Journal of Theoretical and Applied Information Technology
With the growing volume of health information it has become common practice to protect the patient identity while maintaining convenient access to the data. Due to varying flow of cyber security threats, traditional solutions never manage to get flexible access to data without compromising with overflow of data. To overcome these challenges focusing on patient data protection, in this paper, we propose a new Hybrid Integrated Hashing approach entitled "Dynamic Adaptive Hash-Block Access Control (DAHBAC) framework" using blockchain based advanced data access control mechanism. The dynamic multi factor hashing scheme can change in response to the current Vulnerability of data and access patterns, whereas data access control refers to leverage blockchain's immutability and decentralized structure that helps protecting patient privacy while allowing authorized persons to read. The dynamic hashing method prevents intruder attempts by making hash and easy to calculate but requiring real-time modification of the hash for access protection. This is made possible by harnessing the application of zero-knowledge proofs (ZKP) within the frame of blockchain to enable verification of information when there is no disclosure of the data. Compared with the conventional methods, testing of prototype in a health care organization resulted in 92% on attempts by unauthorized workers to enter the system and 7% increasing data retrieval rate. These findings shows that the proposed model is a perfect patient data protection pattern in ehealth systems, because it is not only secures patients data but also enhances the accessibility and scalability to handle more clients. It is enabled by the use of zero-knowledge proofs (ZKP) in combination with blockchain technology to verify information, while keeping the information secret.
Muhammad Zeeshan Ullah Khan, Syed Imran Zaman, Sharfuddin Ahmed Khan
This chapter explores how blockchain-enabled smart contracts can revolutionize supply chain finance (SCF) by reducing operational costs, mitigating fraud, and expediting settlements. Although SCF is recognized for its capacity to improve liquidity and foster trust, it grapples with fragmented documentation, manual oversight, and information asymmetries, particularly in complex industries like agriculture. Blockchain as a decentralized and tamper-proof ledger, addresses these pain points by creating a single, shared version of transactional data across stakeholders. When applied to SCF, blockchain’s transparency and permanence can encourage financial institutions to offer more favo rable loan terms, while its security features deter fraud and unauthorized data manipulation. A central element of blockchain’s promise in SCF is the smart contract, self-executing code that performs contractual obligations once specific triggers are met. By automating tasks such as invoice verification, payment releases, and milestone tracking, smart contracts eliminate the need for third-party enforcement and accelerate the cash conversion cycle. This automation proves especially beneficial in multilateral trade finance, reverse factoring, and agricultural financing, where timely and accurate exchanges of data underpin credit decisions. The upshot is lower transactional friction, improved traceability, and an enhanced ability to manage credit risk, all of which can unlock new opportunities for smaller suppliers and underserved markets.
Muhammad Zeeshan Ullah Khan, Syed Imran Zaman, Sharfuddin Ahmed Khan
This chapter explores the transformative potential of blockchain technology in the context of future supply chain finance (SCF). It begins by illustrating how blockchain’s decentralized and tamper-evident ledger architecture can address traditional inefficiencies in SCF. By recording each transaction in an immutable chain, blockchain not only improves transparency but also reduces fraud risk, particularly in complex supply chains involving multiple intermediaries. The discussion highlights smart contracts as a key innovation, capable of automating routine processes. Real-world implementations in areas ranging from e-commerce logistics to agricultural finance underscore the wide applicability of these concepts. Building on blockchain fundamentals, the chapter details how next-generation supply chains can harness decentralized data sharing and automated contract enforcement to boost operational efficiency, reduce counterfeiting, and manage disruptions more proactively. It examines how blockchain-based solutions can be enhanced by emerging technologies, which together enable real-time monitoring and predictive analytics. However, despite these advantages, organizations must carefully evaluate feasibility concerns. Issues of scalability, interoperability, and energy consumption persist, especially in public blockchains reliant on intensive consensus mechanisms.
