The modern integrated circuit ecosystem is increasingly reliant on third-party intellectual property integration, which introduces security risks, including hardware Trojans and security vulnerabilities. Addressing the resulting trust deadlock between IP vendors and system integrators without exposing proprietary designs requires novel privacy-preserving verification techniques. However, existing privacy-preserving hardware verification methods are all simulation-based and fail to offer formal guarantees. In this paper, we propose ZK-CEC, the first privacy-preserving framework for hardware formal verification. By combining formal verification and zero-knowledge proof (ZKP), ZK-CEC establishes a foundation for formally verifying IP correctness and security without compromising the confidentiality of the designs. We observe that existing zero-knowledge protocols for formal verification are designed to prove statements of public formulas. However, in a privacy-preserving verification context where the formula is secret, these protocols cannot prevent a malicious prover from forging the formula, thereby compromising the soundness of the verification. To address these gaps, we first propose a blueprint for proving the unsatisfiability of a secret design against a public constraint, which is widely applicable to proving properties in software, hardware, and cyber-physical systems. Based on the proposed blueprint, we construct ZK-CEC, which enables a prover to convince the verifier that a secret IP's functionality aligns perfectly with the public specification in zero knowledge, revealing only the length and width of the proof. We implement ZK-CEC and evaluate its performance across various circuits, including arithmetic units and cryptographic components. Experimental results show that ZK-CEC successfully verifies practical designs, such as the AES S-Box, within practical time limits.
Open access
4 source records
cs.CR
cs.LO
Physical Unclonable Functions (PUFs) and Hardware Security
Three traits of decentralized finance are studied. First, the market impact function is derived for optimal-growth liquidity providers. For a standard random walk, the classic square-root impact is recovered. An extension is then derived to fit general fractional Ornstein-Uhlenbeck processes. These findings break with the linearized liquidity models used in most decentralized exchanges. Second, a Constant Product Market Maker is viewed as a multi-phase Carnot engine, where one phase matches the exchange of tokens by a liquidity taker, and another the change of pool size by a liquidity provider. Third, stablecoin de-pegging is a form of catastrophe risk. By using growth optimization, default odds are linked to the cost of catastrophe bonds. De-pegging insurance can act as a counterweight and a key marketing tool when the law forbids the payment of interest on stablecoins.
The centrosome, long recognized as the primary microtubule-organizing center (MTOC) of animal cells, is re-examined through the lens of information theory and systems biology. This preprint proposes a unifying hypothesis: the mother centriole within the centrosome acts as a non-genetic cellular ledger, a stable structural repository that accumulates molecular records of a cell’s replicative history and environmental exposures. These records—comprising specific post-translational modification (PTM) signatures, retained proteins, and structural alterations—are subsequently “read” by the cell to inform critical decisions regarding proliferation, differentiation, senescence, and apoptosis. We synthesize evidence from cell biology, gerontology, and evolutionary biology to construct the “Centrosomal Ledger Model.” This model positions the centriole not as a passive cytoskeletal component but as an active, heritable information-processing node that integrates temporal data across scales—from circadian rhythms to organismal aging. We detail the molecular mechanisms of information encoding (e.g., tubulin polyglutamylation, oxidative marks) and decoding (via ciliary signaling, proteostatic feedback, and mechanical transduction). The model’s implications challenge genetic determinism by highlighting structural inheritance, provides a material basis for cellular age, and offers novel, falsifiable avenues for experimental interrogation in aging and cancer research. Crucially, it suggests that modulating the “read-write” cycle of the centrosomal ledger could represent a new frontier in regenerative medicine.
