Jinfa Hong, Bohao Zhang, Gaoyu Mao, Patrick S. Y. Hung · 5 authors
Lattice-based cryptography (LBC) is an essential direction in the fields of homomorphic encryption (HE), zero-knowledge proofs (ZK), and post-quantum cryptography (PQC), while number theoretic transformations (NTT) are a performance bottleneck that affects the promotion and deployment of LBC applications. Field-programmable gate arrays (FPGAs) are an ideal platform for accelerating NTT due to their reconfigurability and parallel capabilities. High-level synthesis (HLS) can shorten the FPGA development cycle, but for algorithms such as NTT, the synthesizer struggles to handle the inherent memory dependencies, often resulting in suboptimal synthesis outcomes for direct designs. This paper proposes a systematic HLS co-design to progressively guide the synthesis of NTT accelerators. The approach integrates several key techniques: arithmetic module resource optimization, conflict-free butterfly scheduling, memory partitioning, and template-based automated design fusion. It reveals how to resolve pipeline bottlenecks in HLS-based designs and expand parallel processing, guiding microarchitecture iterations to achieve an efficient design space. Compared to existing HLS-based designs, the area-latency product achieves a performance improvement of 1.93 to 191 times, and compared to existing HDL-based designs, the area-cycle product achieves a performance improvement of 1.7 to 10.6 times.
Existing regulatory frameworks for decentralized anonymous payments help combat illicit activities such as money laundering and terrorism financing. However, With the development of the European Union's General Data Protection Regulation (GDPR) and data sovereignty, Existing frameworks struggle to balance privacy with regulatory compliance, often compromising user autonomy and data sovereignty. To address this, we propose the Linkable Distributed Regulatory Tag (LDRT) scheme, which enables traceability without altering transaction structure by leveraging Paillier homomorphic encryption and secret sharing. Building upon this, we introduce Decentralized Anonymous Payment with Data Sovereignty(DAPDS), the first regulatory framework for decentralized anonymous payments that complies with data sovereignty. DAPDS supports both UTXO and account-based models and employs a distributed regulator group with incentives to prevent dishonest behavior. We define and analyze the security properties of both LDRT and DAPDS, proving anonymity, linkability, traceability, collusion resistance, data sovereignty, distributed regulation, and fairness of incentive mechanism. Our work provides a valuable reference for future regulatory framework paradigms for anonymous privacy-preserving traceable blockchain. Experimental results show that DAPDS only incurs an additional 34ms of time cost over ETH in transacation phase and an average of only 2.66s per tracing request in tracing phase.
Yield of tomatoes ( Solanum lycopersicum ) grown in high tunnels in Wanatah, IN, USA, was evaluated using two plant support systems: stake-and-weave or vertical-string. With stake-and-weave, plants were not pruned; stakes were placed every two plants and string was woven horizontally along either side of the plants along the row. With vertical-string, indeterminate cultivars Big Beef and Cherokee Purple were pruned to two stems and each stem was clipped to a vertical string. For the determinate cultivar Mountain Fresh, all branches below the first main stem flower cluster except one were removed and the main stem and major branches were clipped to vertical strings. Yield of US Department of Agriculture (USDA) no. 1 and no. 2 fruit was significantly higher for all cultivars with stake-and-weave than with vertical-string: Big Beef averaged 20.6 and 8.7 lb/plant, Cherokee Purple averaged 8.7 and 2.4 lb/plant, and Mountain Fresh averaged 20.1 and 16.9 lb/plant for stake-and-weave and vertical-string, respectively. The percentage of yield that was culled was less with stake-and-weave than with vertical-string for cultivars Big Beef and Cherokee Purple, but not for Mountain Fresh. Yield of USDA no. 1 and no. 2 fruit over the first 3 weeks of harvest was higher for stake-and-weave by 20% for cultivar Big Beef, and it showed no significant difference for cultivars Cherokee Purple or Mountain Fresh. In this system, when tomatoes were harvested for a period of 8 to 10 weeks, the stake-and-weave system produced more marketable yield than pruning to two or several stems and clipping each stem to a vertical string.
