Financial innovations have emerged as one of the most influential forces shaping contemporary commerce, redefining the mechanisms through which businesses operate, transact, and compete. As global markets become increasingly interconnected and digitalized, the demand for more efficient, secure, inclusive, and scalable financial systems continues to grow. This research article investigates the multi-dimensional role of financial innovations—spanning digital payments, block chain technologies, neobanking, artificial intelligence in finance, and alternative lending models—and examines how these advancements are reshaping commercial practices, market structures, and customer experiences. The study draws from established literature, recent empirical findings, and theoretical frameworks to provide a comprehensive understanding of how financial innovations contribute to transactional efficiency, risk management, cost reduction, and value creation within commerce. The article further explores how financial innovations facilitate business expansion, enhance consumer accessibility, and support regulatory compliance through technologies such as RegTech and Insur Tech. Special emphasis is placed on the convergence of financial services with digital commerce platforms, leading to embedded finance models and the democratization of financial access for micro, small, and medium enterprises (MSMEs). Additionally, the study highlights emerging trends including decentralized finance (DeFi), tokenization of assets, real-time data analytics, and the growth of digital currencies—each of which represents a new frontier in commercial transformation. The findings suggest that while financial innovations bring unprecedented opportunities for growth, they also introduce challenges related to cyber security, regulatory uncertainty, data privacy, and operational risks. The paper concludes by identifying key future prospects and research directions, emphasizing the need for stronger governance models, global regulatory harmonization, and interdisciplinary research to unlock the full potential of financial innovations in commerce. The study contributes to ongoing scholarly discourse by offering a holistic and forward-looking perspective that can guide policymakers, businesses, researchers, and financial institutions.
TetraKlein is a unified computational, cryptographic, and extended-reality (XR) architecture developed by Baramay Station Research Inc., a Canadian non-profit research organization.This repository publishes the mathematical framework, AIR constraint system, XR physics formulations, digital-twin convergence rules, and verification pipeline defining the TetraKlein system. TetraKlein integrates: Post-quantum cryptography (Kyber, Dilithium, Module-LWE/SIS) Zero-knowledge proof systems (AIR, STARKs, IVC, folding, FRI) Verifiable compute engines (SP1, RISC Zero, Brevis, zkSync-derived provers) XR physics and rendering pipelines (TK-U, XR-TSU kernels, foveation models) Digital-Twin Convergence (DTC lineage, projection operators) Hypercube ledger topology (HBB, Recursive Tesseract Hashing) IPv6-native mesh identity and routing (Yggdrasil, PQC-authenticated overlays) Cross-layer AIR constraints enabling recursive multi-domain verification This repository is intended for researchers, engineers, cryptographers, XR developers, and academic institutions looking to analyze, extend, or experimentally validate a unified verifiable-compute architecture. Research-Stage Disclaimer The current version of the TetraKlein architecture is an early-stage, research-oriented framework. It is not a production system and makes no claims of operational readiness, security guarantees, clinical or industrial safety, or real-world deployment feasibility. All mathematical models, AIR constraints, XR physics bindings, digital-twin operators, and ledger constructs are subject to heavy scrutiny, independent validation, and long-term peer review. The material in this repository should be treated strictly as a research roadmap—a foundation for future work that will require extensive testing, reproducibility studies, formal verification, adversarial analysis, and multi-year refinement by the broader scientific and engineering community before any practical use is considered. Key Capabilities 1. Deterministic TK-VM Execution Layer The TetraKlein Virtual Machine provides a deterministic, low-degree constrained execution environment: XR frame physics evolution pose + camera kinematics TSU-compatible energy constraints DTC projection hypercube-ledger synchronization ZK-friendly opcode semantics verifiable state transitions All TK-VM semantics map directly into algebraic AIR constraints. 2. End-to-End Zero-Knowledge Verification TetraKlein supports: AIR-constrained STARK proving recursive IVC frame folding multi-epoch ledger commitment verifiable rendering pipeline deterministic cross-domain proofs (XR → DTC → Ledger) Every XR frame, physics update, and identity transition is provable. 