Mohammed Dawood Dawood, Syed Saif Ullah Hussaini, Mohd Zain ul Abeddin, Bishal Hizli Hizli
Cryptocurrencies have emerged as a disruptive force in global finance, challenging traditional banking systems through decentralization, transparency, and borderless transactions. Initially perceived as speculative assets, cryptocurrencies have increasingly gained institutional recognition, raising important questions regarding their financial role, regulatory governance, and long-term sustainability. This study adopts a qualitative-dominant mixed-method approach based on secondary data analysis. Data were collected from peer-reviewed journals, institutional reports, regulatory documents, and reputable market analyses published over the last decade. Thematic and descriptive analyses were employed to examine trends in cryptocurrency adoption, regulatory responses, technological innovation, and sustainability efforts. The findings indicate that cryptocurrencies have evolved into recognized financial assets, with growing institutional participation and expanding applications in cross-border payments and decentralized finance. However, significant challenges persist, including regulatory fragmentation, cybersecurity risks, market volatility, and environmental concerns related to energy-intensive mining. Regulatory milestones such as the European Union’s MiCA framework demonstrate progress toward legal harmonization, while technological innovations such as Layer 2 solutions, interoperability protocols, and Proof-of-Stake consensus mechanisms support scalability and sustainability. The discussion links these findings to Technology Acceptance and Innovation Diffusion theories, showing that institutional adoption is driven by perceived usefulness, regulatory legitimacy, and technological compatibility. Market Regulation and Institutional theories further explain divergent national regulatory approaches and increasing global coordination efforts. Sustainability considerations emerge as a critical determinant of long-term viability, shaping both technological development and policy intervention. Cryptocurrencies represent a transformative element of the global financial system, offering opportunities for efficiency, inclusion, and innovation.
Contemporary enterprises encounter substantial difficulties managing information dispersed across varied cloud infrastructures, geographically separated facilities, and specialized application environments. Traditional centralized frameworks, including consolidated data repositories and analytical warehouses, demonstrate limited capacity to deliver the required velocity, accuracy, and contextual intelligence necessary for sustained digital progression. Multi-Cloud Data Mesh constitutes a transformative architectural approach, advocating decentralized, domain-centric methodologies that systematically address intricate governance complexities and interoperability obstacles at the organizational scale. This framework establishes operational foundations through four fundamental tenets: Domain-Oriented Ownership, Data as a Product, Self-Serve Platform, and Federated Computational Governance. These architectural pillars collectively resolve decentralization imperatives, scalability prerequisites, interoperability complications, and sovereignty considerations inherent in modern enterprise ecosystems. Through ownership distribution to specialized domains, product-oriented information treatment, self-service platform provisioning, and federated governance implementation, organizations attain necessary scalability, operational flexibility, and contextual precision for continuous innovation across sophisticated multi-cloud landscapes
As the United States Department of Defense (DoD) transitions toward Zero-Trust Architecture, the hardware and software supply chain remains a critical vulnerability. Current provenance models rely on centralized, siloed databases that lack the transparency required to counter sophisticated state-sponsored interdiction. This paper proposes a novel framework: AI-Enhanced Trust Graph Analytics over Distributed Ledgers. The architecture utilizes a permissioned Distributed Ledger Technology (DLT) substrate to host an immutable record of component lifecycles, anchored by Hardware Roots of Trust (RoT) through Physically Unclonable Functions (PUFs). By mapping silicon fingerprints to Software Bill of Materials (SBOM), the system constructs a multi-dimensional Trust Graph. We employ Graph Neural Networks (GNNs) to detect structural anomalies indicative of subversion, while Federated Learning enables inter-agency intelligence sharing without compromising operational security. Our findings demonstrate that this integrated approach significantly reduces the time to detect compromised assets in air-gapped and tactical environments, providing a strategic roadmap for an autonomous, self-healing supply chain.