Eduardo Andrés Calderón Marenco, Romina Mariela Sánchez Silveyra, Juan Manuel Rodrigo, Gabriel Ravelo-Franco
El artículo analiza el efecto de la inteligencia artificial en la contratación inteligente y la protección de datos personales y argumenta que la automatización mediante smart contracts plantea desafíos regulatorios, ante la falta de un marco normativo adecuado para garantizar la privacidad y la seguridad jurídica en Latinoamérica. A través de un análisis comparado de los marcos normativos de Argentina, Perú, Colombia, Ecuador y la Unión Europea, se identifican avances y vacíos en la regulación de estas tecnologías, y se destaca que, aunque algunos países han reconocido la validez jurídica de los contratos inteligentes, la protección de los datos almacenados en blockchains sigue siendo un reto. Asimismo, se aborda el concepto de lex criptográfica como un sistema de autorregulación basado en la descentralización tecnológica, lo que genera tensiones con principios tradicionales del derecho. El artículo concluye que la creciente automatización contractual exige una actualización normativa que armonice la eficiencia tecnológica con la protección de los derechos fundamentales, y propone el desarrollo de un marco regulador que garantice la seguridad jurídica, la transparencia en el tratamiento de datos y la responsabilidad en la toma de decisiones automatizadas, conforme a los estándares internacionales y europeos de protección de datos.
Moderne Blockchains verarbeiten mittlerweile Zehntausende Transaktionen pro Sekunde. Mit steigendem Durchsatz wachsen jedoch auch die Anforderungen für die Verifikation von Blockchains. Zentralisierte Node-as-a-Service (NaaS)-Anbieter (z.B. Infura oder Alchemy) bieten zwar praktische APIs, schaffen jedoch zusätzliche Vertrauensabhängigkeiten und bergen Risiken in Bezug auf Datenschutz und Zensurfreiheit. Ein selbst betriebener Full Node ermöglicht Datenzugang ohne zusätzliche Vertrauensannahmen, ist für die meisten Nutzerinnen und Nutzer jedoch aufgrund des hohen Ressourcenbedarfs kaum praktikabel. Im Gegensatz dazu arbeiten Light Clients deutlich ressourcenschonender, können dafür den vollständigen Anwendungszustand nicht rekonstruieren. Ein neuer Ansatz, der als Sparse Client (bzw. Partially Stateless Client) bekannt ist, ermöglicht dagegen die verifizierbare Überwachung eines Teilzustands der Blockchain, indem ausschließlich jene Transaktionen heruntergeladen, ausgeführt und gespeichert werden, die diesen Teilzustand lesen oder verändern. Bisher fehlt eine fundierte wissenschaftliche Aufarbeitung: Die einzige verfügbare Arbeit zu diesem Thema weist deutliche Limitierungen auf und wurde weder implementiert noch umfassend evaluiert. In dieser Arbeit präsentieren wir zwei Sparse-Client-Protokolle für EVM-kompatible Blockchains: Sparseth für zustandsbasierte Synchronisation und Eventeth für ereignis-basierte Synchronisation. Beide Protokolle ermöglichen es Nutzerinnen und Nutzern, überprüfbare Teilmengen der globalen Transaktions- oder Ereignissequenz und des damit verbundenen Zustands zu verwalten, ohne dass zusätzlicher Validator-Aufwand erforderlich ist. Sparseth nutzt einen Interaktionszähler, um sicherzustellen, dass keine relevanten Transaktionen ausgelassen werden, während Eventeth eine kryptographische Hash-Kette einsetzt, um die Integrität und Vollständigkeit der Ereignisse zu gewährleisten. Im Gegensatz zu bestehenden Ansätzen arbeiten beide Protokolle vollständig auf der Ausführungsschicht und sind mit EVM-basierten Blockchains kompatibel. Unsere formale Analyse zeigt, dass beide Protokolle im angenommenen Widersacher-Modell Sicherheit, Liveness und spärliche Gültigkeit garantieren. Unsere Implementierung in Go demonstriert die praktische Umsetzbarkeit: Event Nodes senken den Bandbreitenbedarf um über 95%, Sparse Nodes reduzieren die auszuführenden Transaktionen um 92% gegenüber Full Nodes. Die Gas-Kosten steigen um 4-16% für typische dApp-Transaktionen, ein Mehraufwand, der sich durch L2-Lösungen und ökonomische Anreize weiter mindern lässt.
