh, S. Hamza Hassan, Alessia Maccaro, Giacomo Pratesi · 5 authors
The proposed model in this paper focuses on asset tracking and monitoring in the healthcare industry and it uses blockchain technology. Data security incidents in the healthcare field have created financial and ethical problems over the last few years. During 2024, the money lost from healthcare data breaches often exceeded $4.88 million due to the exposure of private patients and asset information. This shows why it is important to use secure systems to protect worthwhile information and manage key assets efficiently and correctly. Since such intrusions cannot be prevented by the current systems, businesses end up losing money and working less efficiently. These issues are addressed by using a system that includes blockchain, IoT and digital asset tracking technologies. To ensure data integrity and prevent fake information, Proof of Authority (PoA) uses chosen, recognized authorities to verify and confirm each transaction. To protect transactions, PoA requires a number of approvals from different parties which helps ensure that transactions are safe and secure. With immutable and decentralized features, blockchain makes the management of assets more secure and transparent. Records of asset transfers and data are safely stored on the blockchain with smart contracts, providing real-time monitoring and no room for errors. When integrated with IoT devices, the system can constantly check all the assets, improving the company's efficiency while reducing losses of items. The findings suggest that a PoA blockchain system can help healthcare asset management systems operate more ethically, safely, transparently, and efficiently.
Yating Liu, Xing Su, Hao Wu, Sijin Li · 7 authors
Adversarial smart contracts, mostly on EVM-compatible chains like Ethereum and BSC, are deployed as EVM bytecode to exploit vulnerable smart contracts for financial gain. Detecting such malicious contracts at the time of deployment is an important proactive strategy to prevent losses from victim contracts. It offers a better cost-benefit ratio than detecting vulnerabilities on diverse potential victims. However, existing works are not generic with limited detection types and effectiveness due to imbalanced samples, while the emerging LLM technologies, which show their potential in generalization, have two key problems impeding its application in this task: hard digestion of compiled-code inputs, especially those with task-specific logic, and hard assessment of LLM's certainty in its binary (yes-or-no) answers. Therefore, we propose a generic adversarial smart contracts detection framework FinDet, which leverages LLM with two enhancements addressing the above two problems. FinDet takes as input only the EVM bytecode contracts and identifies adversarial ones among them with high balanced accuracy. The first enhancement extracts concise semantic intentions and high-level behavioral logic from the low-level bytecode inputs, unleashing the LLM reasoning capability restricted by the task input. The second enhancement probes and measures the LLM uncertainty to its multi-round answering to the same query, improving the LLM answering robustness for binary classifications required by the task output. Our comprehensive evaluation shows that FinDet achieves a BAC of 0.9374 and a TPR of 0.9231, significantly outperforming existing baselines. It remains robust under challenging conditions including unseen attack patterns, low-data settings, and feature obfuscation. FinDet detects all 5 public and 20+ unreported adversarial contracts in a 10-day real-world test, confirmed manually.
