Abstract The rise of decentralized technologies introduces challenges in fairness, efficiency, and scalability within distributed ledger protocols. The Internet of Things Applications (IOTA) Tangle, a directed acyclic graph (DAG)-based structure, addresses these challenges by enabling scalable, feeless transactions for IoT applications. This study presents a novel Partially Observable Markov Decision Process (POMDP)-based Tip Selection Algorithm (TSA) to optimize fairness in the IOTA Tangle. The proposed TSA reduces orphaned transactions to as low as 0.003% and eliminates lazy tip selection under medium network loads. Extensive simulations demonstrate that the POMDP-based TSA confirms up to 107 transactions at optimal lambda values, outperforming existing algorithms like Weighted TSA by 328% in efficiency. This algorithm offers significant scalability, fairness, and adaptability, making it a robust solution for IoT-based decentralized applications. These findings advance DAG-based distributed ledger systems by addressing orphaned transactions and lazy behavior, ensuring secure and efficient operations under diverse network conditions.
The accelerating biodiversity crisis has prompted a paradigm shift in the financial sector, where integrating nature into financial risk assessment is becoming increasingly vital. This study conducts a comprehensive bibliometric analysis to explore the intellectual landscape of biodiversity finance, focusing on how biodiversity is being incorporated into financial theory, investment practices, and sustainability governance. Using the Scopus database and VOSviewer software, the study analyzes co-occurrence networks, temporal trends, density visualizations, and collaboration patterns among authors, institutions, and countries. The findings reveal that “biodiversity,” “finance,” and “sustainable finance” serve as conceptual anchors, while emerging themes such as “decentralized finance,” “green bonds,” and “ESG” indicate growing innovation in the field. The United Kingdom and United States lead global collaborations, with strong linkages to European and Asian institutions. This research contributes theoretically by clarifying the field’s multidimensional evolution and practically by identifying knowledge gaps and strategic entry points for policy, investment, and academic advancement. Limitations include database coverage and lack of qualitative content analysis, suggesting future research directions. Overall, the study underscores the critical role of interdisciplinary collaboration in advancing biodiversity-aligned financial systems.
Open access
Environmental Conservation and Management
Forest Management and Policy
Conservation, Biodiversity, and Resource Management
The article examines the legal mechanism for regulating the circulation of virtual assets in Ukraine and the regulatory and legal support for countering illegal activities with various types of cryptocurrencies. The provisions of the Law of Ukraine “On Virtual Assets”, amendments and additions to civil legislation in terms of introducing the concept of “digital thing” are analyzed. It is proven that the provisions of the European Regulation “Markets in Crypto-Assets” (“MiCA”) are essential for the legal regulation of the circulation of virtual assets and countering illegal activities with them. The classification of virtual assets contained in the European Regulation “MiCA” is disclosed in order to understand the essence of various types of cryptocurrencies. The peculiarities of the circulation of such crypto-assets as Bitcoin, Ethereum are disclosed and noted; the concepts of “blockchain”, “validator”, “service token”, “crypto-asset issuer”, etc. are investigated. The role of a number of state bodies in countering the illegal circulation of virtual assets in Ukraine is highlighted. It is argued that the coordination of analytical work and the detection of risky transactions is provided by the State Financial Monitoring Service of Ukraine. It is substantiated that the detection of criminal schemes and ensuring the prosecution of those guilty of offenses with virtual assets is entrusted to the National Police, the Security Service of Ukraine, the State Bureau of Investigation, the Bureau of Economic Security, and the Prosecutor’s Office. Such bodies as the National Bank of Ukraine, the National Securities and Stock Market Commission, and the Ministry of Digital Transformation of Ukraine form a regulatory framework that should prevent the use of crypto-assets for illegal purposes. It is established that countering the illegal circulation of virtual assets in Ukraine is carried out both through preventive measures, analytical work and improvement of the regulatory and legal framework, and through operational-search and criminal-law jurisdiction. This comprehensive model allows responding to the latest challenges, in particular, the use of decentralized finance, anonymous technologies, and cross-border schemes for the illegal circulation of virtual assets.
This study presents a comprehensive bibliometric analysis of the academic literature surrounding digital wallets and crypto payment systems, two pivotal components of the evolving FinTech landscape. By utilizing data from the Scopus database and visualizing it through VOSviewer, the study maps co-occurrence of keywords, co-authorship networks, institutional collaboration, and country-level partnerships. Findings reveal that blockchain technology serves as the central anchor of research, connecting diverse themes such as smart contracts, authentication, digital assets, and decentralized finance (DeFi). Temporal analyses show a progression from foundational infrastructure studies to more application-driven topics like non-fungible tokens (NFTs) and crypto wallets. Co-authorship and collaboration networks highlight key contributors and regions, with India, the United States, and select European countries leading scholarly production and partnerships. The study provides theoretical contributions by identifying core research clusters and emerging themes, while offering practical implications for regulators, developers, and financial service providers aiming to integrate digital and crypto payment solutions. Limitations include database scope and the inherent constraints of bibliometric methods, suggesting avenues for future mixed-method or qualitative enrichment.