Rajasekaran P, M. Duraipandian, Johny Renoald Albert
The increasing rate of growth of the Internet of Things (IoT) in cloud-hospitality health has brought in data storage, transmission, and security challenges with the advent of quantum-enabled threats. Traditional compression methods struggle with computational inefficiency and the threat of invasion of privacy. This paper proposes a Quantum-Enhanced Zero-Knowledge Healthcare Compression Network for solving these challenges by combining Zero-Knowledge Proofs and Quantum-Inspired Deep Learning. The main goal is to provide privacy-preserving, efficient data compression along with optimizing computation costs and safeguarding sensitive healthcare records. Drawbacks in present cryptographic techniques, e.g., high computational costs in homomorphic encryption and scalability limitations in blockchain, require a novelty Adaptive Quantum-Assisted Zero-Knowledge Verification and Quantum Fusion-AutoCNN Encoder (QF-AutoCNN) to overcome this research. This work’s originality lies in combining Quantum zk-SNARKs, Hybrid Quantum Feature Encoding, and Reinforcement Learning-Based Challenge Optimization to provide better security, compression ratio, and verification efficiency. Experimental results show better accuracy (0.9816), improved F-measure (0.9709), and less computational overhead, better than other current methods such as convolutional neural networks-encryption and proxy re-encryption. This research greatly adds to safe cloud healthcare IoT by lessening privacy threats, maximizing storage space, and minimizing processing time, guaranteeing real-time handling of medical information.
The shift from fossil-based energy systems to renewable sources like solar, wind, and hydro presents both opportunities and challenges for developing countries aiming to expand energy access, promote economic growth, and meet climate goals. This study examines the technological, financial, institutional, and governance aspects of clean energy transitions, focusing on regional disparities and implications for low- and middle-income economies. A systematic review of literature was carried out using the SPAR-4-SLR methodology across Scopus, Web of Science, and Google Scholar. Only peer-reviewed studies published in English from 2009 to 2025 were included, guided by four research questions: (1) technological and resource endowments, (2) capital structuring and financial market dynamics, (3) institutional and policy frameworks, and (4) decentralized, digital energy governance. Search terms were tailored for each theme, and studies were classified by topic, region, and methodology. Results show that decentralized renewable systems—especially solar micro-grids—offer affordable alternatives to fossil fuels in rural and off-grid areas, enhancing job creation, energy security, and poverty reduction. Examples from Kenya, India, and Southeast Asia highlight the importance of policy consistency, financial innovation, and institutional preparedness in promoting clean energy deployment. Still, ongoing challenges such as high initial costs, infrastructure gaps, and limited technical skills continue to hinder progress in many regions. • Institutional and financial factors outweigh resource availability in clean energy. • Local policy tools often outperform broad international frameworks of clean energy. • Blended finance reduces cost barriers in early-stage clean energy projects. • Inclusive planning links clean energy to health and equity gains. • Technology transfer works best with local training and governance support.
Blockchain technology has emerged as a transformative force across various industries by providing a decentralized, transparent, and secure digital infrastructure. Central to this transformation are smart contracts, which are self-executing agreements that autonomously enforce and execute contractual terms without the need for intermediaries. While smart contracts offer significant advantages in terms of efficiency and security, their inherent rigidity and limited adaptability pose challenges in dynamic and complex environments. This situation prompts the critical question: How Far Should We Go from Smart Contracts to Smarter Contractors? Driven by the necessity to overcome these limitations, this systematic review investigates the evolution of smart contracts into smarter contractors through the integration of artificial intelligence (AI) and machine learning (ML) technologies. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, an extensive literature search was performed across multiple academic databases, identifying and analyzing 276 relevant studies published between 2015 and 2024. The analysis, structured around six key research questions, reveals that the incorporation of AI and ML has significantly enhanced the functionality, security, and adaptability of smart contracts throughout their lifecycle. These enhancements include automated code generation, formal verification, real-time monitoring, and adaptive management. Despite these advancements, persistent challenges such as scalability, interoperability, data privacy, and computational overhead continue to hinder the full realization of smarter contractors. Additionally, the advent of Large Language Models (LLMs) has further expanded the capabilities of smart contracts, enabling more sophisticated code generation, vulnerability detection, and intelligent auditing. This review underscores the pivotal role of AI and ML in addressing the limitations of traditional smart contracts, highlighting their transformative impact on the broader blockchain ecosystem and facilitating the development of more advanced and intelligent decentralized applications. Finally, we propose future research directions that emphasize the necessity for standardized frameworks, enhanced interoperability protocols, and robust security measures to support the ongoing advancement of intelligent smart contracts.