In recent years, permissionless blockchains have emerged as the foundation for a wide range of decentralized applications, including cryptocurrency management, decentralized finance, auctions, voting systems, digital ownership, and more. The reliability of these applications depends on the security guarantees offered by the underlying blockchain protocols. Although prominent permissionless blockchains often base their security on formal analyses and claims, these assessments typically rely on simplifying assumptions, such as the existence of an honest majority, or overlook critical factors, like the influence of reward distribution on system security. Permissionless blockchains incentivize their participants, referred to as miners or validators, by rewarding them with cryptocurrency to encourage active participation in the protocol. The incentive mechanism plays a crucial role in maintaining the security of the blockchain by motivating participants to follow the desired, or honest, behavior. Since validators and miners are typically rational actors seeking to maximize profit, any flaw in the incentive design may motivate them to deviate from honest behavior in pursuit of an unfair advantage. Such exploitation of the incentive mechanism can lead to a destabilizing situation in which disadvantaged participants may choose either to deviate from honest behavior or to eventually abandon the network, with both possibilities threatening the blockchain's continued progress and stability. In this thesis, we analyze the incentive mechanisms behind several prominent permissionless blockchains, including Bitcoin, Cardano, and Ethereum. Our focus is primarily on potential fairness attacks that allow adversarial miners or validators to increase their profits by capturing an unfair share of blocks. As a first step, we analyze the incentive structure in Bitcoin, the pioneering Proof-of-Work (PoW) blockchain. Specifically, we examine the temporal aspects of profitability associated with one of the most well-known fairness attacks, namely selfish mining, which aims to exclude honest blocks from the longest chain. Building on this, we assess the role of Bitcoin's difficulty adjustment mechanism in mitigating such attacks. We then explore the implications of mining pool rationality for Bitcoin's security and propose novel attack vectors that could further increase an adversary's profit in a rational setting. Additionally, we investigate how upcoming Bitcoin reward halvings and the transition toward a transaction-fee era can impact Bitcoin's security by lowering the security threshold and increasing the potential for immediate profitability. As a next step, we analyze the incentive mechanisms of Proof-of-Stake (PoS) protocols. We examine longest-chain PoS protocols, such as Cardano, and show that due to the block proposer predictability inherent in PoS systems, the selfish mining attack can be even more destructive in this context. We also analyze Ethereum as a voting-based PoS protocol. These protocols are susceptible to block reorganization attacks, in which an adversarial validator attempts to exclude blocks with weak voting support from the canonical chain. We identify a vulnerability in Ethereum's reward mechanism that results from the centralized distribution of vote rewards. If exploited, this vulnerability can lead to both short- and long-range reorganization attacks. To address this issue, we propose a fairer and more decentralized reward mechanism for Ethereum. We hope the insights learned from this thesis will help identify potential incentive-based vulnerabilities in permissionless blockchains and contribute to the design of fairer blockchain protocols.
The proliferation of unmanned aerial vehicle (UAV) swarms in mission-critical applications for 6G and the Internet of Things (IoT) introduces significant security vulnerabilities stemming from their dynamic, distributed, and resource-constrained nature. Traditional security paradigms are often inadequate for these complex cyber-physical systems. This paper proposes a novel, cross-layer security framework that ensures robust and lightweight operation for UAV swarms. The framework is founded on a novel Entropy-Derived Physically Unclonable Function (EPUF) based on DRAM, which employs a data-driven characterization process designed to achieve near 100% reliability in simulation through a data-driven characterization process, which is validated through extensive simulation, addressing a critical limitation of conventional PUFs. To counteract sophisticated threats, we formulate the key management problem as a Markov Decision Process (MDP) and introduce a deep reinforcement learning (DRL) agent that dynamically optimizes key update frequency, balancing security posture against energy consumption. Furthermore, we leverage a lightweight, permissioned blockchain as a decentralized trust anchor for public key management, providing an immutable and resilient ledger and enhancing the principles of distributed and edge intelligence. The core authentication protocol's security is formally verified using the ProVerif tool and Belief Logic, proving its robustness against a Dolev-Yao adversary. Experimental simulations demonstrate that our framework significantly outperforms conventional methods, reducing authentication latency and energy consumption by over 95% compared to PKI-based schemes while effectively mitigating replay and impersonation attacks.
Given the Chinese government’s aim of high-quality development and under the double constraints of incremental land and limited local revenue, Chinese local governments' path-dependence on land finance is confronting significant challenges. Guangdong Province has implemented a series of urban renewal programs called Three Olds Redevelopment (TOR) projects, providing a potential alternative for financially sustainable development. Using panel data covering 21 prefecture-level cities in Guangdong during 2009–2018, this study constructed a quantitative analysis of the fiscal revenue effect of TOR. The results illustrate that TOR reduces fiscal revenues, including tax revenue and land-transfer fees, in the short term but increases them in the long term. Higher fiscal decentralization tends to amplify the fiscal effects, while market power and dependence on land finance may weaken the impact. As for the mechanism, TOR projects can increase long-term fiscal revenue because of social capital involvement and functional upgrading effects. We suggest urban renewal and policy innovation to transcend land finance path-dependence and realize sustainable local finance.
