(scroll down for English) Die GAIA Ökonomie – Kurze Gesamtdarstellung Falls Sie die PDF Dateien lesen, beginnen Sie bitte mit "Das Gaia System Buch". Das Buch diagnostiziert zunächst die strukturellen Grenzen des heutigen Geld- und Wirtschaftssystems: Zins- und Zinseszinseffekte verschieben Vermögen automatisch nach oben und erzeugen permanenten Wachstums- und Rationalisierungsdruck – mit sozialen, ökologischen und psychologischen Folgekosten. Reformschnipsel lindern Symptome, ändern aber nicht die Logik des Systems (Wachstumszwang, Hortungsanreiz). Eine anschauliche Metapher des „unsichtbaren Staubsaugers“ illustriert, wie Zinsmechaniken Jahr für Jahr Kaufkraft von vielen zu wenigen absaugen. Als Antwort entwirft GAIA eine neue ökonomische Infrastruktur aus zwei bewusst getrennten Modulen: (1) GAIA Coin als neutrales, nicht-spekulatives Zahlungssystem und (2) ein Impact-Modul als freiwillige Incentive-Schicht. Der Coin verankert einen leichten Umlaufdruck (Demurrage) im Code, damit Geld im Fluss bleibt und Horten unattraktiv wird („Fließen statt Horten“). Er dient nur als Tauschmittel (kein Vermögensvehikel), zielt auf Vermeidung von Vermögenskonzentration und entkoppelt Geld von Machtakkumulation. Technisch setzt das Zahlungssystem auf Dezentralität, Energieeffizienz und Datenschutz: pseudonyme Wallets und Zero-Knowledge-Proofs ermöglichen gültige Transaktionen ohne Offenlegung persönlicher Details; leichte Clients und energiearme Konsensverfahren sichern Alltagstauglichkeit; Open-Source-Code und Supermehrheit für Updates verhindern Machtmissbrauch. Zahlungen erfolgen in Sekunden, auch offline per QR/NFC möglich. Das Impact-Modul macht gesellschaftlich nützliche Wirkungen sichtbar und belohnbar – ohne Zwang und ohne Strafen. Es bewertet Beiträge zu Fürsorge, Bildung, Kultur, Ökologie oder Gemeinschaft mit Impact-Punkten, die den individuellen Coin-Wert leicht erhöhen. Die Bewertung kombiniert drei Säulen: Impact Smart Contracts (regelgebundene, transparente Prüfregeln), einen dezentralen Vertrauensgraphen aus validierenden Instanzen (z. B. NGOs, Universitäten, Bürgergremien) mit Reputation, sowie KI-gestützte Datenanalysen zur Mustererkennung und Manipulationsabwehr; zusätzlich schützen ZK-Nachweise die Privatsphäre. Für Governance und Schutz sorgen Gemeinwohlräte als dezentral organisierte, rotierende Kontrollinstanzen mit transparenter Arbeitsweise und Whistleblower-Schutz. Die Mitglieder erhalten eine anspruchsvolle Ausbildung (Rechtsstaat, Urteilsfähigkeit, Psychologie inkl. Gruppendynamik/Narzissmus/Trauma, technische GAIA-Grundlagen); Inhalte werden durch unabhängige Institutionen wie DGVT oder Alfred-Adler-Institut qualitätsgesichert. Grundlegende Systemänderungen bedürfen stets einer überwältigenden Supermehrheit. Die Einführung erfolgt politisch evolutionär statt revolutionär: Start in lokalen Pilotkreisen (Kommunen, Bürgerhaushalte, Genossenschaften), anschließende kommunale und regionale Integration; das bestehende Währungssystem bleibt parallel bestehen. GAIA wird als ergänzende Infrastruktur positioniert – anschlussfähig für unterschiedliche politische Lager – und perspektivisch durch klare Rechtsrahmen abgesichert (bis hin zur internationalen Non-Profit-Struktur bzw. zum öffentlichen Gut). Ein Schwerpunkt liegt auf Demografie: GAIA adressiert die Ursachen extremer Geburtenraten (Unsicherheit, fehlende Absicherung, Vereinbarkeitsprobleme) statt Symptome. Durch soziale Stabilität, Sichtbarmachung von Fürsorge und Förderung von Bildung/Familienplanung entsteht ein Balance-Effekt – in Regionen mit hoher Geburtenrate sinkt diese freiwillig; in alternden Gesellschaften wird Familiengründung wieder attraktiver. Konkrete Regionalkapitel (Afrika, Europa, Asien) illustrieren Wirkpfade und Folgewirkungen (Ressourcenschonung, weniger Not-Migration, sozialer Frieden). In Summe bietet das Buch einen juristisch-technischen und psychologischen Gesamtentwurf samt Anhang (Glossar, technische Spezifikationen, juristische Rahmenentwürfe) und einen praxisnahen Realisierungsplan für ein sofort pilotierbares System. Die Vision gilt als realistisch, weil GAIA auf Anreize statt Zwang setzt, technisch sicher und dezentral ist und von unten wachsen kann – mit spürbaren Vorteilen: mehr soziale Sicherheit, weniger Ungleichheit, wirksamer Umweltschutz. ______________________ VorwortWir sind sterbliche Zeugen einer verletzten Erde – und zugleich Träger einer unverletzlichen Würde. GAIA entsteht aus dieser Spannung: aus der Würde jedes Menschen und aus der Einsicht, dass Heilung dort geschieht, wo wir einander nicht verurteilen, sondern verstehen. Der Mensch ist im Innersten gut. Was wir „Böses“ nennen, wächst aus feindseligen Umfeldern, aus frühen und späten Traumata, aus unbewussten Abwehrmechanismen, die sich als irrationales Handeln zeigen. Darum fragt GAIA: Woher kommt jemand? In welchem Umfeld hat er gelernt zu denken? Wo liegen Brüche und Abspaltungen? Und wie führen wir Schritt für Schritt zu Gesundheit, Einsicht und Würde zurück? Diese Haltung ist kein Dekor, sondern das Fundament: geprägt von meinem juristischen Studium – mit seinem Sinn für Klarheit, Logik, Rechtsphilosophie und Verantwortung – und von den Einsichten Eugen Drewermanns: Menschen kann man nur durch Liebe heilen. Kein Mensch verliert seine unverletzliche Würde. Ein System, das diesem Menschenbild gerecht werden will, muss Strukturen schaffen, die Zugehörigkeit ermöglichen, Angst reduzieren und die innere Güte aktivieren – nicht durch Beschämung, sondern durch Verstehen und Verlässlichkeit. Ich veröffentliche die folgenden Texte bewusst früh und unpoliert, als fortlaufenden Dialog aus Fragen und Antworten zwischen mir und ChatGPT. Nicht, weil Form unwichtig wäre, sondern weil die Zeit drängt. Es geht nicht nur um fortschreitende Umweltzerstörung. Die Schere zwischen Arm und Reich öffnet sich zunehmend – mit exponientieller Dynamik. Menschen arbeiten immer mehr für immer weniger reale Kaufkraft. Öffentliches Eigentum wird privatisiert und verscherbelt. Staaten verschulden sich, geraten in Abhängigkeiten und tun, was Finanzakteure ihnen diktieren; Politik wirkt entmachtet, Wahlen scheinen kaum noch Kurswechsel zu bewirken. Gleichzeitig entstehen Konflikte – bis hin zu Kriegen – um Ressourcen und um wirtschaftliche Vorherrschaft. In dieser Lage ist Abwarten keine Option. Ich stehe öffentlich dazu, dass hier künstliche Intelligenz mitgewirkt hat; die Verantwortung für Auswahl, Bewertung und Veröffentlichung trage ich. Diese Rohform ist Absicht: Sie macht Herleitungen sichtbar, dokumentiert Entscheidungen und erlaubt, den Denkweg nachzuvollziehen. Redaktionelle Glättungen können später folgen – vorrangig ist die Umsetzung. GAIA ist ausdrücklich kein Projekt des Klassenkampfes. Es will die Gefühle, Anreize und Motivationen aller berücksichtigen – der Reichen, des Mittelstands und der Armen. Ziel ist Ausgleich statt Frontbildung, Würde, Sicherheit und Sinn für alle. Ich bin überzeugt, dass das möglich ist. Ein zentraler Mechanismus ist dabei die Demurrage (Umlaufsicherungsgebühr): Dadurch muss Geld für das Gemeinwohl nicht erst über Steuern „zurückgeholt“ werden, sondern es entsteht ein kontinuierlicher Finanzstrom, der unmittelbar ins Gemeinwohl fließen oder gemeinwohlfördernde Investitionen gezielt subventionieren kann. Technisch gilt: Jeder Nutzer entrichtet automatisch eine kleine Umlaufgebühr (z. B. 0,5 % pro Monat). Diese