Open access
3 source records
Physical Unclonable Functions (PUFs) and Hardware Security
The digitization of medical records in the healthcare sector demands robust mechanisms to ensure data confidentiality, integrity, and privacy. This paper proposes an innovative multi-factor authentication (MFA) mechanism that leverages smart contracts and blockchain technology to secure the tracking of medical records. The proposed system, named Blockchain Authentication with Zero-Knowledge Proof (BAZKP), provides a tamper-proof environment for storing and accessing records while preserving users’ personally identifiable information (PII). A key novelty of BAZKP lies in storing only the character count structure of passwords rather than the actual credentials, combined with zero-knowledge proofs (ZKP) to verify identity without exposing sensitive data. This hybrid blockchain/ZKP approach addresses limitations of centralized and hardware-based solutions, reducing vulnerabilities while avoiding the cost and usability constraints of dedicated hardware systems. The system was implemented and tested on a private Ethereum testnet, with a proof-of-concept application developed using Solidity, Web3.js, and MetaMask. Performance evaluation over 100 transactions for core operations (registration, login, and password reset) demonstrated practical viability: registration incurred the highest latency (≈4500 ms) and gas consumption (≈120,000 gas), while login and reset operations were more efficient (≈4000 ms/80,000 gas and ≈3500 ms/60,000 gas, respectively). Comparative security analysis against existing MFA methods—including 2FA, hardware tokens, and biometrics—confirms that BAZKP provides superior privacy protection through decentralization and ZKP, without the cost and usability drawbacks of hardware-based solutions. Overall, this approach enhances trust in digital health systems by offering a secure, transparent, and privacy-preserving authentication framework for medical data, representing a significant advancement in digital healthcare security. Keywords: Blockchain; Multi-Factor Authentication; Smart Contracts; Zero-Knowledge Proof; Medical Record Security.
The article deals with the development and theoretical justification of a set of economic and mathematical models that ensure the risk management of decentralised research projects in the pharmaceutical industry using crypto-economic tools. The necessity of this development stems not only from the challenges posed by geopolitical instability and the obsolescence of the traditional “blockbuster” funding model in pharmaceutical corporations, but also from the development of highly specialised markets of medications for the treatment of rare diseases, research into longevity therapies, and the advancement of “long-tail science”, as well as new ways of organising research and development within the paradigm of decentralised science based on Web3 technologies. The study presents models that are unified by an endto-end risk management logic: from the assessment of management structure and human resource capacity, through fundamental valuation, to revenue distribution and protection against biomedical risks. The results obtained make it possible to establish threshold criteria for the management structure in scientific decentralised autonomous organisations (DAOs) and to formulate targeted recommendations for public authorities on improving the regulation of decentralised organisations.
Open access
Economic and Technological Systems Analysis
Digitalization and Economic Development in Agriculture
Electronic Health Records (EHR) are vital to modern healthcare, offering more effective means of electronically managing and accessing patient medical records. Using blockchain technology, this EHR makes use of Ethereum smart contracts for access and decentralized storage to provide security, transparency, and the ability to manage patient medical records in a tamper-proof way. The Interplanetary File System (IPFS), in conjunction with Pinata, provides immutable data storage for medical files. This EHR system combines smart contract-based access, decentralized storage of patient information, and a Web3 interface to support safe wardship of patient medical records while enhancing security and reducing administrative burden. It also includes a convolutional neural network (CNN) machine learning algorithm to harness the patient’s potentially harmful internal kidney conditions. The end product is an intelligent, data-driven, and secure EHR system that increases patient confidentiality of health information in settings with limited resources.
IoT data demands are growing, with Distributed Ledger Technologies (DLTs) offering secure data management, provided they can meet scaling and efficiency requirements that are more restrictive than in conventional application environments. This article comprehensively surveys 27 DLTs of varying paradigms and implementation methods, proposes a scoring method for determining DLT-IoT integration suitability, and then applies that method to the surveyed DLTs. Six DLTs were shortlisted as the most promising, which were then subjected to in-depth analysis around three IoT use cases: health-IoT, e-commerce and automotive manufacturing. We discuss the viability of lightweight DLTs and identify crucial future research directions.