Hans Gersbach, Akaki Mamageishvili, Manvir Schneider
On several proof-of-stake blockchains, agents engaged in validating transactions can open a pool to which others can delegate their stake in order to earn higher returns. We develop a model of staking pool formation in the presence of malicious agents and establish existence and uniqueness of equilibria. We then identify the potential and risk of staking pools. First, allowing for staking pools lowers blockchain security. Yet, honest stakeholders obtain higher returns. Second, by choosing welfare-optimal distribution rewards, staking pools prevent malicious agents from receiving large rewards. Third, when pool owners can freely distribute the returns from validation to delegators, staking pools can disrupt blockchain operations, since malicious agents attract most delegators by offering generous returns.
• Blockchain-based system enables policy-grade traceability for plastic credits. • Smart contracts enforce automated compliance with EPR and ESG frameworks. • DApp supports decentralized oversight, reducing audit burden on regulators • System logs immutable offset records aligned with circular economy targets. • Architecture offers scalable digital infrastructure for waste policy integration. Plastic credit schemes are increasingly adopted to mitigate plastic pollution, yet existing systems remain centralized, opaque, and prone to double counting and fraud. This study proposes and validates a plastic credit system that leverages blockchain technology aimed at enhancing transparency, traceability, and accountability in plastic recovery efforts. A modular three-layer architecture was implemented, comprising a user interaction layer, a blockchain execution layer, and a utility layer for metadata and analytics integration. The system employs two smart contracts on the Polygon Proof-of-Stake (PoS) mainnet using Ethereum standards: ERC-20 for fungible tokenization of plastic credits and ERC-721 for non-fungible certificate issuance. Functional testing confirmed successful execution of token lifecycle operations. Stress testing across 5000 sequential transactions yielded stable performance, with average confirmation times of 5.29 s for fungible token operations and 5.59 s for non-fungible processes. A decentralized application (DApp) was developed to support role-based interaction, credit traceability, and certificate validation. User evaluation returned a high usability score (86.4%), while benchmarking against existing platforms demonstrated improved auditability, automation, and stakeholder control. These findings indicate that blockchain infrastructure can enable decentralized, tamper-resistant plastic credit systems. The proposed model provides a scalable foundation for Extended Producer Responsibility (EPR) compliance and plastic waste traceability, which could potentially support the credibility of Environmental, Social, and Governance (ESG) reporting and supporting circular economy transitions across diverse policy and economic contexts.
Sharding has emerged as a critical solution to address the scalability challenges faced by blockchain networks, enabling them to achieve higher transaction throughput, reduced latency, and optimized resource usage. This paper investigates the advancements, methodologies, and adoption potential of sharding in the context of Hedera, a distributed ledger technology known for its unique Gossip about Gossip protocol and asynchronous Byzantine Fault Tolerance (ABFT). We explore various academic and industrial sharding techniques, emphasizing their benefits and trade-offs. Building on these insights, we propose a hybrid sharding solution for Hedera that partitions the network into local and global committees, facilitating efficient cross-shard transactions and ensuring robust security through dynamic reconfiguration. Our analysis highlights significant reductions in storage and communication overhead, improved scalability, and enhanced fault tolerance, demonstrating the feasibility and advantages of integrating sharding into Hedera's architecture.