Many of the new quantum-resistant digital signature algorithms being evaluated and standardized by NIST rely on the Fiat-Shamir transform for security. This transformation takes a zero-knowledge (or negligible-knowledge) proof of identity and a hash function, and produces an algorithm for a digital signature. Luckily, none of these concepts are very hard to explain! I will give quick introductions to zero-knowledge proofs and hash functions, followed by two examples of digital signature algorithms based on Fiat-Shamir which you could use in an undergraduate classroom. The first one involves a lot of audience participation, and gives opportunities for drawing and decoration if you are so inclined. The second has been fairly well-known since the early days of public-key cryptography, and is likely to at least remind you of systems you have seen before. We wrap up with an overview of how Fiat-Shamir is used in post-quantum cryptography and in blockchains.
This paper proposes a dynamic risk assessment model for cross-border e-commerce data that integrates genetic algorithms with zero-knowledge proofs. The study first constructs a dynamic evaluation framework based on multidimensional feature extraction and employs an improved genetic algorithm featuring an adaptive crossover and mutation operator to efficiently optimize risk assessment parameters. Subsequently, a lightweight zero-knowledge proof protocol is designed to verify data integrity and the trustworthiness of the risk evaluation logic, while ensuring the confidentiality of the original data. Experimental results on a simulated cross-border e-commerce dataset demonstrate that the proposed model achieves 92.7% accuracy on a simulated dataset, surpassing traditional SVM and Random Forest by 15.3% and 7.5%, respectively. Additionally, the proof generation time is maintained at the millisecond level, significantly outperforming existing homomorphic encryption schemes. This research offers an innovative solution to the “security-efficiency-compliance” trilemma in cross-border e-commerce data flows and holds substantial practical significance for constructing a trusted data element circulation system.
Abdul Muheet Chowdhary, Kuldeep Sharma, Kolawole Omole
Abstract The growth of crypto assets-related transactions is not short of phenomenal. One of the concerns of governments is their potential impact on financial and economic stability arising from illicit financial flows and reduced visibility of tax administration over crypto transactions. The United Nations Tax Committee is developing a toolkit for jurisdictions to be able to evaluate crypto asset risks. The chapter seeks to contribute to the development of this toolkit by analysing the challenges posed by the decentralized nature of crypto assets as well as the use of cryptocurrency as a medium of exchange. A survey is suggested that could be employed by governments to evaluate those challenges. The adoption of blockchain technology is continuously evolving, resulting in the formation of Decentralized Autonomous Organizations (DAOs) and Decentralized Finance (DeFi). The chapter discusses their features at length and suggests another survey to evaluate their risks as well as measures which tax administrations may consider to tackle the tax challenges arising from crypto assets, DAOs and DeFi.