Matteo Loporchio, Damiano Di Francesco Maesa, Anna Bernasconi, Laura Ricci
Abstract The ERC-1155 standard introduced on the Ethereum blockchain allows for managing multiple tokens, both fungible and non-fungible, within a single contract. It also supports batch transfers, thereby reducing transaction costs and enabling a more efficient use of blockchain resources. To assess its impact and level of adoption, this paper presents a comprehensive analysis of the ERC-1155 token ecosystem. First, we examine the activity of ERC-1155 contracts and compare the evolution of transfer volumes with those of the two alternative most popular token management standards. Next, we model the economy of each ERC-1155 contract as a directed graph, where nodes represent users and edges denote token transfers. We then study the topological properties of such graphs, analyzing approximately 40,000 networks until the end of 2024. Results indicate that, within our dataset, the adoption of ERC-1155 is growing, although its functionalities are not being fully utilized. Additionally, about 60% of the networks exhibit a completely centralized topology, while the remaining ones are generally sparse and lack small-world characteristics. Finally, the degree distribution analysis shows that preferential attachment is only present in a minority of the networks and the graphs also display a mild disassortative behavior.
Time Series Foundation Models (TSFMs) have emerged as a promising approach for zero-shot financial forecasting, demonstrating strong transferability and data efficiency gains. However, their adoption in financial applications is hindered by fundamental limitations in uncertainty quantification: current approaches either rely on restrictive distributional assumptions, conflate different sources of uncertainty, or lack principled calibration mechanisms. While recent TSFMs employ sophisticated techniques such as mixture models, Student's t-distributions, or conformal prediction, they fail to address the core challenge of providing theoretically-grounded uncertainty decomposition. For the very first time, we present a novel transformer-based probabilistic framework, ProbFM (probabilistic foundation model), that leverages Deep Evidential Regression (DER) to provide principled uncertainty quantification with explicit epistemic-aleatoric decomposition. Unlike existing approaches that pre-specify distributional forms or require sampling-based inference, ProbFM learns optimal uncertainty representations through higher-order evidence learning while maintaining single-pass computational efficiency. To rigorously evaluate the core DER uncertainty quantification approach independent of architectural complexity, we conduct an extensive controlled comparison study using a consistent LSTM architecture across five probabilistic methods: DER, Gaussian NLL, Student's-t NLL, Quantile Loss, and Conformal Prediction. Evaluation on cryptocurrency return forecasting demonstrates that DER maintains competitive forecasting accuracy while providing explicit epistemic-aleatoric uncertainty decomposition. This work establishes both an extensible framework for principled uncertainty quantification in foundation models and empirical evidence for DER's effectiveness in financial applications.
Despite being under development for over 15 years, transaction throughput remains one of the key challenges confronting blockchains, which typically has a cap of a limited number of transactions per second. A fundamental factor limiting this metric is the network latency associated with the block propagation throughout of the underlying peer-to-peer network, typically formed through random connections. Accelerating the dissemination of blocks not only improves transaction rates, but also enhances system security by reducing the probability of forks. This paper introduces SCRamble: a decentralized protocol that significantly reduces block dissemination time in blockchain networks. SCRamble's effectiveness is attributed to its innovative link selection strategy, which integrates two heuristics: a scoring mechanism that assesses block arrival times from neighboring peers, and a second heuristic that takes network latency into account.
This paper proposes a task-agnostic discovery layer for multivariate time series that constructs a relational hypothesis graph over entities without assuming linearity, stationarity, or a downstream objective. The method learns window-level sequence representations using an unsupervised sequence-to-sequence autoencoder, aggregates these representations into entity-level embeddings, and induces a sparse similarity network by thresholding a latent-space similarity measure. This network is intended as an analyzable abstraction that compresses the pairwise search space and exposes candidate relationships for further investigation, rather than as a model optimized for prediction, trading, or any decision rule. The framework is demonstrated on a challenging real-world dataset of hourly cryptocurrency returns, illustrating how latent similarity induces coherent network structure; a classical econometric relation is also reported as an external diagnostic lens to contextualize discovered edges.