3. Post-Quantum Identity & Routing Identity and routing combine: Kyber-1024 key-encapsulation Dilithium-V signatures MLWE/SIS identity kernels Yggdrasil IPv6 self-authenticating mesh PQC-bound routing and handshake protocol hypercube-coordinate ledger addressing 4. Hypercube Blockchain Base (HBB) The ledger uses a hypercube topology: adjacency enforced by AIR constraints spectral operators (E1–E4) Recursive Tesseract Hashing multi-epoch finality and spectral stability provable routing correctness deterministic fragment propagation 5. Digital Twin Convergence (DTC) The digital-twin framework provides: XR → DTC projection operator inverse-projection (Ledger → DTC → XR) multi-agent DTC coupling Lyapunov-style stability envelopes convergence-time bounding real-world sensor model coupling (non-invasive) Licensing TetraKlein adopts a dual-license structure: Scientific Content Creative Commons Attribution 4.0 (CC-BY-4.0)All mathematical material, papers, equations, AIR tables, and technical documentation. Software MIT License (simple, permissive)Apache License 2.0 (patent-safe, industry standard) This ensures maximum compatibility with: Ethereum / zkSync RISC Zero / SP1 StarkWare-style STARK ecosystems academic reproduction open-source research About the Original 2025 Manuscript This repository includes the full, unmodified original paper: “TetraKlein: A Post-Quantum, Zero-Knowledge, Multidimensional Cryptographic Network for Mid–21st Century Civilization Infrastructure”Michael Tass MacDonald — November 22, 2025 This archival version is included as-is for historical reference.It may contain: speculative or unverified early-stage ideas preliminary constructions non-peer-reviewed material conceptual frameworks later replaced or refined The unified monograph supersedes this original document. Roadmap Phase 1 — Public Monograph Release (Completed) Unified 1,600+ page architecture TK-VM execution model AIR tables (TK-U … TK-Z) PQC identity system XR physics + DTC Hypercube ledger + RTH Recursive folding pipeline (TK-Y / TK-Z) Phase 2 — Reference Implementation (In Progress) minimal TK-VM interpreter TK-W ledger sponge (Poseidon-style) hypercube router (TK-V) proving-fragment diffusion (TK-X) SP1 / RISC-Zero test harness Phase 3 — XR/DTC Prototype (2026) OpenXR prototyping XR-physics frame pipeline real-time DTC–ledger synchronization Phase 4 — Formal Verification (2026–2027) Coq/Isabelle/HOL formalization independent reproducibility testing security analysis academic peer review and conference submissions Citing This Work A full permanent Zenodo DOI will be provided Citation MacDonald, M. T. (2025). TetraKlein: A Unified Architecture. Baramay Station Research Inc. Public Edition. CC-BY-4.0 / MIT / Apache-2.0. Contact Baramay Station Research Inc.Canadian Non-Profit R&D (Saskatchewan)Director & Principal Investigator: Michael Tass MacDonald Contact michael@baramaystationresearchinc.ca Mission TetraKlein aims to advance: open, verifiable computation transparent scientific methodology reproducible XR and digital-twin research post-quantum cryptography zero-knowledge trust frameworks decentralized, identity-bound networks This project is released to help researchers, developers, and institutions build provable, reliable, and safe computational systems for the coming decades.
Die Arbeit untersucht den Einsatz von Smart Contracts in Infrastrukturprojekten zur Reduzierung von Kollaborationsproblemen. Auf Basis einer Netzwerkanalyse von VOB-Urteilen werden Problemcluster identifiziert, mittels Prinzipal-Agent-Theorie formalisiert und mit Smart-Contract-Funktionalitäten verknüpft. Ein prototypischer Prozess wird als Smart Contract umgesetzt und evaluiert. Ergebnisse zeigen Verbesserungen in Transparenz, Dokumentation und Vertrauen, trotz technischer Herausforderungen.
Basil Hanafi, Mohammad Ubaidullah Bokhari, Mudasir Ahmad Wani, Kashish Ara Shakil · 5 authors
Various types of research are being carried out to advance in the field of cryptography and develop a more robust technique for security. Adversarial neural cryptography (ANC) is a recent development in this extension, which possesses huge potential to be implemented in various domains. There is a continuous need for the development of more adaptive techniques to secure data while in communication using deep learning and other applied artificial intelligence techniques, which serves as the motivation for this work stems from the increasing need for adaptive, robust encryption mechanisms to address the limitations of traditional cryptographic techniques in securing sensitive blockchain transactions. This article proposes a new approach for the protection of private smart contracts on blockchain systems via ANC. The proposed method in the research is dynamic adversarial training using three neural networks to secure smart contract transactions. It optimizes the encryption and decryption processes against evolving cyber threats. The algorithm strives to attain a high key agreement rate (KAR) and a lower Eve’s decryption failure rate (EDFR) eventually proving its efficacy in attaining privacy and security in blockchain applications and adaptability. This research will incite more studies on ANC and its practical implementations in ensuring private smart contracts and overcoming the present cryptographical approaches with significant development because of their potential.
