The growing number of connected devices creates a strong demand for secure and private identity management across different networks. Conventional centralized systems suffer from a single point of failure, while many decentralized, blockchain-based solutions struggle to balance scalability, functional versatility, and privacy protection. To bridge these gaps, we propose a Blockchain-based Lightweight Dual-mode Authentication (BLDA) mechanism. BLDA introduces two distinct authentication pathways: the first achieves constant-time verification based on a dynamic cryptographic accumulator integrated with zero-knowledge proofs (ZKPs), offering optimal efficiency and unlinkability for simple membership checks. The second enables logarithmic-time verification based on a Merkle Patricia Trie (MPT) and ZKPs, providing efficient and privacy-preserving attestation of specific user attributes. Both modes ensure minimal information disclosure during authentication. A security and complexity analysis demonstrates that BLDA provides a secure and efficient framework, well-suited for large-scale applications requiring efficient cross-domain authentication.
Privacy-preserving computation enables multiple parties to jointly compute a function while keeping their inputs private. Protocols designed for the semi-honest model achieve high efficiency by assuming participants will correctly follow the protocol’s cryptographic steps. However, this security assumption is confined to the protocol’s internal execution, creating a crucial accountability gap. It offers no inherent method to prove that the inputs and function used in the computation actually align with what was externally agreed upon. In this paper, we introduce a novel framework that enhances privacy-preserving computation with public verifiability and accountability, while maintaining composability. Our framework leverages a blockchain as an immutable trust anchor to register cryptographic commitments of both participant inputs and the function’s specification. We then employ a zero-knowledge proof protocol to verify that the privacy-preserving computation is performed correctly using the committed data and function logic. The security of our model is formally proven to guarantee both input privacy and computational integrity, while our performance evaluation shows its practical scalability.
Decentralized anonymous credentials (DACs) enable users to prove possession of specific identity attributes without disclosing additional information or relying on a centralized authority. However, existing DAC schemes commonly rely on complex zero-knowledge proofs, resulting in high computational overhead. They also lack sufficient flexibility and efficiency in handling multi-authority environments and supporting complex access policies, while facing limitations in trust assumptions and scalability. To address these challenges, this paper proposes a novel threshold anonymous credential scheme. Specifically, we introduce a ciphertext-policy attribute-based encryption (CP-ABE) scheme that supports threshold key distribution and aggregation, and leverage it to construct the threshold anonymous credential scheme. Experimental results and security analysis demonstrate that the proposed scheme exhibits high efficiency and flexibility in constructing authentication for complex access policies.
Adaptive models and mechanisms of project financing, which are becoming critical for ensuring the sustainability of entrepreneurial activity in Ukraine in conditions of unprecedented military uncertainty were explored and analyzed in the article. Particular attention was paid to the need to integrate risk-sharing instruments between the public and private sectors. The study focused on transforming traditional approaches to assessing investments that have proven to be unviable in conditions of systematic military risk and mass destruction of capital assets, and to identify factors that minimize fiscal pressure and facilitate the attraction of private capital to critical recovery sectors. The methodology was based on the analysis of empirical cases (the «5-7-9%» program, grant mechanisms) and their critical comparative analysis using the real options theory (ROT) as a strategic framework for assessing managerial flexibility (relocation, expansion options). Global regulatory requirements (IFRS, RDNA4) and institutional risk transfer mechanisms (MIGA and DFC) were also systematized. The hypothesis of a direct proportional dependence of financing efficiency on the synergy between state compensation for systemic risk and the ability of enterprises to quickly adapt was substantiated. The results confirm that business sustainability was achieved through a two-vector mechanism: centralized risk absorption (MIGA/DFC) provides an «external anchor», and decentralized flexibility mechanisms allow the implementation of managerial options at the enterprise level. Empirical analysis showed the effectiveness of state credit risk subsidy programs and identified key challenges, which allowed formulating recommendations for the transition to mechanisms for subsidizing the cost of insurance premiums. The scientific value of the article lies in the substantiation of an adaptive project financing model that integrates ROT and institutional de-risking, as well as in the systematization of requirements for investors and forecasting possible consequences of modern financing models in Ukraine.