Beträge werden nicht vernichtet, sondern in einem Treasury (Schatzkonto) des GAIA-Coin-Moduls gesammelt. Das Treasury liegt bewusst im Coin-Modul, damit der Geldkreislauf sauber bleibt und jederzeit exakt nachvollziehbar ist, wieviel Demurrage eingenommen wurde. Aus diesem Topf werden allgemeine Ausgaben (Betrieb, Sicherheit, Technik) gedeckt und gemeinwohlförderliche Maßnahmen finanziert; das Impact-Modul entscheidet lediglich über die Vergabe aus diesem Treasury – einschließlich möglicher Demurrage-Rabatte – nach transparenten Gemeinwohl-Kriterien. Kurz: Demurrage-Einnahmen fließen ins Treasury des Coin-Moduls, das Impact-Modul verteilt daraus wirksam und nachvollziehbar. Ich weiß nicht, ob dieses System in seiner heutigen Form vollständig funktionieren wird. Wenn nicht, ändern wir es, bis es trägt. Und falls Teile scheitern, bin ich mir sicher: In diesen Ideen liegt genügend Inspiration und Struktur, um gemeinsam die Version zu finden, die funktioniert. GAIA ist als lernendes System gedacht – Fehler sind änderbare Daten. Statt Perfektion auf Papier beginnt nun die Programmierung: GAIA Coin und Impact-Modul, iterativ, testbar, offen. Bauen, prüfen, verbessern – jetzt. Wer mitgehen will, ist eingeladen; wer zweifelt, kann uns beim Beweisen zusehen. GAIA ist kein Schaukasten, sondern eine Baustelle für Gemeinwohl, Gerechtigkeit und Umweltschutz. Helme auf. An die Arbeit.E-Mail: info@dzydent.com _____________________ Executive Summary des Manuskripts „Das GAIA-System – Gesamtdarstellung“ (für Entscheider in Politik, Verwaltung, Technik und Stiftungen): Ausgangslage & ZielDas Buch reagiert auf strukturelle Fehlanreize des bestehenden Geldsystems (Horten, Vermögenskonzentration, Wachstumsdruck) und skizziert eine praxistaugliche, rechts- und techniknahe Alternative: GAIA als ergänzende Infrastruktur, die freiwillig parallel läuft und ohne Systembruch eingeführt werden kann. Kernlösung in zwei Modulen GAIA Coin (Zahlungsmittel): nicht-spekulativ, mit leichtem Umlaufdruck (Demurrage) im Protokoll verankert, um Geldfluss zu sichern und Hortung unattraktiv zu machen. Architektur: energiearme Konsensverfahren, pseudonyme Wallets, Zero-Knowledge-Proofs (ZKPs) für Datenschutz, offene Codebasis, Supermehrheit + Timelock für Änderungen. Zahlungen in Sekunden; Offline-Weitergabe per QR/NFC möglich. Impact-Modul (opt-in): freiwillige Wirkungsschicht, die gemeinwohlfördernde Handlungen (z. B. Fürsorge, Bildung, Kultur, Umweltschutz) erfasst und mit Impact-Punkten belohnt; keine Überwachung, keine Ideologie, s
Open access
Earth Systems and Cosmic Evolution
Corporate Social Responsibility and Sustainability
Eduardo Sardenberg Tavares, Antonio José G. Busson, Sérgio Colcher
Smart contracts are fundamental to blockchain ecosystems, but remain susceptible to security vulnerabilities that can lead to severe financial losses. Recent advances in agentic AI systems, powered by large language models (LLMs), enable autonomous code analysis and decision-making without explicit task-specific supervision. These systems leverage prompt engineering and zero-shot reasoning to detect vulnerabilities in smart contracts without prior fine-tuning. In this work, we evaluate the effectiveness of agentic LLM-based approaches in identifying vulnerabilities using prompt engineering and zero-shot reasoning across a curated dataset of Solidity smart contracts. Our findings highlight the limitations of current LLMs in automated vulnerability detection, providing insights into their practical applicability for securing decentralized applications. Our best-performing configuration, which integrates zero-shot reasoning with the Tree of Thoughts framework, achieved an F1-score of 73.66%.