Despite Pakistan's long-enduring chronic energy crisis, a recent surge in solar generation supplied nearly 25 % of the national electricity grid in the first quarter of 2025, broadening access and easing cost pressures. Conversely, IMF conditionality under IMF financing programs, such as tariff rebasing and a 10 % sales tax on solar imports, risk undermining these distributive gains. This study examines the interaction between solar adoption, IMF credit, and household electricity prices in shaping energy justice, using annual data from 2007 to 2024. We capture direct, mediated, and dynamic effects using time-series analysis and causal mediation models, respectively. Results show that a 1 % increase in solar generation reduces injustice by 0.142 points ( p < 0.01), with benefits most substantial in rural areas. Mediation analysis demonstrates that IMF credit alleviates injustice only when channeled through solar adoption ( β = 0.251 → SE; Sobel z = −2.47), while tariff hikes directly worsen inequality ( β = −0.399, p < 0.05) but partially induce adoption. VAR evidence reveals that price shocks immediately intensify injustice, whereas solar shocks reduce disparities gradually. Results conclude that solar energy holds great promise, but cannot single-handedly drive a just transition. Achieving equitable outcomes requires addressing policy barriers by removing regressive fiscal measures, safeguarding net-metering, dedicating IMF resources to decentralized solar projects in marginalized communities, and broadening targeted subsidies for low-income populations. A failure to implement such measures could render Pakistan's solar expansion exclusive, thereby widening inequality. • Solar deployment substantially reduces distributive energy injustice in Pakistan, with a 1 % rise in solar output lowering injustice by 0.142 points. • IMF credit enhances distributive energy justice only when directed toward solar investment. • Household electricity prices drive energy injustice: tariff hikes intensify inequality but spur limited, inequitable solar adoption. • Price shocks incur immediate and enduring distributive costs, whereas the equity benefits of solar adoption accrue gradually. • Achieving an equitable transition requires embedding distributive justice in fiscal and financing frameworks.
Min-Bin Lin, Bingling Wang, Fabian Y R P Bocart, Christian M Hafner · 5 authors
Abstract The market of non-fungible tokens (NFTs), driven by blockchain and smart contracts, provides both artists and art collectors an unprecedented marketplace with more security, flexibility, publicity, and freedom to monetize. Yet, the emergence of such a market has been considered to be packed with speculation and economic uncertainty, given the limited understanding towards this market. To provide a precise depiction of the NFT art market and gauge market volatility, we construct the Digital Art Index, a novel price index using hedonic regression on the top 10 liquid NFT art collections (as of 2023). Addressing artwork price inequality, which often disrupts the price discovery process, this paper introduces two innovative alternative methods: Huberization and score-based filtering. These methods effectively mitigate the influence of outliers, particularly in an emerging market with limited accessible observations. In conclusion, the NFT art market presents significant opportunities for large gains, which are often favoured by risk-takers, but also carries the potential for significant losses. Its pricing is necessarily determined by institutional creators and platforms, meaning that solo artists may not benefit significantly in the current market environment.
The rapid integration of IoT with edge computing has revolutionized various domains, particularly healthcare, by enabling real-time data sharing, remote monitoring, and decision-making. However, it introduces critical challenges, including data privacy breaches, security vulnerabilities, especially in environments dealing with sensitive information. Traditional access control mechanisms and centralized security systems do not address these issues, leaving IoT environments exposed to unauthorized access and data misuse. This research proposes Fuzzychain-edge, a novel Fuzzy logic-based adaptive Access control model for Blockchain in Edge Computing framework designed to overcome these limitations by incorporating Zero-Knowledge Proofs (ZKPs), fuzzy logic, and smart contracts. ZKPs secure sensitive data during access control processes by enabling verification without revealing confidential details, thereby ensuring user privacy. Fuzzy logic facilitates adaptive, context-aware decision-making for access control by dynamically evaluating parameters such as data sensitivity, trust levels, and user roles. Blockchain technology, with its decentralized and immutable architecture, ensures transparency, traceability, and accountability using smart contracts that automate access control processes. The proposed framework addresses key challenges by enhancing security, reducing the likelihood of unauthorized access, and providing a transparent audit trail of data transactions. Expected outcomes include improved data privacy, accuracy in access control, and increased user trust in IoT systems. This research contributes significantly to advancing privacy-preserving, secure, and traceable solutions in IoT environments, laying the groundwork for future innovations in decentralized technologies and their applications in critical domains such as healthcare and beyond.