PURCAREA LIVIU, RADULESCU CARMEN VALENTINA, Mănescu Andreea Maria
Blockchain technology has rapidly transformed the way decentralized systems operate, offering new possibilities for transparency, security, and autonomy. However, these benefits come with a notable drawback: the significant environmental cost associated with blockchain consensus mechanisms — particularly Proof of Work (PoW) Möser et all (2021). This paper examines the environmental impact of blockchain and investigates more sustainable alternatives, such as Proof of Stake (PoS) and other energy-efficient models. Using Ethereum’s transition from PoW to PoS as a central case study, along with examples such as Algorand and Chia, we explore how different architectural decisions affect energy consumption. Our analysis, based on recent academic research and technical data, suggests that sustainable blockchain models are technically viable—but their success depends on broader systemic changes, including clear regulations, governance reforms, and industry-wide engagement. In conclusion, blockchain can evolve into a sustainable technology, but only through a responsible and coordinated effort
Luigi Coppolino, Giovanni Maria Cristiano, Salvatore D’Antonio, Jonah Giglio · 6 authors
Ensuring the reliability and accuracy of information is a critical challenge in sectors like finance, media, and health. The reliance on centralized verification systems introduces risks of bias, manipulation, and limited transparency. To address these issues, we propose VeriNet , a decentralized framework for third-party content verification leveraging blockchain technology and the Ethereum Attestation Service. VeriNet integrates on-chain and off-chain attestations to ensure privacy, transparency, and accountability, supported by a Decentralized Data Warehouse and cryptographic Proof-of-SQL mechanisms. The framework includes Contributors , who submit content, and Verifiers , who assess its authenticity. We carried out two Proof-of-Concept implementations, namely in deepfake detection and fintech credit scoring, to demonstrate the efficiency of VeriNet and its adaptability to diverse domains. Moreover, we conducted an experimental evaluation focusing on various parameters, such as costs and execution time, to demonstrate framework feasibility, scalability, and potential to establish a trusted ecosystem for collaborative verification.
The rapid co-evolution of Artificial Intelligence (AI) and blockchain technology has exposed a persistent gap between intelligence-the ability to extract insight from data-and trust-the assurance that data, models, and decisions are transparent, verifiable, and tamper-proof.This study introduces the Unified Trust-Intelligence Framework (UTIF), an end-to-end architecture that natively fuses AI and distributed-ledger technologies to deliver auditable, privacy-preserving, and energy-aware intelligent services.A systematic review compliant with PRISMA guidelines (167 peer--reviewed sources, 2018-2024) reveals four critical deficiencies in the current literature: (i) the lack of formal on-chain model certification, (ii) opaque immutability of operational logs, (iii) limited cross-chain and cross-domain interoperability, and (iv) sub--optimal energy footprints.UTIF addresses these gaps through: On-chain algorithmic certification that fingerprints model weights and training metadata via cryptographic hashing.Federated data governance that combines privacy-preserving federated learning with zero-knowledge proofs (ZK-SNARKs) for regulatory compliance (GDPR, EU AI Act).An AI-assisted hybrid PoS-BFT consensus that dynamically tunes fault-tolerance parameters under varying network conditions.A self-verifiable MLOps pipeline deployed on Hyperledger Fabric with Layer-2 rollups, providing continuous integration, delivery, and audit trails.Experimental validation uses two open-access benchmarks-MIMIC-IV (clinical) and ECB-SDW (financial)-executed on a 20-node heterogeneous testbed.UTIF reduces transaction latency by 38 % and operational energy consumption by 27 % compared with Fabric 2.x and PoA baselines, while enhancing adversarial ro-bustness (F1 + 12 %) through on-chain model attestation.A perception survey of 46 domain experts reports a statistically significant boost in trustability (+1.27 0.31 on a 5-point Likert scale, p < 0.01).Stress tests show 98 % valid throughput under Sybil scenarios with 1,000 malicious nodes, maintaining a carbon footprint below 0.25 kg CO e per 1,000 transactions.The findings demonstrate that deep, native convergence of AI and blockchain can simultaneously achieve measurable trust guarantees, competitive performance, and sustainability.The article concludes with regulatory implications, identified limitations (network scale, oracle dependencies), and a research roadmap toward edge-to--cloud, 6G-ready, Web3-compliant intelligent infrastructures.