Cryptocurrency exchanges are integral to the digital asset economy; however, their rapid growth has been accompanied by recurrent high-impact cyberattacks that erode trust and inflict substantial losses. Guided by the PRISMA-ScR framework, this review systematically screened peer-reviewed and industry sources to construct a validated dataset of 220 major incidents (2009–2024) across centralized (CEX) and decentralized (DEX) exchanges. We classify attack vectors, analyze repeated high-impact patterns, and identify systemic vulnerabilities spanning cryptographic mechanisms and exchange infrastructure. Across CEX platforms, four of ten identified attack types accounted for 62 of the 80 incidents and approximately $1.764 billion in losses (42.1% of the $4.191 billion CEX total). Across DEX platforms, five of eighteen attack types were responsible for 120 of 140 incidents, totaling $3.755 billion (87.3% of the $4.303 billion DEX total). The overall losses sum to $8.494 billion across 220 incidents (80 CEX; 140 DEX). Repeated vectors comprised 182/220 incidents and $5.519 billion (65.0%) of losses, dominated by wallet/key compromise (78 incidents; $2.394 billion) and DEX system/server/protocol exploits (56 incidents; $1.939 billion); these two classes account for 134/182 repeated incidents (79.1%) and $4.333 billion (78.5%) of repeated losses. We examine the susceptibility of cryptographic defenses to emerging quantum adversaries and assess the exchange readiness for post-quantum threats. This study is the first to systematically compile and quantitatively analyze cybercrime incidents affecting both centralized and decentralized cryptocurrency exchanges in a unified dataset, enabling unprecedented comparability of systemic risks with actionable insights for cybersecurity researchers, regulators, and exchange operators seeking quantum-safe infrastructure evolution.
Abstract This study examines the economic and geopolitical determinants of Indonesia’s defense expenditure from 1984 to 2022 using the Autoregressive Distributed Lag (ARDL) model to capture both short-term and long-term dynamics. Recognizing the contextual relevance of Indonesia’s Total People’s Defense and Security System (SISHANKAMRATA), the analysis relies on conventional military expenditure data (% of GDP) due to the absence of consolidated multi-ministerial records. The results show that in the short run, defense spending is highly sensitive to macroeconomic shocks: inflation, exchange rate volatility, and foreign direct investment exert negative effects, while debt, trade openness, and regional military expenditure strengthen budgetary allocations. In the long run, macroeconomic fundamentals (debt, growth, inflation, and foreign investment) together with neighboring countries’ military spending drive defense expenditure, whereas regional average spending has a negative effect and U.S. military expenditure does not show a structural impact. These findings underscore the dual pressures of fiscal fragility and regional security competition in shaping Indonesia’s defense budget. Policy implications highlight the importance of inflation-adjusted and exchange rate–resilient budgeting, sustainable financing mechanisms such as defense bonds or a Defense Sovereign Wealth Fund (D-SWF), and deeper ASEAN defense cooperation to balance security needs with fiscal discipline. This study contributes a macro-level perspective on defense economics under conditions of institutional fragmentation, offering a framework for future comparative and panel-based research across decentralized security systems.
Ashar Prima, Dewi Gayatri, Yati Afiyanti, Christantie Effendy
Background: Indonesia faces a growing double burden of non-communicable diseases, particularly cancer. The latest data from the Global Cancer Observatory (Globocan) indicates over 408,661 new cases and 242,099 cancer-related deaths in 2022, with a projected 63% increase in the case burden between 2025 and 2040 without strategic intervention. Although a new legal framework through Health Law No. 17 of 2023 and the Minister of Health Decree (KMK) No. HK.01.07/MENKES/2180/2023 has mandated palliative care as an integral component of health services, its implementation still faces significant systemic barriers. Policy and Implications: This policy brief analyzes the disconnection between the policy mandate and on-the-ground reality, identifying critical gaps in accessibility, healthcare workforce capacity particularly among nursesand financing mechanisms through the National Health Insurance (JKN) program. The failure to effectively integrate palliative care not only causes unnecessary suffering for millions of patients but also burdens the health system with inefficient costs and suboptimal end-of-life care, reflected in the high "financial toxicity" experienced by patients. Recommendations: We recommend a four-pillar strategy: (1) Formalize and standardize palliative services within the JKN benefits package with a clear financing model to address regulatory ambiguity; (2) Develop a national competency-based palliative education and training strategy for all health workers, with a focus on empowering nurses in primary care; (3) Implement a decentralized and tiered palliative care delivery model centered on Community Health Centers (Puskesmas) to ensure equitable access; and (4) Launch a national public education campaign to destigmatize palliative care and increase awareness. Conclusion: The integration of palliative care is not merely an option but a strategic and ethical imperative for achieving Universal Health Coverage (UHC) in Indonesia. It is a cost-effective investment to improve patients' quality of life, support families, and ensure the sustainability of the national health system in facing future non-communicable disease challenges.