This article builds upon previous research on the ethical tokenization of emotions and the concept of a ‘Sentiment Exchange,’ advancing it into a broader normative framework termed Emotional Economics 2.0. That initial contribution demonstrated how affective signals could be measured, exchanged, and governed to ensure dignity and fairness within digital economies. The present paper extends this framework into a broader theoretical model termed Emotional Economics 2.0, articulated through three normative laws. The First Law conceptualizes human attention as a conserved and finite resource, grounding emotional value in cognitive limitations rather than artificial scarcity. The Second Law introduces emotional flow as the basis of value creation, operationalized through a dual-channel system: a fixed universal allocation of 20 tokens per day to each individual and a variable issuance tied to the measurable emotional impact of registered entities. The Third Law formulates a global emotional sovereignty contract, envisioning emotional tokens as a universal right akin to a digital basic income embedded at the protocol level. The paper further explores Scenario 4, where emotional tokens function as a universal unit of account in a post-scarcity “post-economy.” Illustrative examples—such as housing, food, and luxury goods denominated in tokens—demonstrate how this model reframes wealth, value, and redistribution. Legal challenges, governance mechanisms (Global Ethical-Scientific Committee and Decentralized Autonomous Organization), and risks of manipulation are addressed as integral to the model’s design rather than as afterthoughts. By explicitly linking the original Sentiment Exchange proposal with the Three Laws of Emotional Economics, this work positions itself as a second step in an evolving research program. Rather than a predictive claim, it advances a normative horizon: an alternative to neoliberal scarcity logics that places dignity, emotions, and collective well-being at the center of economic thought.
Hasina Andriambelo, Hery Zo Andriamanohisoa, Naghmeh Moradpoor
Federated learning enables collaborative model training without sharing raw data, but practical deployments increasingly require verifiable guarantees that clients compute updates correctly. Zero-knowledge proofs can provide such guarantees, yet existing approaches face scalability limits due to the combined cost of polynomial commitments and fast Fourier transform (FFT) intensive verification. Pairing-based schemes offer compact proofs but incur high prover and verifier overhead, while hash-based constructions reduce algebraic cost at the expense of rapidly growing proof sizes. This paper proposes Hybrid-Commit, a polynomial commitment architecture for Binius zero-knowledge proofs that aligns cryptographic primitives with the algebraic structure of federated learning workloads. The scheme separates verification into additive and multiplicative phases: linear aggregation is handled using batched additive commitments optimized for binary fields, while non-linear constraints are verified via hash-based commitments over sparsely selected FFT domains. Proofs from multiple clients are combined through recursive aggregation while preserving non-interactivity. Experiments demonstrate scalability in prover time and proof size (near-constant prover time across 4–11 clients; 160 bytes per client representing 341× and 813× reductions vs. FRI-PCS and Orion), although verification time (762 ms per client) does not scale favorably, making the scheme suitable for bandwidth-constrained scenarios. The scheme achieves under 2% end-to-end training overhead with no impact on model accuracy, indicating that workload-aware commitment design can improve specific scalability dimensions of zero-knowledge verification in federated learning systems.
Blockchain Technology has presented a tamperproof and secure structure that supports the modern digital environment. Among the many consensus blockchain systems, the Proof of Stake (PoS) has emerged as an extensively used protocol due to its lower energy consumption and high security. However, the staking mechanism and creation of the validator process are the main parts of the PoS network, and any flaw in these processes can cause financial loss and breach the system's security. Therefore, we use formal verification methods for the verification and validation of the staking mechanism. In this study, we presented a formal model of staking processes in a PoS-based system, emphasizing the verification of accurate signer, stake allocation, creation of a validator with an NFT minted, and update timeline. The CSP\# language is used to specify these processes, and Linear Temporal Logics (LTL) are used to define the properties against these specifications. To verify that the system satisfies the critical properties, like verification of an authentic signer, secure stake registration, and deadlock-free validator creation using the Process Analysis Toolkit (PAT) model checker, and ensure the system's liveness and safety. Our results describe the importance of formal verification in enhancing the Polygon Blockchain's integrity by ensuring the staking mechanism in the stake manager contract is secure and reliable.