This chapter examines the current inefficacious condition of cross-border payments and the challenges they pose to users and oversight authorities. In particular, it analyses the causes and implications of the ongoing decline in correspondent banking. It distills the Building Blocks in the G20 Roadmap to Cross-Border Payments , the progress made at the transnational level in their implementation, and the remaining obstacles. The discussion encompasses recommendations that are exploratory in nature, including the feasibility of new multilateral cross-border payment platforms and arrangements, the soundness of global stablecoin arrangements, and factoring an international dimension into the design of central bank digital currencies (CBDCs). The chapter suggests that, while the G20 Roadmap is comprehensive, it lacks certain elements, including the potential for utilising distributed ledger technology (DLT) to enhance the efficiency, speed, and safety of cross-border payments. It draws on theories of technology and innovation adoption to analyse DLT adoption for cross-border payments and the regulatory approach that should be taken to facilitate its use. Alongside the benefits of stimulating economic growth and enhancing financial inclusion over the long term, it explores the regulatory, legal, and institutional barriers that DLT infrastructure may present for cross-border payments.
Imagine you're explaining something new to a friend. You might say "the atom is like a tiny solar system" or "the brain works like a computer." We use these comparisons—analogies—constantly to understand unfamiliar things through familiar ones. They're how Darwin explained evolution (like selective breeding), how Rutherford explained atomic structure (like planetary orbits), and how we navigate everyday life. But here's the puzzle: while we have rigorous mathematical systems for logical deduction (if A then B), probability (how likely is X?), and other forms of reasoning, we've never had a formal system for analogy. When is an analogy actually valid? How much confidence should it give us? Can we combine multiple analogies? These questions have lived in philosophical limbo for over a century. What This Paper Does This paper creates the first complete logical system for analogical reasoning—essentially, the "mathematics of analogy." Just as probability theory gives us precise rules for reasoning under uncertainty, Analogical Logic (AL) gives us precise rules for reasoning by similarity. The Core Insight The key idea is that analogies aren't about surface similarities—they're about structural correspondences. A whale looks like a fish (similar shape, fins, lives in water), but that's a weak analogy because their deeper structures differ fundamentally (mammals vs. fish, lungs vs. gills, warm vs. cold-blooded). Meanwhile, the atom and solar system look nothing alike at the surface level, but make a powerful analogy because their relational structures match: a central massive body attracts smaller bodies that orbit it. The system captures this by separating: Relational structure: How things relate to each other (orbits, attracts, causes) Surface properties: What things are like individually (hot, charged, massive) How It Works The paper builds a complete formal system with five components: A language for precisely describing domains (like the solar system or atom) and mappings between them Five axioms that characterize how analogies behave: Every domain is perfectly analogous to itself If A is analogous to B, then B is analogous to A Analogies can be chained, but get weaker with each link Valid analogies must preserve relational structure Surface properties affect analogy strength but not validity Five inference rules for deriving new knowledge: Transfer relations from source to target Transfer properties (with reduced confidence) Recognize when differences weaken analogies Generate hypotheses by transferring explanations Strengthen conclusions when multiple analogies converge A strength metric (Σ) ranging from 0 to 1 that quantifies how good an analogy is, combining structural alignment with property similarity Soundness proofs showing that valid analogical arguments produce reliable conclusions with calculable confidence levels What Makes It Non-Obvious Some surprising results emerge: Non-monotonicity: Unlike deductive logic, adding true information can invalidate previous analogical conclusions. The whale/fish analogy weakens dramatically when you learn whales are mammals—new knowledge can break old analogies. Weak transitivity: If A is analogous to B and B is analogous to C, then A is analogous to C, but more weakly. Information degrades through analogical chains. Structure trumps properties: A perfect structural match with zero property overlap (Σ = 0.70) creates a stronger analogy than perfect property match with weak structure (Σ < 0.50). Seeing It In Action The paper works through historical scientific analogies in detail: Rutherford's atom (like a solar system): Calculates Σ = 0.80 (strong analogy), shows which inferences were valid (inverse-square force law) and which failed (continuous electron trajectories—quantum mechanics revealed this disanalogy) Darwin's natural selection (like artificial breeding): Calculates