Open access
Healthcare Systems and Reforms
Palliative Care and End-of-Life Issues
Health Systems, Economic Evaluations, Quality of Life
Yu Gao, Carlo Campajola, Nicolò Vallarano, Andreia Sofia Teixeira · 5 authors
IOTA is a distributed ledger technology that relies on a peer-to-peer (P2P) network for communications. Recently an auto-peering algorithm was proposed to build connections among IOTA peers according to their “Mana" endowment, which is an IOTA internal reputation system. This paper’s goal is to detect potential vulnerabilities and evaluate the resilience of the P2P network generated using IOTA auto-peering algorithm against eclipse attacks. In order to do so, we interpret IOTA’s auto-peering algorithm as a random network formation model and employ different network metrics to identify cost-efficient partitions of the network. As a result, we present a potential strategy that an attacker can use to eclipse a significant part of the network, providing estimates of costs and potential damage caused by the attack. On the side, we provide an analysis of the properties of IOTA auto-peering network ensemble, as an interesting class of homophile random networks in between 1D lattices and regular Poisson graphs.
The rapid growth of fintech start-ups has led to a drastic change in the financial ecosystem in India, but at the same time, they are under scrutiny from various regulatory bodies because of the volume of risk associated with digital finance (specifically financial fraud, data security, and transaction risk) associated with digital finance. While compliance with the various regulations has historically been a lengthy manual process that involved multiple compliance departments and therefore had a high level of inherent error risk, with the introduction of blockchain technology, there is now the potential to develop compliance systems that use automated and tamper-proof processes that allow for an increased amount of transparency, auditability, and operational efficiencies. Thus, the main focus of this research paper is to evaluate how a blockchain-based system for regulatory compliance might impact fintech start-ups. A case study was conducted on CryptoShield Solutions Pvt. Ltd., a Mumbai-based RegTech company specializing in distributed ledger–based compliance platforms. The data was collected during the internship through observation, workflow analysis, discussions with professionals, and review of anonymized compliance records.The findings indicate that fintech firms adopting blockchain compliance tools have observed 35–45% reduction in manual reporting hours, improved accuracy, faster audit completion cycles, and stronger trust among investors and regulators. However, awareness remains shallow due to skill gaps, cost perception, and lack of standardized guidelines. The final recommendations of the study on stronger digital adoption efforts, awareness programs, and capacity-building initiatives will significantly accelerate the pace of blockchain-enabled compliance transformation in India.
Open access
Blockchain Technology Applications and Security
Innovations and Analysis in Business and Education
Democratic institutions increasingly rely on verifiable digital trust to enable fair participation and evidence-based decisions. Truvry is a decentralised protocol that converts behaviour-based evidence (usage patterns, transaction integrity, peer attestations) into portable cryptographic proofs that remain independent of any single platform or identifier, allowing individuals to transfer trust capital across domains while preserving privacy. The current prototype is zero-knowledge–compatible; in this version we use hashed proof anchoring and field-level redaction (no zk-SNARK module is deployed), with configurable smart-contract verifiers. By decoupling trust from identity, Truvry widens citizen inclusion, mitigates gatekeeping bias, and supplies auditable inputs for AI-mediated governance. In prototype tests (n=112), end-to-end proof issuance averaged 3.7 s (fastest local 1.4 s), verifier parse+check averaged 1.8 s, and the current minimum anonymisation entropy is 8.9 bits; gas costs for optional on-chain anchoring remained below US$0.02. All results are based on simulated user streams; a production pilot is planned.