Francisco Angulo de Lafuente, Seid Mehammed Abdu, Nirmal Tej
This paper presents SiliconHealth, a comprehensive blockchain-based healthcare infrastructure designed for resource-constrained regions, particularly sub-Saharan Africa. We demonstrate that obsolete Bitcoin mining Application-Specific Integrated Circuits (ASICs) can be repurposed to create a secure, low-cost, and energy-efficient medical records system. The proposed architecture employs a four-tier hierarchical network: regional hospitals using Antminer S19 Pro (90+ TH/s), urban health centers with Antminer S9 (14 TH/s), rural clinics equipped with Lucky Miner LV06 (500 GH/s, 13W), and mobile health points with portable ASIC devices. We introduce the Deterministic Hardware Fingerprinting (DHF) paradigm, which repurposes SHA-256 mining ASICs as cryptographic proof generators, achieving 100% verification rate across 23 test proofs during 300-second validation sessions. The system incorporates Reed-Solomon LSB watermarking for medical image authentication with 30-40% damage tolerance, semantic Retrieval-Augmented Generation (RAG) for intelligent medical record queries, and offline synchronization protocols for intermittent connectivity. Economic analysis demonstrates 96% cost reduction compared to GPU-based alternatives, with total deployment cost of $847 per rural clinic including 5-year solar power infrastructure. Validation experiments on Lucky Miner LV06 (BM1366 chip, 5nm) achieve 2.93 MH/W efficiency and confirm hardware universality. This work establishes a practical framework for deploying verifiable, tamper-proof electronic health records in regions where traditional healthcare IT infrastructure is economically unfeasible, potentially benefiting over 600 million people lacking access to basic health information systems.
The parallel Byzantine Fault Tolerant (BFT) protocol is viewed as a promising solution to address the consensus scalability issue of the permissioned blockchain. One of the main challenges in parallel BFT is the view change process that happens when the leader node fails, which can lead to performance bottlenecks. Existing parallel BFT protocols typically rely on passive view change mechanisms with blind leader rotation. Such approaches frequently select unavailable or slow nodes as leaders, resulting in degraded performance. To address these challenges, we propose a View Change Optimization (VCO) model based on mixed integer programming that optimizes leader selection and follower reassignment across parallel committees by considering communication delays and failure scenarios. We applied a decomposition method with efficient subproblems and improved benders cuts to solve the VCO model. Leveraging the results of improved decomposition solution method, we propose an efficient iterative backup leader selection algorithm as views proceed. By performing experiments in Microsoft Azure cloud environments, we demonstrate that the VCO-driven parallel BFT outperforms existing configuration methods under both normal operation and faulty condition. The results show that the VCO model is effective as network size increases, making it a suitable solution for high-performance parallel BFT systems.
Christopher Blake, Chen Feng, Xuechao Wang, Qianyu Yu
A proof of the security of the Bitcoin protocol is made rigorous, and simplified in certain parts. A computational model in which an adversary can delay transmission of blocks by time $Δ$ is considered. The protocol is generalized to allow blocks of different scores and a proof within this more general model is presented. An approach used in a previous paper that used random walk theory is shown through a counterexample to be incorrect; an approach involving a punctured block arrival process is shown to remedy this error. Thus, it is proven that with probability one, the Bitcoin protocol will have infinitely many honest blocks so long as the fully-delayed honest mining rate exceeds the adversary mining rate. This means that an adversary cannot censor future transactions of a user in perpetuity, which would render the protocol useless.