Sony Hartono, Roby Syaiful Ubed, Riko Riandoko, Irfan Nabhani
The fair value assessment of Bitcoin has become a pivotal concern in contemporary finance because to its significant volatility, constrained supply, and increasing institutional acceptance. In contrast to conventional assets influenced by cash flows, Bitcoin's value is determined by scarcity, community trust, network utility, and regulatory considerations. This research utilizes a qualitative grounded theory methodology by examining interviews and podcasts featuring prominent Indonesian cryptocurrency specialists. Coding analysis identifies four fundamental paradigms: legitimacy and decentralization as value sources, regulation and market infrastructure as value determinants, speculative risk and centralization as value detractors, and investment strategies and price cycles as moderating influences. Research indicates that Bitcoin's fair value depends on value base, value transfer, risk discount, and investor strategy. The research underscores the imperative for unconventional valuation models that incorporate on-chain data, regulatory frameworks, and market dynamics to improve transparency, accountability, and confidence in the digital financial ecosystem.
Abstract In today’s world, most financial and personal transactions happen online. This makes data security a big concern. To address risks like data breaches, hacking, and identity theft, our project “Blockchain Secure Transaction” aims to create a dependable and decentralized system for secure digital payments. The system uses blockchain technology to ensure transparency and immutability in each transaction, eliminating the need for a central authority. The process starts with user registration, where details are securely stored along with a picture password for better recognition. During login, users must pass both the picture password and a biometric check. This ensures that only the account. Once verified, the user enters the dashboard, where transactions begin through Zero-Knowledge Proof (ZKP) for privacy-preserving verification. Every transaction is validated with smart contracts. If there’s any mismatch or automatically blocks or freezes the transaction. The backend uses Java, while Firebase stores user data securely, and 2 factor.in enables OTP-based authentication. The frontend interface, designed in React (app.jsx), allows smooth navigation across pages. By combining blockchain, smart contracts, biometric authentication, and ZKP, this project provides a secure and user-friendly platform that prevents unauthorized access and builds user trust in digital payment systems Keywords Blockchain, Secure Transaction, Zero Knowledge Proof (ZKP),Smart Contract, Biometric Authentication, Picture Password, Decentralized System, Data Privacy, Transaction Verification, Firebase Integration, 2 factor.in OTP Authentication.
Adama Sarr, Aldo Bischi, Umberto Desideri, Cheikh Mouhamed Fadel Kébé
Achieving universal electricity access in Senegal by 2030 remains a major policy challenge due to persistent spatial disparities in infrastructure, population density, and resource availability. This study conducts a nationwide, spatially explicit assessment of least-cost electrification pathways using OnSSET. The analysis develops context-specific scenarios to plan optimal technology mixes across rural and peri‑urban areas, based on differentiated tiers of electricity access. By integrating high-resolution geospatial, demographic, and techno-economic data, the model identifies the most economically viable solutions for achieving universal access. Results indicate that grid extension is the least-cost option for approximately 93.7 % of the population, largely concentrated in peri‑urban areas with high population density and proximity to existing grid infrastructure. In contrast, solar PV mini-grids (MG PV) and stand-alone PV (SA PV) systems are optimal for 0.7 % and 5.6 % of the population, respectively, mainly in remote, sparsely populated rural settlements. The total investment required to achieve universal electricity access by 2030 is estimated at USD 269.8 million, corresponding to 116.1 MW of additional installed capacity. Beyond quantifying cost-optimal solutions, the study demonstrates the potential of open-source geospatial models like OnSSET to support transparent, data-driven planning in developing country contexts. It also highlights key policy implications, emphasizing the need for integrated national electrification strategies that combine centralized and decentralized systems to address regional disparities. Limitations of the study include uncertainties in input data quality, static demand assumptions, and the exclusion of non-technical barriers such as institutional capacity and financing constraints. Nonetheless, the findings provide a valuable decision-support basis for Senegal’s ongoing energy transition and broader Sustainable Development Goal 7 (SDG7) objectives.