Mr. DEVENDAR, Nandi J. Reddy, B.Sahasra, T.Srileka
Artificial intelligence and the quick development of photograph editing software in latest years have made it very simple to regulate virtual pix covertly. The authenticity and dependability of digital media utilized in social networks, journalism, and criminal proof have come below scrutiny because of manipulations like copy-circulate forgery and deepfake creation. The aim of this work is to perceive photograph forgeries via combining deep gaining knowledge of-based class techniques with traditional feature extraction methods.The cautioned device extracts precise neighborhood functions from input images the usage of the oriented speedy and turned around brief (ORB) algorithm. For powerful feature matching, 2-Nearest Neighbor (2NN) and Hierarchical Agglomerative Clustering (HAC) are then used. A Convolutional Neural community (CNN) model is trained to distinguish among authentic and manipulated photos by means of figuring out pixel-degree irregularities and texture changes if you want to growth type accuracy. examined on the publicly reachable MICC-F220 and MICC-F2000 datasets, the device outperforms baseline SVM strategies with a ninety% detection accuracy and a zero.1 false tremendous charge
Open access
Digital Media Forensic Detection
Generative Adversarial Networks and Image Synthesis
Decentralized finance protocols are frequently exploited, creating a demand for fast and reliable repair of vulnerable smart contracts and validation that reflects runtime security. Large language models are an emerging source of patches, yet many evaluations rely on manual checks or self-assessment, which cannot confirm whether attacker profit is actually prevented. We introduce an executable benchmark that replays verified real-world exploits against patched Solidity contracts under a resilient protocol that permits alternate attack paths and controlled state variation. Our framework compiles candidate patches, deploys them on a forked chain, and tests whether the exploit still yields profit. The benchmark covers six test cases drawn from reproducible incidents and is released as open-source. Among the nine evaluated models, GPT-5, GPT-4.1, and Claude Opus 4.1 performed the best, mitigating four of six test cases. Microsoft Phi-4 was the most reliable open-source model, mitigating two of six exploits and producing compilable patches for the remaining cases. No model mitigated the H2O case once resilient checks were enabled, while a simpler access control flaw, BTNFT, was often repaired with minimal edits. Grounding validation in executable exploit replay provides a precise and scalable method to measure whether proposed repairs harden contracts at runtime.
The convergence of quantum physics and machine learning presents unprecedented opportunities for developing ultra-secure authentication systems. This comprehensive paper investigates the integration of quantum random number generators (QRNGs) with advanced machine learning architectures, including quantum neural networks (QNNs), long short-term memory (LSTM) networks, and hybrid quantumclassical models, to establish authentication mechanisms with information-theoretic security guarantees. We provide rigorous theoretical foundations spanning quantum entropy theory, min-entropy estimation, and randomness certification, complemented by detailed analyses of contemporary QRNG hardware implementations including photonic integrated circuits achieving generation rates exceeding 20 Gbps. The paper explores deep learning architectures for biometric authentication, demonstrating how QNN-enhanced systems achieve superior performance through quantum superposition and entanglement. Furthermore, we examine the application of quantum entropy sources in zero-knowledge proof protocols, particularly zk-SNARKs and zk-STARKs, addressing post-quantum security concerns. Through comprehensive mathematical formulations, algorithmic implementations, and security analyses, we establish that hybrid quantum-classical authentication systems combining QRNG-derived cryptographic keys with ML-based behavioral authentication provide provably secure, practical solutions for next-generation cybersecurity applications. Experimental results from current quantum hardware platforms validate theoretical predictions and demonstrate real-world applicability.