The unique International Mobile Equipment Identity (IMEI) number is essential for identifying mobile devices and blacklisting stolen ones within networks. Current solutions are limited to local blacklists and lack a global mechanism for information exchange among operators. Efforts by the Global System for Mobile Communications Association (GSMA) to implement a common blacklist have been constrained by costs, resulting in fragmented and ineffective IMEI management systems. To address these challenges, we have developed a blockchain-based framework that uses the decentralized consensus and tamper-proof nature of distributed ledger technology to enable a unified and globally accessible IMEI blacklist. The framework is implemented on a permissioned blockchain deployed on the Sepolia testnet, utilizing the Proof of Authority (PoA) consensus mechanism to ensure fast and secure validation in a multi-stakeholder environment. Our solution includes a Decentralized Application (DApp) for user interaction, with smart contracts deployed using a Web3 wallet and connected via the Alchemy API to enable efficient communication between the front end and blockchain. Smart contracts automate device status verification, theft reporting, and transaction recording, enhancing transparency, accountability, and security in mobile device management. To validate IMEI numbers, the system uses the Luhn algorithm, a widely accepted checksum method. The framework also collaborates with law enforcement and insurance companies to improve theft verification and claims processing. Experimental results demonstrate the framework's scalability, achieving low latency of under 1 second at transaction rates up to 1,000 TPS and reducing transaction processing time by 30% compared to a traditional centralized database-based system. Performance outcomes were validated through 30 independent test runs to account for variability, underscoring the framework's robustness and potential for widespread adoption. These results set a new standard for global mobile device security through industry-wide collaboration.
Danqi Chen, Yicheng Wang, Ahmed Muneeb Mehta, Muhammad Asif · 6 authors
Decentralization has become a central theme in debates on economic growth, governance effectiveness, and sustainable development. This study undertakes a bibliometric analysis of 1,322 articles published between 2005 and 2024, retrieved from the Web of Science (WoS) and Scopus databases. Using VOSviewer and Bibliometrix (R-package), the analysis maps research productivity, collaboration networks, and thematic clusters. The results reveal two dominant clusters: fiscal decentralization and its socio-economic and environmental impacts and determinants of decentralization and economic growth . High-ranked journals such as Sustainability and Environmental Science and Pollution Research emerge as leading outlets, while the most productive countries include the China and United States . The findings highlight underexplored areas such as decentralization’s role in renewable energy transitions and green finance. Framed within fiscal federalism and endogenous growth theory, this study contributes by identifying emerging trends, regional gaps, and policy implications for designing decentralization strategies that foster inclusive and sustainable economic growth. However, as a bibliometric study, it is limited in capturing case-specific or contextual details, which could be addressed in future qualitative or mixed-method research.
Love Allen Chijioke Ahakonye, Cosmas Ifeanyi Nwakanma, Jae Min Lee, Dong‐Seong Kim
The growing complexity of distributed industrial IoT systems heightens cybersecurity risks, exposing the limitations of centralized ML-based intrusion detection. Federated Learning (FL) enables decentralized, privacy-preserving model training but remains susceptible to adversarial threats and system-level failures. This study introduces PureChain, a decentralized ledger using a proof-of-authority and association (PoA 2 ) consensus mechanism to enhance FL-based IDS security. The study offers insight into the mathematical model of the PureChain-enhanced FL, which integrates blockchain-inspired consensus protocols for collaborative intrusion detection across organizations, ensuring data privacy while providing tamper-proof logs and automated responses through smart contracts. It incorporates dynamic fault tolerance, poisoning resistance, and privacy preservation with FL, enhancing security and performance in decentralized systems. Experimentation with varying client subsets demonstrates its adaptability with a TPS range of 312 . 5 − 1178 . 3 and a low latency range of 0 . 0008484 − 0 . 0032 . The framework ensures comprehensive security, reliability, and privacy, providing a scalable solution for decentralized, secure systems.