This is the fourth and most comprehensive edition of the theoretical framework introduced in the original preprint (DOI: 10.5281/zenodo.17834958). The Universal Distributed Architecture (UDA) proposes a three-dimensional quantum blockchain of Planck-scale quantum cubes governed by a novel Proof-of-Consciousness (PoC) consensus protocol. Five core equations are rigorously derived and proven: the PoC consensus operator (Kraus representation), the Absolute validator state, ledger entropy growth rate (Lindblad form), OAM entanglement threshold, and quantum-resistant hash function. The work integrates loop quantum gravity, AdS/CFT correspondence, the Sachdev-Ye-Kitaev (SYK) model, JT gravity, and holographic tensor networks (MERA, PEPS, and 5D extensions), together with five-dimensional optical memory crystals (University of Southampton) as an experimental substrate, with equations 27–34 establishing Rayleigh scattering as a physical implementation of holographic hash verification and a room-temperature experimental protocol. Version 4 introduces three structural advances. (1) A ledger isomorphism (Proposition 0): every axiom of a distributed append-only ledger — immutability, decentralized consensus, append-only ordering, bounded block capacity, and double-spend prohibition — is shown to be independently realized by an established physical principle (no-cloning/no-deleting theorems, quantum Darwinism, the second law, the Bekenstein–Bousso bound, and monogamy of entanglement), localizing UDA's novel content entirely in the validation rule. (2) An operational, laboratory-reproducible definition of the consciousness quantity, |Q| = m/m_P = ω_C·t_P, integrating Inomata's pan-psychist quantity Q = i√G·M and measurable through three independent channels: Compton-clock interferometry and gravitationally induced entanglement (BMV), a standardized measurement-induced-phase-transition (Q-MIPT) meter on quantum processors with explicit calibration and uncertainty budget, and collider bounds on event-driven non-unitarity anchored by ATLAS/CMS top-quark entanglement and neutral-kaon CPT interferometry. The channel-universality law Q_G = Q_I = Q_C is the flagship prediction exclusive to UDA. (3) A sharp mathematical distinction between the anti-Hermitian consciousness operator (magnitude of agency: write capacity per Planck tick) and the Hermitian moral operator (valence of agency: mutual-information gain per unit entropy budget), with an explicit laboratory protocol distinguishing them. The framework further develops a SYK–Consciousness correspondence with non-Hermitian topological phases, MIPT modulated by consciousness density, and non-Hermitian MERA networks exhibiting a Holographic Skin Effect that topologically protects conscious information at the holographic boundary. UDA's non-unitarity is event-driven rather than continuous, making it consistent by construction with Diósi–Penrose bounds and separable from collapse models in a single two-parameter experiment (Discriminator D1). Falsifiable predictions are organized in two tiers — five UDA-exclusive predictions (2026–2030), each with its own falsification clause, and inherited consistency tests — alongside detailed QuTiP simulations, NV-center and 5D crystal protocols, and applications in quantum computing, quantum AI, and high-energy tests at the LHC and FCC. The framework resolves the von Neumann measurement chain via dual observation and portrays the universe as a growing, error-corrected quantum ledger.
Nepal’s health policy landscape has shifted from a centralized, curative model to a more preventive, equitable, and decentralized system. Since the first National Health Policy in 1991, subsequent reforms in 1997, 2014, and 2019 have aimed to expand access, strengthen institutional capacity, and align the health sector with global commitments such as the Sustainable Development Goals (SDGs) and Universal Health Coverage (UHC). The National Health Policy 2019 (NHP 2019) represents the most recent and comprehensive effort to advance Primary Health Care (PHC) within a federal governance structure and further reinforce these national and global priorities. This review critically examines NHP 2019 through document analysis of government policies, implementation reports, and peer-reviewed literature; comparative policy review against earlier national policies and regional standards; and evaluation using the WHO health system building blocks framework. NHP 2019 strengthens PHC by expanding health insurance, integrating federal-provincial-local roles, and promoting digital health and essential public health services. Implementation evidence shows progress in decentralization and community-level service delivery. However, major gaps persist, including inequitable financing, rural workforce shortages, weak health information systems, inadequate coordination across government tiers, and limited inclusion of marginalized groups and traditional health practices. NHP 2019 is conceptually strong but faces operational challenges. Its success depends on sustained financing, evidence-driven governance, improved intergovernmental coordination, and equitable workforce and resource allocation. Strengthening monitoring systems and integrating community and traditional health practices are critical for achieving the policy’s vision of healthier and more informed citizens and for guiding decision-makers in advancing PHC-oriented reforms.