Σ = 0.88 (very strong), shows how the analogy generated the theory of evolution despite the key disanalogy (no intentional "breeder" in nature) Electricity (like water flow): Shows a moderate analogy (Σ ≈ 0.70) that's useful for engineering despite microscopic differences Why It Matters This isn't just theoretical housekeeping. The system: For AI: Provides foundations for machines to reason by analogy rigorously, with confidence estimates For science: Formalizes how analogies drive discovery and when to trust them For philosophy: Resolves century-old debates about the nature of similarity and analogical inference For education: Helps evaluate teaching analogies (which ones support learning vs. create misconceptions?) For everyone: Makes explicit the implicit reasoning we use constantly
Світлана Володимирівна Ковальчук, Віталій Григорович Федоришен
The article explores the fundamental essence and strategic role of investment capital within the context of the dynamic development of the stock market amidst the global digitalization of the economy. The authors conduct a comprehensive analysis of the conceptual apparatus, focusing on refining the definition, classification, and multifaceted functions of investment capital as a core resource for ensuring the financial stability of enterprises and maintaining a high level of liquidity in the securities market. Particular attention is paid to the transformation of capital from traditional forms into digital assets, a process that is fundamentally reshaping the architecture of modern financial relationships and global capital flows. The study demonstrates that the synergy between investment capital and digital technologies critically enhances market transparency, minimizes transaction costs, and accelerates the execution of financial operations. The research details the impact of cutting-edge technologies, such as blockchain-based trading, artificial intelligence for predictive analytics, and decentralized finance (DeFi) protocols, on the efficiency of capital allocation. Based on an empirical analysis of statistical data for the period 2021–2025, the correlation between investment capital inflows and key market capitalization indicators is identified. The paper further examines the influence of digital platforms on asset structures, price dynamics, and the overall resilience of the stock market to extreme volatility and external economic shocks. The authors reveal that digitalization acts as a powerful catalyst for the redistribution of capital i favor of high-tech sectors of the economy, thereby altering traditional investment paradigms. Furthermore, the research substantiates practical recommendations for stimulating the effective use of capital through the development of robust fintech infrastructure, the adaptation of regulatory frameworks to the requirements of the digital era, and the implementation of comprehensive programs to enhance digital financial literacy among market participants. The findings of the study demonstrate that the active involvement of investment capital under the conditions of stock market digitalization enhances the international competitiveness of the national economy and contributes to the sustainable development of the financial system. This article will be of significant value to researchers, financial sector practitioners, and investors interested in modern approaches to capital management and the evolution of the stock market under the ongoing pressure of digital transformation and technological progress.
The article provides a comprehensive comparative analysis of the fiscal decentralizatio mechanism in Ukraine and the European Union countries, with a focus on its impact on the financial capacity of local self-government. It is substantiated that fiscal decentralization is a key instrument for ensuring sustainable socio-economic development of territories, as it determines the level of budgetary autonomy, the stability of local budget revenues, and the ability of territorial communities to perform their own and delegated functions effectively. The current state and dynamics of fiscal decentralization in Ukraine during 2022–2024 are analyzed, taking into account the influence of martial law and war-related challenges on the structure of local budget revenues and the degree of dependence on interbudgetary transfers. The study conducts a comparative assessment of key quantitative indicators of fiscal decentralization in Ukraine and selected EU countries, including the share of local budgets in the consolidated public budget, the proportion of own-source revenues, the role of intergovernmental transfers, and the level of tax autonomy of local authorities. The results demonstrate that EU countries are characterized by higher financial stability of local governments, a greater share of own revenues, and more effective fiscal equalization mechanisms. Based on the analysis, quantitative benchmarks for adapting European fiscal decentralization practices to the Ukrainian context are proposed, aimed at strengthening the financial capacity of territorial communities. The findings may be used in shaping public finance policy, particularly in the context of European integration and post-war recovery of Ukraine.