Open access
Chaos-based Image/Signal Encryption
Physical Unclonable Functions (PUFs) and Hardware Security
Smart contracts, as self-executing code on blockchain platforms, are transforming digital agreements across multiple industries. This paper reviews the technical foundations, applications, security challenges, and emerging directions of smart contract technology through an analysis of recent academic literature and real-world implementations. While smart contracts demonstrate significant potential in decentralized finance, supply chain management, and healthcare, they face critical challenges, including security vulnerabilities, ecosystem centralization risks, and legal uncertainties. Layer-2 scaling solutions, cross-chain interoperability protocols, and AI-assisted security auditing represent promising directions for addressing these challenges. Our analysis reveals that despite technological advances, fundamental issues in security verification and regulatory frameworks require continued research attention.
Mohammad Nasrinasrabadi, Maryam A. Hejazi, Arefeh Jaberi, Hamed Hashemi‐Dezaki · 5 authors
Cryptocurrencies utilize blockchain technology to ensure transparency, decentralization, and immutability in financial transactions. It is expected that blockchain applications will significantly impact renewable energy markets. However, there is a lack of studies addressing the energy requirements of digital currencies. This research proposes optimizing a hybrid energy system consisting of distributed renewable and non-renewable energy sources, focusing on cryptocurrency mining. Although previous studies have not yet addressed energy system optimization considering cryptocurrency mining farms, the increasing prominence of such farms highlights the growing need for research in this area. The primary renewable sources in the proposed hybrid system include photovoltaic (PV) panels and wind turbines. We employ diesel generators as backup systems to compensate for the intermittent nature of solar and wind energy production. Besides meeting the demands of urban loads, cryptocurrency mining devices will be considered a major energy consumer. In this article, the optimal configuration of the energy system will be determined based on technical and economic indicators. Additionally, economic evaluations will be conducted to assess the income generated from cryptocurrency mining farms, and appropriate approaches will be identified from both technical and financial perspectives, focusing on return on investment (ROI).
This study examines the intricate relationships between cryptocurrency and various uncertainties related to economic policy and global risk factors. It explores the interactions between cryptocurrency and global risk factors, comparing these with their relationships to different measures of economic policy uncertainty (EPU). We find that cryptocurrency returns are more sensitive to global risk factors than to the country-level EPU. Notably, gold exhibits bidirectional causality with cryptocurrency in returns and volatility. The research sheds light on the dynamic interactions within cryptocurrency markets, underscoring the importance of continuous monitoring and adaptive strategies to navigate the evolving financial landscape of the digital ecosystem.
This study explores how integrating cryptocurrencies into traditional financial portfolios can influence investment performance. Focusing on Bitcoin and Ethereum alongside key European stock indices (BUX, DAX, and FTSE), the analysis examines whether blockchain-based assets can enhance diversification and improve the balance between risk and return. Using weekly market data from 2019 to 2023, the research applies Markowitz mean–variance optimization to identify optimal asset allocations under different objectives such as maximizing the Sharpe ratio, minimizing risk, and maximizing returns. The findings reveal that cryptocurrencies show weak correlations with European stock indices, suggesting meaningful diversification potential. When included in portfolios, Bitcoin and Ethereum can significantly boost returns, though they also increase volatility. Portfolios optimized for risk reduction favored traditional indices, while those targeting higher returns relied predominantly on cryptocurrencies. Overall, combining digital and conventional assets produced a more balanced performance, with the Sharpe-ratio–maximized portfolio demonstrating the best trade‐off between stability and profitability. These results indicate that cryptocurrencies can play a valuable complementary role in modern portfolio construction. They are most suitable for investors willing to accept higher risk in exchange for potentially greater rewards, while more risk‐averse investors may benefit from maintaining a stronger focus on traditional equity indices. The study contributes to understanding how blockchain‐driven assets can expand financial opportunities and supports a broader view of diversification in contemporary investment strategies.