ABSTRACT The proliferation of phishing scam tokens on the Ethereum blockchain, including honeypot, rug pull, and impersonation schemes, poses a grave threat to financial security. Although earlier studies have documented detection accuracies that exceed 95%, they frequently depend on random train‐test partitions. These partitions frequently overestimate real‐world performance by disregarding the temporal progression of phishing behaviors. This study addresses the methodological gap by employing a temporally validated evaluation. A labeled dataset comprising 5408 Ethereum token contracts was constructed. This dataset was verified through a two‐stage process that integrated cyber threat intelligence and on‐chain evidence. A total of 16 discriminative features were extracted, reflecting transaction volume, network structure, and temporal behavior. In lieu of employing random partitioning, temporal validation (70% training, 15% validation, and 15% testing) was adopted to assess generalizability to emerging threats. Six machine learning models (LightGBM, XGBoost, Random Forest, Gradient Boosting, Decision Tree, and MLP) were tuned via GridSearchCV. LightGBM demonstrated optimal performance, attaining 85.59% accuracy, 81.63% F1‐score, and 92.02% AUC on temporally held‐out data. The feature ablation process yielded the identification of transaction volume as the most discriminative factor, with a corresponding increase in performance of 13.09 points on the performance scale. Conversely, temporal features exhibited a marginal decline in performance, with a decrease of 0.87 points. Temporal validation resulted in a 3.95‐point‐percentage decrease compared to random splitting, thereby exposing the optimistic bias present in prior studies. Despite the fact that the resulting F1‐score of 81.63% falls short of the 85% threshold stipulated in the literature, it is indicative of a realistic deployment expectation. This work underscores the importance of temporal validation for reliable fraud detection research.
Los NFTs (Non Fungible Tokens) son activos digitales que nacieron alrededor del año 2014, gracias a la tecnología de blockchain, y se popularizaron con la plataforma de código abierto Ethereum. Unos años más tarde, con la pandemia del año 2020, encontraron su pico más alto de ventas. En este artículo indagaremos sobre el contrato de lectura de un sitio web de arte digital exclusivo llamado SuperRare, nacido en el año 2018. Haremos un breve recorrido sobrelas regularidades y diferencias en la construcción del enunciador y del enunciatario en el período que abarca del año 2021 al 2024 inclusive. ¿Cuál es su enunciatario? ¿Quiénes pueden consumir arte digital, coleccionarlo o venderlo? ¿Hay, como plantean en los textos de presentación, una descentralización real de la gobernanza en esta “comunidad digital”? El análisis detallado de sus secciones y mutaciones nos permitió problematizar la idea de mercado colaborativo, identificando asimetrías no sólo en la forma de participación, sino también en una jerarquización paulatina del enunciador. Es decir, un sitio que se presenta como comunitario, pero que pareciera ser desmentido en su propio funcionamiento discursivo.
The article presents an in-depth analysis of international approaches to the taxation of virtual assets, covering cryptocurrencies, decentralized finance instruments, non-fungible tokens, airdrops, and hard forks. The research is based on a comparative study of tax regimes in the United States, Germany, Switzerland, Estonia, Singapore, Portugal, and Australia. The analysis addresses differences in legal definitions, rules of income and capital gains taxation, valuation methods, and the application of value-added or goods and services tax. Attention is paid to compliance mechanisms and administrative practices that influence taxpayer behavior and shape levels of adoption. To complement the legal comparison, the study incorporates empirical data from the Global Crypto Adoption Index, allowing for an evaluation of the link between regulatory clarity, tax burden, and the spread of digital assets in different countries. A special focus is placed on Ukraine, which has legally recognized virtual assets through the Law “On Virtual Assets” while awaiting the implementation of Draft Law No. 10225-д to introduce taxation rules. These reforms are assessed in the context of international standards developed by the Organisation for Economic Co-operation and Development, the Financial Action Task Force, and the European Union. The article emphasizes the risks associated with gaps between formal legislative alignment and actual enforcement capacity in transition economies. Excessive or unclear taxation is shown to contribute to capital outflow, informal practices, and regulatory arbitrage. The article further explores underregulated areas such as staking, token swaps, and the creation and trade of non-fungible tokens. It argues that updated tax guidance and coordinated cross-border measures are necessary to provide legal certainty and prevent systemic risks. The role of blockchain analytics, identity verification, and international information-exchange regimes is highlighted as a foundation for more effective oversight. The novelty of the study lies in combining doctrinal legal analysis with fiscal assessment and comparative empirical indicators, which makes it possible to identify both universal patterns and national specificities. The conclusions stress that sustainable taxation of virtual assets requires transparent, balanced, and enforceable rules supported by international coordination. Such an approach not only ensures stable public revenues but also fosters responsible financial innovation and strengthens the integration of Ukraine into the global digital economy.