Water scarcity represents one of the most critical challenges confronting arid and semi-arid regions, particularly under the intensifying pressures of climate change. In desert environments, limited freshwater availability constrains public health, food security, and socio-economic development, while traditional funding mechanisms often prove inadequate for scaling sustainable water infrastructure. This study examines the potential of decentralized finance (DeFi) bonds, combined with desalination and atmospheric water harvesting technologies, as an innovative financing and delivery model for enhancing water resilience in desert regions. The research adopts a qualitative, exploratory case study approach, drawing on a structured review of academic and policy literature, documented blockchain-based water initiatives, and a conceptual financial analysis of DeFi bond mechanisms. The OikosNomos.world (ONW) initiative is examined as the primary case study, with attention to its proposed deployment of solar-powered desalination systems, boreholes, and atmospheric water harvesting infrastructure. The analysis indicates that existing desalination and water harvesting technologies are technically viable in arid environments, particularly when integrated with renewable energy systems. Furthermore, blockchain-enabled DeFi bonds demonstrate potential to enhance transparency, automate fund allocation through smart contracts, and attract global impact-oriented capital beyond traditional grant-based models. However, the study also identifies key challenges, including regulatory uncertainty, governance complexity, infrastructure constraints, and the need for sustained community engagement. The paper concludes that while DeFi-financed water infrastructure is not a standalone solution to water scarcity, its strategic integration with proven water technologies and inclusive governance models offers a scalable and transparent pathway for strengthening desert resilience. Future empirical research and pilot deployments are required to validate financial performance, adoption outcomes, and long-term socio-environmental impacts.
V. T. Krishnaprasath, T. Surya, B. Suganthi, Mohammed Kasim M · 6 authors
The globalization of semiconductor supply chains and the rise of third-party IP reuse have intensified concerns around hardware Trojan insertion, counterfeit IP distribution, unauthorized overbuilding, and dispute-prone verification workflows in modern VLSI design. This paper proposes a Blockchain-Enabled Secure VLSI Framework that unifies distributed hardware verification, provenance tracking, and IP protection through tamper-evident ledger records and cryptographically verifiable design artifacts. The proposed framework registers RTL/netlist milestones, verification reports, test signatures, and PDK-dependent constraints as immutable transactions, enabling all stakeholders (IP vendors, integrators, foundries, and verification labs) to validate authenticity and integrity without exposing sensitive design content. To prevent IP leakage, the framework supports hash-anchored commitments, permissioned access control, and zero-knowledge–ready attestations for key verification claims (e.g., “coverage ≥ threshold” or “equivalence passed”) while keeping raw waveforms and proprietary constraints off-chain. A smart-contract policy engine enforces licensing (time-bound/feature-bound), audit logging, and revocation, while a lightweight on-chain/off-chain storage strategy ensures scalability. Analytical evaluation and prototype-level profiling indicate that the approach can provide end-to-end traceability with sub-second block confirmation in permissioned mode, ~25–45% reduction in dispute resolution time via automated audit trails, and ~15–30% lower manual compliance effort by standardizing verification evidence exchange. The framework is suitable for secure SoC integration, multi-vendor verification, and IP lifecycle governance in advanced VLSI flows.
Open access
Physical Unclonable Functions (PUFs) and Hardware Security
Fintech enterprises operate at the intersection of rapid technological innovation and stringent regulatory oversight, creating a complex organizational challenge. This review systematically examines organizational restructuring strategies that enable fintech firms to balance innovation and compliance. Drawing on the concepts of ambidexterity and contingency theory, the paper analyzes functional, divisional, matrix, and networked structures, highlighting their respective advantages and limitations for fostering innovation and ensuring regulatory adherence. Cross-functional teams, hybrid models, and embedded compliance practices emerge as key enablers for achieving dual objectives. The synthesis provides practical guidance for managers seeking to design adaptable organizational architectures, while also offering theoretical contributions to the literature on innovation management and regulatory alignment. Future research directions include cross-country comparisons, longitudinal studies, and exploration of emerging fintech models such as decentralized finance platforms.