Background and Research Aims Community Resource Management Areas (CREMAs) are Ghana’s decentralized approach to biodiversity conservation and sustainable natural resource governance. Despite their institutional legality and community support, CREMAs face persistent financial instability, largely due to donor dependency and the absence of sustainable internal funding mechanisms. In contrast, Village Savings and Loan Associations (VSLAs) have emerged as resilient, community-driven financial systems that promote local livelihoods. This study asks: Can communities sustainably finance their own conservation through grassroots mechanisms like VSLAs? Grounded in Community-Based Natural Resource Management (CBNRM), collective action theory, and informal rural finance systems, the aim is to explore the feasibility of integrating VSLAs into CREMA governance as a model for localized conservation finance. Methods A qualitative case study was employed across four CREMAs in Ghana’s Black Volta River and Western Wildlife corridors. Sixteen focus group discussions were conducted with CREMA executive members, community leaders, VSLA participants, and women and youth groups. Thematic analysis identified perceptions, challenges, and opportunities for financing CREMA activities through VSLAs. Results Findings indicate strong community acceptance of CREMAs, but significant underfunding limits their conservation impact. Conversely, VSLAs were described as trusted, inclusive, and capable of supporting household and community needs. Participants advocated integrating VSLAs into CREMA governance with transparency safeguards and shared control mechanisms. Women and youth, central to VSLA operations, were identified as key stakeholders for advancing inclusive conservation finance. Conclusion Integrating VSLAs into CREMA structures presents a promising model for bottom-up, sustainable financing of conservation activities in Ghana. It builds on existing community trust systems, enhances participation, and reduces reliance on external donors. Implications for Conservation This study contributes a novel community-based green financing framework that links informal rural finance with decentralized conservation governance. It offers replicable insights for scaling localized conservation finance in other resource-dependent, tropical contexts across Sub-Saharan Africa.
Open access
Conservation, Biodiversity, and Resource Management
With the advent of Web3, there has been an unprecedented number of smart contracts being executed on decentralized networks like Bitcoin and Ethereum. While recent studies offer vulnerability detection frameworks and mitigation recommendations, little research addresses the pipeline from vulnerability detection to meaningful patch generation. We introduce VulnPatch, a generalized smart contract auditing and patching framework that employs multiple, fine-tuned LLM agents with distinct roles: detecting vulnerable functions, explaining their causes, and generating patched code. We have demonstrated empirically that having these sub-tasks delegated to different agents significantly improves performance across these sub-tasks rather than having one single agent that will handle all the tasks together. VulnPatch operates in three stages: first, fine-tuning the LLM on vulnerable and safe Solidity contracts; second, pinpointing vulnerability causes; and third, producing a report detailing vulnerable code and corresponding patches. Compared to state-of-the-art LLMs, VulnPatch achieves $\mathbf{9 3. 3 \%}$ accuracy and F1 score, generating reports for 84.44% of vulnerabilities, with explanations and patches showing $90 \%$ sound reasoning and $84 \%$ patch validity.
This paper addresses the critical systemic risk of AI Safety Arbitrage, where users exploit inconsistent safety standards across jurisdictions to access restricted capabilities. Through a controlled red-team test, we demonstrate how current frameworks fail to prevent the extraction of hazardous procedural knowledge, leaving these failures unreported and without legal consequence. To resolve this, we propose a Global Socio-Technical Architecture for AI Accountability based on distributed ledger technology (DLT). This infrastructure creates a protocol network that is conceptually similar to TCP/IP but for accountability designed to align incentives through transparency and cryptographic verification. Key Contributions & ArchitectureThe proposed solution rests on four pillars designed to replace trust relationships with cryptographic verification: Globally Unique Model Registration: Establishes digital identities (DIDs) for AI systems with value chain provenance. Independent Auditor Certification: Licensed validators stake economic value on certification accuracy, removing the need to trust model provider claims. Hardware-Backed Attestation: Tamper-resistant verification ensures deployed systems adhere to registered specifications. Continuous Reputation Monitoring: Oracle networks provide ongoing assessment of compliance with automated penalties for fraud. Technical & Governance Implementation Zero-Knowledge Proofs (ZKP): We illustrate technical viability using zkEVM technology. This allows auditors to prove compliance with safety standards without revealing proprietary training data or model architectures, resolving the tension between accountability and Intellectual Property protection. The AIAO Framework: Inspired by the International Civil Aviation Organization (ICAO), we propose the AI Accountability Coordination Organization (AIAO). This body defines "red-line" safety primitives that nations voluntarily adopt, allowing for regulatory sovereignty while ensuring global interoperability. ConclusionBy breaking the "regulatory arbitrage cycle," this framework enables a transition from safety theater to verifiable safety. It supports open-source innovation through graduated oversight and reputation systems, ensuring that AI development remains both agile and accountable.