Crowdfunding websites tend to have centralized escrow infrastructure, which can create concerns over the lack of transparency, security threats, and fraud vulnerability. The proposed system, a hybrid blockchain–AI architecture, combines Ethereum-based smart contracts and machine learning-based fraud detection to result in a decentralized and transparent crowdfunding space. The blockchain layer ensures accountability with controlled release of funds based on milestones, limiting the tendency to spend funds more due to the cryptocurrency nature with the AI module detecting fraudulent activity based on analysis of textual, transactional, temporal, and reputation data. Experimentation proves that the proposed system promotes higher trust, reduces transaction cost, and signifies a robust fraud detection model compared to conventional crowdfunding models. The results suggest creating a combination of the immutability of blockchains with the analytical power of AI as a potential route to safer and more effective decentralized finance apps.
Reddy P. Santosh, B. Rohith, B. S. Abhiram, Sujay G. Kaushik
The chain of custody (CoC) process in legal and forensic asset management requires a secure, transparent, and tamper-proof system to maintain evidence integrity. Traditional CoC methods, relying on centralised databases and manual documentation, are prone to manipulation, inefficiencies, and unauthorised access, compromising legal proceedings. This paper presents a blockchain-based CoC framework leveraging decentralised ledger technology (DLT) for immutable, verifiable, and automated evidence management. Smart contracts facilitate secure asset registration, controlled custody transfer, and full traceability, ensuring reliable documentation across each phase of custody. To address scalability challenges and high transaction costs, the system integrates interplanetary file system (IPFS) for decentralised storage and optimises on-chain and off-chain data handling. Secure hashing and zero-knowledge proofs (ZKPs) enhance data integrity, accessibility, and compliance by enabling evidence verification without exposing sensitive data. A case verification mechanism enables judicial authorities to authenticate evidence using blockchain records, while an automated logging and reporting module generates a comprehensive “Consolidated Case Report” detailing FIR data, evidence metadata, and verification statuses. By addressing privacy concerns, storage efficiency, and operational scalability, this framework advances the reliability, security, and transparency in managing evidence, reducing reliance on manual verification and strengthening legal forensics.
Nara Raquel D. Andrade, Oscar William N. de Carvalho, Carlos H. G. Ferreira, Glauber Dias Gonçalves
The market for Non-Fungible Tokens (NFTs) continues to evolve, yet it still lacks robust methodologies to estimate the future value of its assets. Unlike traditional financial markets, NFT pricing is challenged by intangible factors such as the artistic nature of the items and the influence of social and transactional networks among buyers and sellers. This study investigates whether the structural position of participants in the transaction network can serve as a relevant predictor of the future value of NFTs. To this end, we reconstructed the NFT trading network for the period 2020–2021, extracted both structural and transactional metrics of the participants, and applied supervised machine learning models, including deep neural networks. The results demonstrate the feasibility of the proposed approach, achieving 74% accuracy and a global F1-Score of 72%. Interpretability analysis using SHAP values revealed that, in addition to historical price averages, network metrics such as degree and neighborhood significantly contribute to prediction. These findings highlight the role of network dynamics in NFT valuation and point toward promising directions for more transparent and evidence-based pricing methodologies.
Abdullah Jameel Abualhamayl, Mohanad A. Almalki, Firas Al-Doghman, Abdulmajeed A. Alyoubi · 5 authors
Traditional real estate markets are often dominated by institutional investors, which may limit access for small investors due to high entry costs and limited opportunities for fractional participation. Such market dynamics create slow, expensive, and inflexible transaction processes that limit liquidity and prevent broader participation in property ownership. To address these challenges, we propose a blockchain-based framework using fractional non-fungible tokens (F-NFTs) to digitally certify and manage shared real estate ownership. Our approach involves improvements to the ERC-721 token standard to support fractional ownership, development of smart contract algorithms for property registration and transfer, implementation of a prototype, and deployment on the Ethereum Sepolia testnet. The performance evaluation reveals that the complete certification process costs around $47.16 USD and can be completed in roughly 264 seconds, which reflects notable enhancements in transaction efficiency compared to traditional systems. By enabling costeffective and transparent property certification, this framework enhances transparency, democratizes property investment, and broadens market accessibility.