This paper unveils a pioneering modular framework for Decentralized Explainable Artificial Intelligence (DeXAI), harnessing blockchain to deliver unparalleled trust and clarity in AI systems. Addressing the opacity and privacy challenges of centralized AI, our framework integrates federated learning with Explainable AI (XAI) methods, namely SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), to produce intuitive explanations for AI decisions across distributed networks. A blockchain layer secures predictions and explanations, with smart contracts ensuring ethical compliance and auditable trails. Designed for adaptability, the framework supports diverse AI models and blockchain platforms, excelling in critical sectors like healthcare and finance. Our prototype validates its scalability and effectiveness, setting a new benchmark for trustworthy AI.
Mohd Shahid Ali, Alam Ahmad, Mohd Atif, Monika Mittal · 5 authors
Decentralized exchanges (DEXs) are one of the keystones of decentralized finance (DeFi). Instead of booking the order under centralized system, you have straight peer-to-peer trades via Automated Market Makers (AMM). AMMs like Uniswap and Curve have actually been developed to reduce the friction of liquidity provisioning. Nevertheless, they still experience impermanent loss, deadweight loss, compartmentalization of market liquidity, in addition to suboptimal operation in volatile environments. This paper explains an RL-based algorithm that can regulate liquidity and flexible market-making in DEXs. RL agents has been trained to maximize capital allowance, liquidity rebalancing, and spread adjusting in live trading information from SushiSwap and Uniswap in addition to synthetically created cardiovascular test. DQN, PPO, and A3C are three RL algorithms that we have actually contrasted versus constant-product AMM standards. With risk-adjusted returns of as much as 1.6 vs. 0.9, an impermanent loss reduction of 15-20%, and test-set revenues of 12.5 -15.7% vs. 8, it seems that RL-poured method is considerably much better. The stability and scalability of RL models under different swimming pool dimensions and volatility regimes are further made certain by level of sensitivity analysis. Actually, PPO is the most effective in high-volatility circumstances, DQN assembles more quickly in moderate scenarios, and A3C offers a trade-off. Our results open up the design of flexible monetary AI systems and are right away appropriate to enhancing liquidity rewards, stability, and performance in DeFi. The result of the experiment shows that RL can be made use of to improve the rationality of liquidity administration in DEXs. The integration of administration systems right into multi-agent RL and the gas-efficient migration of these algorithms from off-chain to on-chain ought to be the primary tasks of future research study.
How do the emerging Web 3.0 technologies affect the survival of non-state armed groups (NSAGs) in their violent struggles vis-à-vis state entities? While techno-optimists argue that Web 3.0 can democratize the internet and curb monopolistic practices, its decentralized features, such as enhanced privacy, data ownership, and personalization, also present significant security challenges. These technologies can be weaponized by NSAGs to promote their efficiency and resilience. Borrowing insights from social movement theory, we construct a theoretical framework to explain how Web 3.0 applications affect the dynamics of NSAGs by impacting their organizational modes and strategies. It is argued that blockchain-based platforms, metaverse projects, and other Web 3.0 technologies promote the efficiency of the recruitment, training, financing, purchasing, and communication processes of NSAGs, increasing their capacities as social organizations, and thereby render these groups more resilient to collapse. We illustrate and corroborate our theoretical claims by examining the cases of how NSAGs such as the Islamic State utilize decentralized crypto exchanges and the Dark Web in their operations.
Open access
Terrorism, Counterterrorism, and Political Violence
The article examines the theoretical foundations for selecting algorithms and data structures to ensure secure storage and processing of metadata in IoT systems using the Ethereum blockchain. A classification of metadata types specific to heterogeneous IoT environments is presented, taking into account semantic significance, update frequency, and data criticality. Formal requirements for algorithms are formulated, covering resistance to forgery, computational complexity, scalability under high-intensity request loads, and resource efficiency in terms of gas costs and network throughput. A comparative analysis of data structures employed in the Ethereum infrastructure, including Merkle Tree, Merkle-Patricia Trie (MPT), Multi-State MPT, and GPU-accelerated modifications, is performed according to criteria such as asymptotic complexity, memory efficiency, and suitability for incremental updates. A conceptual model for organizing metadata exchange between IoT nodes and smart contracts is proposed, incorporating modules for encoding, verification, gas cost optimization, and standardized interaction interfaces. The presented results provide a theoretical basis for developing formally verified and energy-efficient solutions in the field of secure Ethereum blockchain integration with the Internet of Things.