This chapter examines how blockchain can act as a trust and provenance layer within freight-oriented Intelligent Transport Systems (ITS). It outlines the core features of distributed ledgers and smart contracts, and shows how they enable secure data exchange, end-to-end traceability, automated payments, and verifiable digital identity for vehicles, containers, and shipments. The chapter maps integration with Internet of Things (IoT), big data analytics, and policy tools so that verified events from the field inform planning, operations, and compliance. Two case studies, involving TradeLens activity at Indian ports and a pharmaceutical cold chain pilot in Germany, illustrate gains in clearance times, auditability, and service reliability, alongside limits created by governance and interoperability. Adoption challenges are assessed across scale, regulation, and organizational readiness. Future pathways couple blockchain with artificial intelligence (AI), automated compliance, and decentralized freight marketplaces. Overall, the chapter positions blockchain as enabling infrastructure for transparent, resilient, and sustainable freight systems.
Educational inequality persists across ASEAN despite improvements in enrollment and literacy, reflecting structural rather than merely access-related challenges. This study examines how governance structures, financing mechanisms, institutional capacity, and socio-economic stratification interact to produce disparities in educational access, participation, and progression. Using a qualitative-dominant mixed-methods design with cross-national comparative policy analysis, the research integrates macro-level quantitative indicators with in-depth qualitative evaluation of policy frameworks across ASEAN member states. Findings reveal that while primary enrollment approaches universality, secondary and tertiary education exhibit pronounced attrition, particularly among rural, low-income, and minority populations. Centralized governance, equitable public financing, and targeted support correlate with higher retention and reduced disparities, as evidenced in high-performing systems such as Singapore, whereas decentralized or under-resourced systems exacerbate structural inequities. Moreover, digital access and institutional capacity emerge as critical factors influencing educational trajectories. The study underscores that addressing inequality requires systemic reforms integrating governance coordination, progressive financing, institutional strengthening, and equity-focused interventions. By foregrounding structural determinants and cross-national variation, this research contributes to theoretical and policy debates on educational equity, providing evidence-based guidance for ASEAN strategies aimed at achieving inclusive, high-quality education across diverse socio-economic and geographic contexts.
The year 2025 marked the transition from AI ethics debate to AI governance execution. Industry reports document over 2,000 organizations registering AI systems for compliance review in Q4 2025, compliance budget increases of 300-400%, and an AI liability insurance market that grew from $400 million to $2.1 billion. Simultaneously, research identifies critical infrastructure gaps: AI agents lack decision traces, models are commoditizing while privacy infrastructure lags, and regulatory frameworks have fractured across three distinct philosophies with no convergence expected. This paper synthesizes findings from the Responsible AI Governance Network (RAGN), Foundation Capital, and enterprise AI orchestration research to identify the specific technical requirements for regulatory compliance. It then presents the Y.I.N. (Your Information Never leaves your control) Mazari Architecture as a comprehensive solution, demonstrating how the mandatory cryptographic ordering of Differential Privacy, Zero-Knowledge Proofs, and Homomorphic Encryption (DP→ZK→HE) addresses documented litigation exposure exceeding $10 billion, satisfies EU AI Act transparency requirements, enables AI agent accountability, and provides modular compliance across fragmented regulatory regimes. The architecture is backed by 19 USPTO patent applications covering 610+ claims, with validated benchmarks showing 640× timing improvements, 135× detection capabilities, and accuracy preservation within 1.5 percentage points.
Open access
2 source records
Ethics and Social Impacts of AI
Law, AI, and Intellectual Property
Artificial Intelligence in Healthcare and Education
David López Jiménez, Eduardo Carlos Dittmar, Jenny Patricia Vargas Portillo
Stablecoins have emerged as a transformative yet controversial development in digital finance, promising stability while posing novel regulatory and systemic risks. Their growing role in payments, decentralized finance, and remittances raises questions about monetary sovereignty, consumer protection, and financial integrity. This chapter examines the governance of stablecoins through comparative and interdisciplinary perspectives, focusing on the evolving relationship between self-regulation, soft law, and hard law. Special attention is given to the Commonwealth Model Law on Stablecoins (2025), which provides a harmonised legislative framework designed to balance innovation with financial stability and inclusion. By comparing this model law with international approaches such as the EU's Markets in Crypto-Assets Regulation (MiCA) and voluntary standards from global standard-setting bodies, the chapter highlights how model laws can bridge the gap between flexibility and enforceability in the governance of digital money.