The research explores the features of administrative-territorial reform in Ukraine within the context of European integration and active decentralization processes. The author examines the legislative framework for local self-government reform, including the Concept of Local Self-Government Reform and the Implementation Plan, as well as practical measures aimed at territorial consolidation and strengthening the financial capacity of newly established territorial communities. Special attention is given to improving resource management efficiency, developing municipal services, enhancing the organizational and institutional capacity of local government bodies, and ensuring citizen participation in decision-making at the local level, including expanding practices of direct democracy. The research analyzes the dynamics of local budgets, the growth of capital expenditures, and the level of public support for the reform, demonstrating the effectiveness of the implemented measures. The role of international assistance and inter-municipal cooperation in enhancing community capacity is highlighted, along with the importance of professional training and development of local officials. The research emphasizes the relevance of a comprehensive approach to creating financially autonomous and effective territorial communities, including the development of methodological foundations for assessing their capacity to manage local finances and socio-economic development. This research is valuable for scholars, local government practitioners, and international experts interested in decentralization, administrative-territorial reform, and the improvement of municipal financial sustainability.
Alejandro Peñuelas-Angulo, Claudia Feregrino-Uribe, Morales-Sandoval Miguel
Multi-authority Attribute-based Encryption (MAABE) schemes distribute the responsibility of managing the scheme attributes and attribute keys among several attribute authorities. However, MA-ABE schemes often assume that the attribute authorities are always fully trusted parties and lack authenticity checks. This paper proposes a Ciphertext-Policy MAABE scheme that incorporates a user-authority mutual authentication mechanism exploiting the properties of Zero-Knowledge Proofs. A batch version of the Schnorr protocol validates the authority identity and the possession of claimed attribute keys. The complete scheme is enhanced using asymmetric pairings to improve security and performance. Furthermore, outsourced decryption is considered to exploit the available computing resources under a fog-enabled IoT environment. The proposed scheme's analysis shows the overall construction's efficiency and, particularly, the efficiency and suitability of the mutual verification protocol.
Sangwon Shin, Ngoc-Son Pham, Lei Xu, Weidong Shi · 5 authors
Zero-Knowledge Proof (ZKP) cryptographic algorithms have garnered significant attention for their ability to enhance privacy. However, the practical deployment of these algorithms remains challenging because they demand extremely high computational effort and handle huge volumes of data, especially in the Number Theoretic Transform (NTT) step. In this work, we propose an HBM-aware dataflow that employs sub-tiling and row-shuffling techniques to overcome the nonuniform stride access problem and to maximize HBM bandwidth utilization. We also design the NTT accelerator to use minimal FPGA resources. In particular, we explore diverse design options for the 256-bit modular multiplier and adopt an efficient design that optimizes resource usage and performance. Experimental results demonstrate that the proposed accelerator achieves lower latency and enhanced resource utilization compared to state-of-the-art FPGA-based designs.