The article examines the theoretical foundations for selecting algorithms and data structures to ensure secure storage and processing of metadata in IoT systems using the Ethereum blockchain. A classification of metadata types specific to heterogeneous IoT environments is presented, taking into account semantic significance, update frequency, and data criticality. Formal requirements for algorithms are formulated, covering resistance to forgery, computational complexity, scalability under high-intensity request loads, and resource efficiency in terms of gas costs and network throughput. A comparative analysis of data structures employed in the Ethereum infrastructure, including Merkle Tree, Merkle-Patricia Trie (MPT), Multi-State MPT, and GPU-accelerated modifications, is performed according to criteria such as asymptotic complexity, memory efficiency, and suitability for incremental updates. A conceptual model for organizing metadata exchange between IoT nodes and smart contracts is proposed, incorporating modules for encoding, verification, gas cost optimization, and standardized interaction interfaces. The presented results provide a theoretical basis for developing formally verified and energy-efficient solutions in the field of secure Ethereum blockchain integration with the Internet of Things.
In the context of the war, which has caused large-scale and systemic destruction of the energy infrastructure and significantly undermined the state’s ability to ensure stable and uninterrupted energy supply, there has arisen a need to revise the existing territorial energy provision systems due to the vulnerability of centralized infrastructure to physical and cyberattacks, as well as the necessity to secure a resilient, decentralized and autonomous operation of critical facilities and households. The purpose of this article is to examine the available potential and substantiate the prospects for cross-border cooperation of Transcarpathian territorial communities in the field of sustainable energy supply. The study reveals the existing potential of Transcarpathian in the sphere of sustainable energy, which is currently utilized at a very low level. It is well established that overcoming institutional, social, and infrastructural barriers to its full development requires strengthening cross-border cooperation both within existing programs and through new cross-border initiatives. One promising direction for an effective transition to sustainable energy supply in cross-border territories and communities is the creation of cross-border sustainable energy clusters, which would help mobilize investments, enhance coordination among government bodies, research institutions, and businesses, and reduce social tensions by involving communities in project planning and monitoring. An organizational model for cluster formation is proposed, representing an integrated multi-level system aimed at consolidating institutions, resources, and technologies of border regions to build a common energy space. Existing constraints to the model’s implementation under current conditions and possible ways to overcome them have been systematized. Keywords: sustainable energy supply, innovative sustainable energy clusters, renewable energy sources, territorial communities, cross-border cooperation.
This paper examines factors that influence prices of most common five cryptocurrencies such as Bitcoin, Ethereum, Dash, Litecoin, and Monero over 2010-2018 using weekly data. The study employs ARDL technique and documents several findings. First, cryptomarket-related factors such as market beta, trading volume, and volatility appear to be significant determinant for all five cryptocurrencies both in short- and long-run. Second, attractiveness of cryptocurrencies also matters in terms of their price determination, but only in long-run. This indicates that formation (recognition) of the attractiveness of cryptocurrencies are subjected to time factor. In other words, it travels slowly within the market. Third, SP500 index seems to have weak positive long-run impact on Bitcoin, Ethereum, and Litcoin, while its sign turns to negative losing significance in short-run, except Bitcoin that generates an estimate of -0.20 at 10% significance level. Lastly, error-correction models for Bitcoin, Etherem, Dash, Litcoin, and Monero show that cointegrated series cannot drift too far apart, and converge to a long-run equilibrium at a speed of 23.68%, 12.76%, 10.20%, 22.91%, and 14.27% respectively.
Introduction: Since its inception with Bitcoin in 2008, blockchain technology has evolved into a foundational infrastructure for secure, transparent, and decentralized systems across various sectors, including finance, supply chains, healthcare, and the internet of things (IoT). The introduction of Ethereum and smart contracts in 2015 catalyzed the development of decentralized applications (DApps) and decentralized autonomous organizations (DAOs), significantly expanding blockchain’s utility. Methods: A new blockchain trend is being created to address IoT applications requiring fast transaction processing accuracy. It is built on fast consensus mechanisms, including PBFT, sharding, the clustering principle, and the parallel execution of smart contracts Results: In this paper, we address the problems of parallel transaction execution consistency with respect to having the same transaction and smart contract execution order. Discussion: We analyze how current consensus mechanisms affect transaction ordering and explore methods to ensure deterministic execution without compromising speed or scalability. Conclusion: We propose a new algorithm called unordered global distributed transaction, which is based on order verification instead of time-consuming transaction consensus ordering.