Ayan Sar, Sampurna Roy, Tanupriya Choudhury, Ajith Abraham
Generative adversarial networks (GANs) and diffusion models have dramatically advanced deepfake technology, and its threats to digital security, media integrity, and public trust have increased rapidly. This research explored zero-shot deepfake detection, an emerging method even when the models have never seen a particular deepfake variation. In this work, we studied self-supervised learning, transformer-based zero-shot classifier, generative model fingerprinting, and meta-learning techniques that better adapt to the ever-evolving deepfake threat. In addition, we suggested AI-driven prevention strategies that mitigated the underlying generation pipeline of the deepfakes before they occurred. They consisted of adversarial perturbations for creating deepfake generators, digital watermarking for content authenticity verification, real-time AI monitoring for content creation pipelines, and blockchain-based content verification frameworks. Despite these advancements, zero-shot detection and prevention faced critical challenges such as adversarial attacks, scalability constraints, ethical dilemmas, and the absence of standardized evaluation benchmarks. These limitations were addressed by discussing future research directions on explainable AI for deepfake detection, multimodal fusion based on image, audio, and text analysis, quantum AI for enhanced security, and federated learning for privacy-preserving deepfake detection. This further highlighted the need for an integrated defense framework for digital authenticity that utilized zero-shot learning in combination with preventive deepfake mechanisms. Finally, we highlighted the important role of interdisciplinary collaboration between AI researchers, cybersecurity experts, and policymakers to create resilient defenses against the rising tide of deepfake attacks.
Pipelined Byzantine Fault Tolerant (BFT) consensus is fundamental to permissioned blockchains. However, many existing protocols are limited by the requirement for view-consecutive quorum certificates (QCs). This constraint impairs performance and creates liveness vulnerabilities under adverse network conditions. Achieving "certificate decoupling"-committing blocks without this requirement-is therefore a key research goal. While the recent BeeGees algorithm achieves this, our work reveals that it suffers from security and liveness issues. To address this problem, this paper makes two primary contributions. First, we formally define these flaws as the Invalid Block Problem and the Hollow Chain Problem. Second, we propose pBeeGees, a new algorithm that addresses these issues while preserving certificate decoupling with no additional computational overhead. To achieve this, pBeeGees integrates traceback and pre-commit validation to solve the Invalid Block Problem.Further, to mitigate the Hollow Chain Problem, we introduce a prudent validation mechanism, which prevents unverified branches from growing excessively. To summarize, pBeeGees is the first protocol to simultaneously achieve safety, liveness, and certificate decoupling in a pipelined BFT framework. Experiments confirm that our design significantly reduces block commit latency compared to classic algorithms, particularly under frequent stopping faults.
The Internet of Things (IoT) embeds various modules into physical objects to connect them to the internet. However, due to the low power and limited computing capabilities of IoT devices, it is difficult to ensure data integrity and secure access control. To address these limitations, various studies have attempted to integrate blockchain technology into IoT systems. Nevertheless, challenges such as low scalability and difficulties in storing large-scale data remain. In this paper, we propose the I2D (IOTA-IPFS-DID)-IoT system to overcome these issues. The I2DIoT system integrates Internet of Things Application (IOTA), InterPlanetary File System (IPFS), Decentralized Identity (DID), and Verifiable Credential (VC). In the proposed system, collected data is uploaded to IPFS, a distributed file system, generating a Content Identity (CID). Only the CID is recorded on IOTA, a distributed ledger technology, to ensure data integrity. In addition, a DID-based authentication mechanism enables access control without a central server. Experimental results show that the I2D-IoT system successfully performs both data integrity and access control. CPU and memory usage were measured, and CPU utilization remained under 10%, except during synchronization with the IOTA network.
Riya Sharma, Prabhdeep Singh, Rohan Verma, Deep Mann
In the last decade, blockchain technology has evolved in various applications, especially in healthcare systems where decision-making needs to be reliable, secure, and transparent. In these applications, the main concern is the storage and transfer of the patient's medical data to offer solutions for data privacy, user control over their data access, and satisfying regulatory compliance. Moreover, due to the need for security assurance and integration with other systems, i.e., traditional healthcare systems, the platform needs a system that, in addition to storing the data, can be able to simplify exchange and manage patient data records through collaboration with smart contracts. This chapter considers the constraints surrounding the role and growth of blockchain smart contracts in promoting healthcare regulatory compliance, positional questions that are yet to be resolved or explored, and boundaries in the underlying structure of international regulatory policies. The chapter introduces the methodological issues inherent in the process of resolution of regulatory compliance. The main purpose is to help system designers understand how to systematically evaluate how blockchain-based systems comply with General Data Protection Regulation (GDPR) requirements. Further the study proposes a set of smart contracts for a blockchain-based healthcare data exchange aimed at the resolution of regulatory compliance accounting issues.