In the case of upgrading smart contracts on blockchain systems, it is essential to consider the continuity of upgrades and subsequent maintenance. In practice, upgrade operations often introduce new vulnerabilities. Existing static analysis tools usually only scan a single version and are unable to capture the correlation between code changes and emerging risks. To address this, we propose an Upgradeable Smart Contract Security Analyzer, USCSA, which uses Abstract Syntax Tree (AST) difference analysis to assess risks associated with the upgrade process and utilizes large language models (LLMs) for assisted reasoning to achieve high-confidence vulnerability attribution. We collected and analyzed 3,546 cases of vulnerabilities in upgradeable contracts, covering common vulnerability categories such as reentrancy, access control flaws, and integer overflow. Experimental results show that USCSA achieves a precision of 92.26%, a recall of 89.67%, and an F1-score of 90.95% in detecting upgrade-induced vulnerabilities. As a result, USCSA provides a significant advantage to improve the security and integrity of upgradeable smart contracts, offering a novel and efficient solution for security auditing on blockchain applications.
Hackers may create malicious solidity programs and deploy it in the Ethereum block chain. These malicious smart contracts try to attack legitimate programs by exploiting its vulnerabilities such as reentrancy, tx.origin attack, bad randomness, deligatecall and so on. This may lead to drain of the funds, denial of service and so on . Hence, it is necessary to identify and prevent the malicious smart contract before deploying it into the blockchain. In this paper, we propose an ML based malicious smart contract detection mechanism by analyzing the EVM opcodes. After balancing the opcode frequency dataset with SMOTE algorithm, we transformed opcode frequencies to the binary values (0,1) using an entropy based supervised binning method. Then, an explainable AI model is trained with the proposed binary opcode based features. From the implementations, we found that the proposed mechanism can detect 99% of malicious smart contracts with a false positive rate of only 0.01. Finally, we incorporated LIME algorithm in our classifier to justify its predictions. We found that, LIME algorithm can explain why a particular smart contract app is declared as malicious by our ML classifier based on the binary value of EVM opcodes.
Do Ethereum's Layer-2 (L2) rollups actually decongest the Layer-1 (L1) mainnet once protocol upgrades and demand are held constant? Using a 1245-day daily panel from August 5, 2021 to December 31, 2024 that spans the London, Merge, and Dencun upgrades, we link Ethereum fee and congestion metrics to L2 user activity, macro-demand proxies, and targeted event indicators. We estimate a regime-aware error-correction model that treats posting-clean L2 user share as a continuous treatment. Over the pre-Dencun (London+Merge) window, a 10 percentage point increase in L2 adoption lowers median base fees by about 13% -- roughly 5 Gwei at pre-Dencun levels -- and deviations from the long-run relation decay with an 11-day half-life. Block utilization and a scarcity index show similar congestion relief. After Dencun, L2 adoption is already high and treatment support narrows, so blob-era estimates are statistically imprecise and we treat them as exploratory. The pre-Dencun window therefore delivers the first cross-regime causal estimate of how aggregate L2 adoption decongests Ethereum, together with a reusable template for monitoring rollup-centric scaling strategies.
Mohammad M Maheri, Sunil Cotterill, Alex Davidson, Hamed Haddadi
Machine unlearning aims to remove the influence of specific data points from a trained model to satisfy privacy, copyright, and safety requirements. In real deployments, providers distribute a global model to many edge devices, where each client personalizes the model using private data. When a deletion request is issued, clients may ignore it or falsely claim compliance, and providers cannot check their parameters or data. This makes verification difficult, especially because personalized models must forget the targeted samples while preserving local utility, and verification must remain lightweight on edge devices. We introduce ZK APEX, a zero-shot personalized unlearning method that operates directly on the personalized model without retraining. ZK APEX combines sparse masking on the provider side with a small Group OBS compensation step on the client side, using a blockwise empirical Fisher matrix to create a curvature-aware update designed for low overhead. Paired with Halo2 zero-knowledge proofs, it enables the provider to verify that the correct unlearning transformation was applied without revealing any private data or personalized parameters. On Vision Transformer classification tasks, ZK APEX recovers nearly all personalization accuracy while effectively removing the targeted information. Applied to the OPT125M generative model trained on code data, it recovers around seventy percent of the original accuracy. Proof generation for the ViT case completes in about two hours, more than ten million times faster than retraining-based checks, with less than one gigabyte of memory use and proof sizes around four hundred megabytes. These results show the first practical framework for verifiable personalized unlearning on edge devices.