Abstract Decentralized exchanges (DEXs) allow traders to express their willingness to pay for quick execution through a public priority fee bidding mechanism. We provide evidence that high-fee DEX trades are more informative and contribute more to price discovery. Using address-level blockchain transaction data, we show that informed traders persistently bid higher fees to secure early execution, revealing a strong willingness to pay for execution priority. Further, analysis of Ethereum mempool data demonstrates that informed traders employ a “jump bidding” strategy, placing high initial bids to deter potential competitors.
Abstract The purpose of this study was to assess the dependence structure and volatility connectedness among the COVID-19 crisis, the 2022 Russia–Ukraine war, and their influence on cryptocurrencies, crude oil, developed markets, and the equity markets of China and ASEAN countries under varying market conditions. The analysis segmented the sample into three distinct periods: pre-COVID-19, during COVID-19, and the 2022 Russia–Ukraine conflict. To assess the dependence structure and risk spillover patterns across the markets for each period, we employed the generalized autoregressive conditional heteroskedasticity (GARCH)-extreme value theory (EVT)-vine copula and quantile vector autoregression (QVAR) connectedness methodologies. Findings from our GARCH-EVT-Vine-Copula model indicated that subsequent to the outbreak of COVID-19, market portfolios associated with the MSCI-developed markets index demonstrated significantly lower tail connectedness. However, the impact of the 2022 Russia–Ukraine war on the stock markets of China and ASEAN countries was found to be overestimated. Furthermore, the QVAR connectedness analysis revealed that connectedness was greater in bullish market conditions than in normal and extreme downside periods. Additionally, the portfolio analysis results suggested that the equity markets of China and ASEAN countries, along with the crude oil markets, cryptocurrency indices, and the MSCI developed markets index, were unable to achieve high levels of hedging effectiveness. Concurrently, it was recommended that investments be directed toward Chinese and ASEAN equities as safe-haven assets.
Defensive publication establishing prior art for proof-first digital identity systems using prime-indexed state evolution, zero-knowledge proofs, and silence-by-default semantics. This specification defines the Meta-Theorem of Prime Identity (MTPI), an architectural framework requiring cryptographic proof for every state transition. Core components include: Prime-Indexed Recursive Tensor Mathematics (PIRTM) with contractive dynamics guarantee; prime-gated activation with drift bounds δ(t) ≤ 0.3; Conscious Sovereignty Layer (CSL) with ethical tensor field commutation relations; Archivum append-only audit schema; and conformance requirements including Surveillance Fork detection. Reference implementations provided in Solidity and Circom 2.1. Mathematical foundations, alternative embodiments, and public domain designations included for maximum prior art scope. Keywords: zero-knowledge proofs, prime-indexed identity, verifiable computation, AI safety, defensive publication, proof-first computing, MTPI, PIRTM, CSL
Open access
2 source records
Cryptography and Data Security
Physical Unclonable Functions (PUFs) and Hardware Security
Zero-Knowledge (ZK) proof systems are cryptographic protocols that can (with overwhelming probability) demonstrate that the pair $(X, W)$ is in a relation $R$ without revealing information about the private input $W$. This membership checking is captured by a complex arithmetic circuit: a set of polynomial equations over a finite field. ZK programming languages, like Noir, have been proposed to simplify the description of these circuits. A developer can write a Noir program using traditional high-level constructs that can be compiled into a lower-level ACIR (Abstract Circuit Intermediate Representation), which is essentially a high-level description of an arithmetic circuit. In this paper, we formalise some of the ACIR language using SMT-LIB and its extended theory of finite fields. We use this formalisation to create an open-source formal verifier for the Noir language using the SMT solver cvc5. Our verifier can be used to check whether Noir programs behave appropriately. For instance, it can be used to check whether a Noir program has been properly constrained, that is, the finite-field polynomial equations generated truly capture the intended relation. We evaluate our verifier over 4 distinct sets of Noir programs, demonstrating its practical applicability and identifying a hard-to-check constraint type that charts an improvement path for our verification framework.