As information technology underpins advances in life and healthcare sciences, there is a growing intersection of healthcare and information engineering that is opening new possibilities for remote health monitoring and the secure exchange of health information between patients and clinicians. To gain the trust of the citizens, healthcare technologies need to ensure that the information they store and process is confidential, has not been tampered with, and, in the case of large-scale processing, is conducted according to probabilistic compliance policies. Currently, the onus on data protection practices of healthcare technology providers is drawn from legislation. This paper outlines data integrity and compliance policies and shows how these can be encoded in a blockchain-based system. The study enhances this blockchain-based system to use the Ethereum blockchain for executing smart contracts, which can execute probabilistic compliance rewarding health-related workflows. These smart contracts increase transparency and data integrity by not only laying out a set of promises for citizens and public health physicians to monitor the state of a blockchain protocol to ensure there are no attempted violations, thus increasing the service's trustworthiness. While the security of the blockchain is used to ensure data privacy and security, all blockchain-located proxy healthcare data can only be accessed through patients appointing a blockchain address and the associated articulated smart contract at their own discretion, making the proposed solution particularly patient-centric.
Consortium blockchains (e.g., Hyperledger Fabric) have been adopted for inter-organizational supply chain management, while NFTs have gained attention to represent asset uniqueness and ownership on public blockchains (e.g., Ethereum). Applying NFTs to supply chains offers the potential to track individual products and enable seamless lifecycle management, including consumer engagement. Also, standardized smart contract interfaces for NFTs (e.g., ERC721) promote interoperability across blockchain services. However, traditional NFTs are limited to single-asset representation and lack features needed for enterprise use, such as support for composite products, access control, and efficient ledger storage. Recognizing the potential of ERC-7401 (nested NFTs) for enabling hierarchical NFT structures, this study investigates its application to traceability in consortium-based supply chains. We designed a nested NFT framework on Hyperledger Fabric, incorporating attribute-based access control and block size reduction mechanisms through transient arguments and batch processing. We implemented a prototype on Hyperledger Fabric that integrates block size reduction and access control mechanisms into Nested NFTs. Through design and implementation, we demonstrated the feasibility of this approach for complex supply chain management.
Kiran Deep Singh, Prabhdeep Singh, Ankita Gupta, Rohan Verma
The healthcare sector has vast untapped potential in data management in biotech, pharmaceutical companies, research centers, and other clinical institutions. Health research that involves access and analysis of individuals' health information can lead to a much-improved understanding, prevention, and treatment of health conditions. Blockchain's potential has been identified in various applications, including managing personal health data. There are extensive data sets that can advance patient care protocols and deepen the understanding of patient pathology, fostering the development of new treatments. However, there has always been a privacy concern, and the financial value of these datasets deters stakeholders from sharing their data. The regulatory body has provided protection in promoting patent rights and data sharing through initiatives like common health research data spaces and fair data principles. Trust in the healthcare industry is paramount, where the protection of patient information is critical. While patients can withdraw consent for data use in research, blockchain technology offers a solution for managing patient consent and facilitating the securing of the data. This research implements a smart contract system for patient consent management and data sharing amongst state holders, which includes patients, researchers, data controllers, and supercomputer owners. Unlike traditional healthcare data management models, this mechanism shifts power from data controllers to a consortium of stakeholders. This chapter proposes a permission blockchain and smart contract mechanism that can enhance data sharing and consent management in healthcare, offering a more flexible and secure approach to handling sensitive health data.
Blockchain Technology Applications and Security
FinTech, Crowdfunding, Digital Finance
Artificial Intelligence in Healthcare and Education