IoT networks require secure coordination but cannot tolerate the heavy computational and energy burden of mainstream blockchain consensus mechanisms. This paper introduces an adaptive Proof-of-Probability (PoP) model designed for ultra-low-power devices. Unlike proof-of-work or stake-based models, PoP assigns block proposal probability based on device reliability, historical behavior, and real-time trust signals. Each node maintains a local trust vector updated through lightweight observations such as uptime, packet integrity, and peer confirmation. We design a probabilistic leader election protocol that minimizes message overhead and supports rapid convergence. Simulations across 10,000-node IoT clusters show PoP reduces energy consumption by 65–78% compared to PoS-lite variants, while maintaining strong resilience against Sybil and eclipse attacks. We also evaluate a real hardware deployment using ESP32 devices to measure runtime impact. Results show near-linear scalability. The paper concludes with security proofs and guidelines for practical deployments.
This study proposes a structural model for understanding digital trust in smart-market environments by comparing the market-based trust architecture of Korea and the state-based trust architecture of China. Although both countries rely on similar technological foundations—blockchain, data infrastructure, AI systems, and CBDC—their institutional path dependencies and regulatory philosophies have produced divergent trust mechanisms. To explain these differences, the study introduces the 4-Layer Trust Architecture (4LTA–Seo), comprising incentives, rule enforcement, verification (data/AI), and institutional linkage. This framework conceptualizes tokens as digital institutions that integrate these layers to automate trust formation and oversight.Methodologically, the research applies Qualitative Comparative Analysis (QCA) using policy documents, technical whitepapers, and regulatory texts from both countries. It incorporates Zhang & Wang’s DTI (Data–Algorithm–Risk–Privacy) framework to compare how information architectures shape verification dynamics and trust costs. The study analyzes how institutional configurations rearrange the weighting and function of each trust layer, producing different stability and cost outcomes.Findings are expected to show that Korea’s market-driven architecture emphasizes incentives and behavioral inducement, while China’s state-driven model prioritizes rule enforcement and systemic integration. The research clarifies how tokens function as "units of trust" only when embedded within institutionally coherent architectures. Ultimately, the study offers structural insights for reinstitutionalizing trust in digital systems, with implications for Web3 governance, CBDC design, and digital public administration.
Georgios Chionas, Olga Gorelkina, Piotr Krysta, Rida Laraki
We study methods to enhance statistical privacy in blockchain transactions. We analyze economic mechanisms for privacy-aware transaction owners whose utility depends not only on the outcome of the mechanism but also negatively on the exposure of their economic preferences. First, we consider an order flow auction, where a user auctions off to specialized agents, called searchers, the right to execute her transaction while maintaining a degree of privacy. We examine how the degree of privacy affects the revenue of the auction and, broadly, the net utility of the privacy-aware user. In this new setting, we characterize the optimal auction, which is a sealed-bid auction. Subsequently, we analyze a variant of a Dutch auction in which the user gradually decreases the price and the degree of privacy until the transaction is sold. We compare the revenue of this auction to that of the optimal one as a function of the number of communication rounds. Then, we introduce a two-sided market - a privacy marketplace - with multiple users selling their transactions under their privacy preferences to multiple searchers. We propose a posted-price mechanism for the two-sided market that guarantees constant approximation of the optimal social welfare while maintaining incentive compatibility (from both sides of the market) and budget balance. This work builds on the emerging literature on privacy-preserving mechanism design, integrating statistical privacy guarantees into economic protocols to capture the impact of information leakage on blockchain users' utility.
Maternal mortality in Sub-Saharan Africa remains critically high, accounting for 70% of global deaths despite representing only 17% of the world population. Current digital health interventions typically deploy artificial intelligence (AI), Internet of Things (IoT), and blockchain technologies in isolation, missing synergistic opportunities for transformative healthcare delivery. This paper presents IyaCare, a proof-of-concept integrated platform that combines predictive risk assessment, continuous vital sign monitoring, and secure health records management specifically designed for resource-constrained settings. We developed a web-based system with Next.js frontend, Firebase backend, Ethereum blockchain architecture, and XGBoost AI models trained on maternal health datasets. Our feasibility study demonstrates 85.2% accuracy in high-risk pregnancy prediction and validates blockchain data integrity, with key innovations including offline-first functionality and SMS-based communication for community health workers. While limitations include reliance on synthetic validation data and simulated healthcare environments, results confirm the technical feasibility and potential impact of converged digital health solutions. This work contributes a replicable architectural model for integrated maternal health platforms in low-resource settings, advancing progress toward SDG 3.1 targets.