Polygon Chain Development Kit (CDK) Validium is a Layer 2 blockchain scaling solution that processes transactions off-chain. It uses Polygon’s distinctive approach to Zero-Knowledge Proofs (ZKPs) implemented within their Zero-Knowledge Ethereum Virtual Machine (zkEVM). A key factor in its successful deployment is robustness, ensuring that users can trust their transactions will be processed accurately and promptly. This research concerns developing robust validation methodologies and comprehensive testing strategies targeting the “double-spending” problem within Polygon CDK Validium. We indicate theoretical scenarios where double-spending vulnerabilities could arise in Polygon CDK Validium by identifying how execution errors can combine with a specific category of flawed constraints to create vulnerabilities. When combined with what we classify as Invalid PIL Constraints For EVM Specification Vulnerabilities (IPCFESV), these errors can trigger problematic behaviours. We further illustrate how erroneous behaviour resulting from IPCFESV can lead to cascading involvement in withdrawal operations resulting in irreversible cross-layer double-spending. We also illustrate how a protocol anti-censorship mechanism bypasses standard validation checks, thereby intensifying reliance on constraint correctness. We then propose ways to determine the correct behaviour. We propose a method to utilise Polygon’s integration testing framework for generating execution traces for de-facto ERC-20 fungible token standard. The outcomes of this study will form the foundational basis for the subsequent development of practical testing and verification methods for Polygon CDK Validium. Implementation and empirical validation remain as future work.
This project develops a blockchain-based web application aimed at verifying pharmaceutical products, a critical step in combating counterfeit drugs. By leveraging Ethereum's Sepolia test network and the power of smart contracts, the system facilitates secure, transparent processes for registering, verifying, and tracking pharmaceutical items. The application combines a PHP-based backend with a JavaScript-powered frontend, seamlessly integrating tools like MetaMask for user authentication and Web3.js to enable blockchain communication. The study underscores the significant advantages blockchain offers over traditional verification methods, particularly in terms of data integrity, transparency, and security. As a result, it meets the CIA triad model's requirements for confidentiality and integrity in information security. The novel features of the project include the use of optimism roll-ups for scalability, two-factor authentication (2FA), and data encryption to address critical challenges often overlooked in similar proposals for blockchainbased authentication systems. The results of the project demonstrate a tangible improvement in supply chain transparency and fraud prevention within the pharmaceutical industry. This work lays a strong foundation for further exploration of decentralised applications, not only in pharmaceutical validation but also across other essential sectors.
A. Marques, Luiz Eduardo Terra de Faria, Diogo S. Mendonça
The exponential growth of the cryptoasset market and the advancement of decentralized technologies have challenged traditional models of tax collection. In particular, self-custody wallets, which allow users to maintain direct control over their digital assets without the mediation of financial institutions, pose significant obstacles to oversight and tax compliance. This paper proposes an approach for the automatic collection of taxes on foreign exchange operations with stablecoins, using smart contracts on decentralized exchanges (DEXs). Through the implementation of a Proof of Concept (PoC), based on the Split Payment logic, we demonstrate the technical feasibility of applying a tax rate, analogous to the IOF, directly during transactions carried out by self-custody wallets, without the need for prior user identification. Experimental results, validated on the Polygon mainnet, reinforce the potential of the proposed model as a practical solution aligned with the principles of Web3, contributing to the debate on automated tax compliance mechanisms in decentralized environments.
Non-fungible tokens (NFTs) are unique digital assets that play an increasingly important role in decentralized markets, supporting new forms of ownership, valuation, and exchange. Their inherently multimodal structure, which encompasses visual content, metadata, and trading history, has led to a growing academic interest in modeling NFT pricing and market behavior. However, existing research is limited by the lack of comprehensive datasets that unify these modalities with consistent formatting and longitudinal coverage. To address this gap, we introduce MultiNFT, a large-scale multimodal dataset comprising 50 curated profile picture (PFP) NFT collections, including 523,020 unique assets and 2.38 million transaction records from April 2021 to September 2025. MultiNFT integrates standardized images, structured metadata, and time-series trading data, along with rarity scores and aesthetic features, offering a unified foundation for multimodal learning and NFT analytics. Unlike prior datasets that focus on visual similarity or static snapshots, MultiNFT captures evolving valuation dynamics across market cycles and connects them to trait-level characteristics. We demonstrate the utility of the dataset through three case studies, including within-collection rarity-price analysis, visual feature clustering across collections, and quantifying feature contributions in a comprehensive pricing model. By bridging computer vision, behavioral modeling, and financial forecasting, MultiNFT supports a wide range of interdisciplinary research and practical use cases. The dataset is publicly available and is intended to promote reproducible experimentation and further exploration of the mechanisms driving value in digital asset ecosystems.