In the context of cultural resource big data sharing and trading, existing practices face challenges such as coarse-grained permission management, privacy leakage during data delivery, and insufficient process automation. To address these issues, this paper proposes an integrated solution for permission management and automated transaction delivery. First, a multi-dimensional attribute permission model (MDAPM) for cultural resources is introduced, which leverages fuzzy mathematics and smart contracts to achieve dynamic authorization across the three dimensions of user, resource, and context, while incorporating zero-knowledge proofs (ZKP) to mitigate privacy leakage in permission verification. Second, a permission-data-address coupled delivery framework (PDACPF) is designed, which integrates the consortium blockchain and the interPlanetary file system (IPFS) for distributed storage to enable end-to-end automation of cultural resource data management, from preprocessing and transaction triggering through secure delivery. Simulation results demonstrate that under a lOO-concurrency scenario, the solution achieves a permission adjustment response time of less than 0.8 s, a data delivery success rate of at least 99.9%, and reduces privacy leakage risk to zero, thereby effectively supporting standardized trading and sharing of cultural resource big data.
Traditional Internet of Things (IoT) architectures suffer from centralization-induced vulnerabilities like single points of failure and data tampering. This paper proposes BITS, a lightweight blockchain-based framework designed for resource-constrained IoT environments. Its core is a novel Reputation-based Proof of Stake (RPoS) consensus algorithm that combines staking with behavioral reputation to elect reliable validators, significantly reducing energy consumption and computational overhead. BITS also employs smart contracts for automated device authentication and access control, alongside lightweight security techniques inspired by Advanced Metering Infrastructure (AMI). Theoretical and simulation-based analyses demonstrate that BITS achieves a throughput and latency profile compatible with typical IoT application requirements, while exhibiting superior resilience against DDoS attacks and malicious nodes compared to both Proof of Authority and centralized models. BITS effectively balances security, decentralization, and efficiency, offering a viable solution for enhancing trust in IoT ecosystems.
The Decentralized Autonomous Organizations (DAOs) are shaping the future of the governance by moving away toward the power of the communities making calls without a central body. Nevertheless, it is becoming harder to assess the quality of the proposals as those are increasing and the number of demands is increasing as well. Manual reviews need more man power, lack consistency and are prone to bias because each one can produce varying levels of clarity, possibility, and fit within organizational objectives. In this paper, we introduce the proposal evaluation system based on AI, which uses transformer-based Natural Language Processing (NLP) models and Explainable AI (XAI) to automate and interpret the assessments of DAO proposals. The system scores in three dimensions, including impact, feasibility, and goal alignment in a clear and continuous way, with the justifications in human-readable formats. Our solution promotes both the fairness and scalability of decision-making in DAOs by decreasing voter fatigue and achieving a more straightforward workflow in governing the activities. Trained and validated on real-world DAO proposal datasets, the model delivers high performance regarding accuracy, explainability, and user trust. The contribution of this project is to guide the community to the intelligent systems of governance in Web3 through how to increase the transparency and trust in them using automated decision-support tools leaving the decentralized nature of DAOs intact.
Distributed Ledger Technology (DLT) engineering practices commonly rely on the adaptation and development of components as key building blocks. However, incorrect component specifications can lead to architectural flaws, which may propagate to implementation stages and result in faulty configurations. To address this, we build on declarative modeling techniques from program verification and refactoring to formally specify DLT components and their architectural composition. We introduce a component-based approach, Alloy4CMD , for the formal modeling and analysis of DLT architectural design. This approach maps individual components into well-formed formal specifications, enabling decidable (bounded) reasoning and property checking. We further employ a lattice-based abstract interpretation to approximate component semantics, with verification carried out in Alloy through assertions expressing conformance to requirements. The analysis involves automated model finding with bounded consistency checks using the Alloy Analyzer. Our approach provides validated, reusable modules, composes them into a validated architectural meta-model that supports early-stage DLT architectural design, and is independent of any particular DLT platform.