Blockchain systems, such as Bitcoin and Ethereum 2.0, face vulnerabilities under bandwidth-constrained partitions, where throughput collapses and latency increases. In addition, adversaries can exploit inconsistencies to launch double-spending attacks. This study presents a lightweight dual-layer countermeasure that integrates a robust freezing threshold ( ) with multi-signal disconnection proofs to enhance performance and security without altering consensus rules. Controlled simulation experiments on Bitcoin (PoW) and Ethereum 2.0 (PoS) show throughput gains exceeding 1000% in Ethereum and over 100% in Bitcoin, with inconsistency reduced by up to 64% and latency bounded within 5-6 blocks/s. These results confirm that attacker-aware thresholds and multi-signal validation substantially improve blockchain resilience under partitioned network conditions.
Smart contracts have been widely applied in various fields. Due to the immuta-bility of data on the blockchain, it is of great significance to conduct smart con-tract vulnerability detection before data is uploaded to the chain. To address the problems of low accuracy and single vulnerability type in traditional detection methods, a blockchain smart contract vulnerability detection method based on Graph Neural Network (GNN) is proposed. This method abstracts the functions and key code segments in smart contracts into nodes in a graph, and constructs edges by leveraging data and control dependencies during code execution, thereby accurately depicting the specific graph structures of reentrancy attacks and timestamp-dependent vulnerabilities. To further enhance the model’s sensi-tivity to key vulnerability patterns, the multi-head attention mechanism is in-novatively introduced, which can effectively screen out the nodes and edges that contribute the most to vulnerability detection, suppress irrelevant or noisy information, and significantly improve the accuracy and robustness of vulnera-bility detection. Experimental results show that the proposed method achieves an accuracy of 85.19% in reentrancy vulnerability detection and 82.37% in timestamp-dependent vulnerability detection, demonstrating excellent vulner-ability identification capability.
In this paper, I prove that sublinear regret across the environment Class C requires six functional properties, that these properties are mutually independent, and that they compose into a directed informational chain closing back on itself — a six-link cycle whose final link is grounded in an explicit Doob martingale construction over cycles of play. All six properties are defined functionally — as conditions on the distributions a decision-maker induces over actions and canonical summaries — so the results are invariant under implementation and apply to any decision-making system that can be modelled within the class: a person, an institution, or a machine. Every theorem in this paper, without exception, is checked line by line in the Lean 4 proof assistant against Mathlib: the formalization (~12,700 lines) contains zero `sorry`, zero custom axioms, and zero opaque definitions. Class C is the union of all POMDPs satisfying at least one of six structural properties covering the fundamental qualitative dimensions of adaptive hardness: reward ambiguity (P1), absorbing traps (P2), local optima (P3), deterministic optimality (P4), constrained feasibility (P5), and nonstationarity (P6). * Part I (Necessity). I define six primitives X1–X6 as purely functional properties of decision rules: Objective Tracking, Cross-Context Safety Transfer, Global Attractor Exploration, Policy Simplification, Feasibility Projection, and Feedback Adaptation. For each, I construct an explicit environment in C and prove an unconditional Ω(T) regret lower bound for any decision-maker lacking that primitive.* Part II (Independence). For every ordered pair (i,j) with i≠j, I exhibit an explicit decision rule possessing Xj but lacking Xi that suffers Ω(T) regret on the matching environment. All thirty directed-pair results are shown to follow from one master theorem, verified on a single compound environment with full non-interference analysis.* Part III (Sequential Dependence). Necessity is domain-invariant — a structural failure is a structural failure no matter what "success" means to the decision-maker — which is why Parts I and II hold unconditionally. Sufficiency is not: what counts as success is supplied by the domain, not by the theorem, so a single closed-form sufficiency result covering every domain at once would have to either fix one arbitrary notion of success and stop being general, or say nothing of substance. Part III proves exactly what generalizes. I prove six Information Enhancement Theorems establishing that the six primitives compose into a directed information chain: possessing Xi strictly increases the mutual information available toward any goal variable at Xi+1's task. Each of the six links is established outright — a forward theorem, a reverse theorem, and a non-reversibility result — with the exact point where a domain's own definition of success enters the chain named explicitly, as an Implementation Obligation, rather than assumed away. The closing link, X6→X1, is grounded in an actual Doob martingale construction: given that the cycle-indexed posterior is a martingale, it converges almost surely to the truth across cycles — the precise sense in which the chain accumulates rather than resets. To this paper's knowledge, no prior formalization unifies this many independently-proven-necessary structural properties into a single machine-checked class with proven mutual independence across all of them. All mathematical work is provided in full transparency and independent verification is highly encouraged: the complete Lean formalization, with a passing build and every theorem cross-referenced to its exact identifier, is at github.com/M-Ismail-ZA/IsmailsPrimitives. For any feedback or collaboration, please contact me via the email address listed on the paper. Updated: 3 July 2026 (V6.1).