Rahul Aravindh M, Prasannavelan R M, Pradeep N, K. Malathi
A secure and transparent blockchain-based voting system is proposed, designed to preserve voter anonymity, prevent tampering, and ensure one-vote-per-user compliance in decentralized digital elections. The system introduces a lightweight voter authentication layer using one-time password (OTP) verification, with off-chain hashed identity storage to prevent exposure of personal data. Unlike traditional models that rely solely on smart contract logic, this approach strengthens the end-to-end security boundary by validating user eligibility before on-chain interaction. Votes are cast through smart contracts deployed on a public blockchain, ensuring immutability and auditability, while maintaining voter anonymity by detaching authentication logic from vote recording. To address performance bottlenecks and storage limitations, non-critical identity data is excluded from the blockchain, with hashed authentication tokens acting as cryptographic proofs of voter legitimacy. The proposed method was validated through simulation of small-scale voting rounds, demonstrating secure vote casting with a rejection rate of 100% for duplicate or invalid attempts. Average authentication time remained under 200 milliseconds per session. The modular design facilitates integration with government or institutional ID systems and supports anonymous and verified voting modes, making it adaptable for educational, corporate, or civic deployment. Future iterations will explore zero-knowledge proofs to further enhance privacy guarantees while preserving voter eligibility validation.
This study uses the Diebold-Yilmaz (2012) and Baruník-Křehlík (2018) frameworks to examine time-varying volatility spillovers among five key rare earth minerals, cryptocurrencies, and macroeconomic uncertainty indexes. Our results reveal considerable cross-market spillovers (31.75% of total variance), which are short-term (29.97%, 1–4 days) in nature and over 50% during the COVID-19 pandemic. Ethereum (70.99%) and bitcoin (66.58%) emerge as predominant short-term transmitters, whereas dysprosium (31.45%) has a more long-lasting, cross-horizon effect. Macroeconomic uncertainty indices act as net recipients. This increased short-run spillover requires forward-looking macroeconomic policy and integrated risk management directed at cryptocurrency and strategic rare earths for financial stability.
Introduction Digital identity infrastructures used in electronic passports, national eID schemes, and federated authentication systems rely predominantly on centralised registries and classical public key cryptography. These architectures enable large-scale identity correlation, mass data aggregation, and single points of compromise, while remaining vulnerable to quantum attacks against RSA and elliptic-curve cryptography. There is no deployed identity framework that simultaneously provides post-quantum security, cryptographic privacy guarantees, and decentralised trust. Methods This study proposes a quantum-proof digital passport architecture combining lattice-based post-quantum cryptography, decentralised blockchain identifiers, and transformer-based decentralised artificial intelligence. The framework employs NIST-aligned post-quantum key encapsulation and digital signatures, zero-knowledge proofs for selective disclosure of identity attributes, and homomorphic encryption for encrypted identity verification. Blockchain oracles and decentralised identifiers enforce credential integrity and auditability without reliance on central identity providers. Transformer attention mechanisms support adaptive identity validation while preventing persistent identity profiling. Results Architectural analysis shows that the proposed system prevents quantum-enabled credential forgery, retrospective decryption, and cross-service identity linkability. Zero-knowledge verification removes plaintext exposure of personal data, and decentralised credential control eliminates central compromise vectors. The design remains interoperable with existing passport and eID infrastructures. Discussion The results demonstrate that secure post-quantum digital identity requires the combined application of quantum-resistant cryptography, decentralised governance, and cryptographic privacy enforcement.
Open access
Cryptography and Data Security
Quantum Computing Algorithms and Architecture
Physical Unclonable Functions (PUFs) and Hardware Security