The rapid evolution of financial technology (fintech), including cryptocurrencies and decentralized finance (DeFi), has transformed how consumers and businesses engage with financial services. This chapter examines the drivers of fintech adoption by extending established technology acceptance models, such as technology acceptance models (TAM), unified theory of acceptance and use of technology (UTAUT), and theory of planned behavior (TPB). A systematic review of 80 articles (2017–2023) identifies key factors influencing adoption, including perceived usefulness, ease of use, social influence, and facilitating conditions. Emerging factors, such as financial literacy, hedonic motivation, and trust, are especially important during crises, such as the COVID-19 pandemic. However, gaps remain in understanding how evolving perceptions of security and trust impact sustained adoption, particularly in decentralized environments, such as blockchain networks and crypto assets, where algorithmic transparency replaces institutional intermediaries. This chapter proposes integrating trust, security, and user perceptions into existing models to create a cohesive framework applicable across fintech services. The findings provide actionable insights for researchers and industry stakeholders to enhance user acceptance and guide future innovation.
We present LISA, an agentic smart contract vulnerability detection framework that combines rule-based and logic-based methods to address a broad spectrum of vulnerabilities in smart contracts. LISA leverages data from historical audit reports to learn the detection experience (without model fine-tuning), enabling it to generalize learned patterns to unseen projects and evolving threat profiles. In our evaluation, LISA significantly outperforms both LLM-based approaches and traditional static analysis tools, achieving superior coverage of vulnerability types and higher detection accuracy. Our results suggest that LISA offers a compelling solution for industry: delivering more reliable and comprehensive vulnerability detection while reducing the dependence on manual effort.
Privacy-preserving technologies have introduced a paradigm shift that allows for realizable secure computing in real-world systems. The significant barrier to the practical adoption of these primitives is the computational and communication overhead that is incurred when applied at scale. In this paper, we present an overview of our efforts to bridge the gap between this overhead and practicality for privacy-preserving learning systems using multi-party computation (MPC), zero-knowledge proofs (ZKPs), and fully homomorphic encryption (FHE). Through meticulous hardware/software/algorithm co-design, we show progress towards enabling LLM-scale applications in privacy-preserving settings. We demonstrate the efficacy of our solutions in several contexts, including DNN IP ownership, ethical LLM usage enforcement, and transformer inference.
Pixels and market cycles both move NFT prices. Non-fungible tokens (NFTs) are unique digital assets, often used to represent ownership of digital art, collectibles, and other media, secured on blockchain networks like Ethereum. The rise of NFTs has led to the creation of a multi-billion-dollar market for digital art and collectibles, making it a key area of interest for researchers, artists, and investors. Using 94,039 transactions from 26 major generative Ethereum collections, this study extracts 196 machine-quantified image descriptors - color, composition, palette structure, geometry, texture, and deep-learning embeddings - and applies a three-stage filter to identify stable predictors for hedonic regression. A static mixed-effects model shows that market sentiment and transparent, interpretable image traits have significant and independent pricing power: higher focal saturation, tighter compositional concentration, and greater curvature are rewarded, while clutter, heavy line work, and dispersed palettes are discounted; deep embeddings add limited incremental value once explicit traits are included. To assess state dependence, a Bayesian dynamic mixed-effects panel with cycle effects is estimated, allowing Composition Focus - Saturation - the ratio of saturation in the central region to the whole image, capturing vividness and concentration at the focal area - to vary across market regimes. Collection-level heterogeneity (brand premia) is absorbed by random effects. The time-varying coefficients exhibit clear regime sensitivity, with stronger premia in expansionary phases and weaker or negative loadings in downturns, while the grand-mean effect is small on average. Overall, NFT prices reflect both observable digital product characteristics and market regimes, and the framework offers a cycle-aware tool for asset pricing, platform strategy, and market design in digital art markets.
Proposer anonymity in Proof-of-Stake (PoS) blockchains is a critical concern due to the risk of targeted attacks such as malicious denial-of-service (DoS) and censorship attacks. While several Secret Single Leader Election (SSLE) mechanisms have been proposed to address these threats, their practical impact and trade-offs remain insufficiently explored. In this work, we present a unified experimental framework for evaluating SSLE mechanisms under adversarial conditions, grounded in a simplified yet representative model of Ethereum's PoS consensus layer. The framework includes configurable adversaries capable of launching targeted DoS and censorship attacks, including coordinated strategies that simultaneously compromise groups of validators. We simulate and compare key protection mechanisms - Whisk, and homomorphic sortition. To the best of our knowledge, this is the first comparative study to examine adversarial DoS scenarios involving multiple attackers under diverse protection mechanisms. Our results show that while both designs offer strong protection against targeted DoS attacks on the leader, neither defends effectively against coordinated attacks on validator groups. Moreover, Whisk simplifies a DoS attack by narrowing the target set from all validators to a smaller list of known candidates. Homomorphic sortition, despite its theoretical strength, remains impractical due to the complexity of cryptographic operations over large validator sets.
Purpose-This study explores the critical business implications of Web3 technologies within Türkiye's unique legal landscape, a nation experiencing significant crypto adoption and evolving regulations. By analyzing the architectural shifts from Web1.0 to Web3, we aim to understand how traditional legal frameworks create significant challenges for all stakeholders affected by decentralized environments, not just those operating within them.Design/methodology/approach-This study adopts a multidisciplinary and comparative research design, integrating both technological and legal perspectives to investigate the evolution from Web1.0 to Web3 and the associated legal implications. Given the complexity and scope of the subject matter, a mixed methods approach is employed, combining qualitative content analysis, document analysis, and comparative case study methodologies.Findings-The findings suggest that existing legislation is inadequate and outdated and poses a risk of future legislative actions causing irreversible or difficult-to-remedy harm if current legal gaps remain unaddressed. Discussion-Drawing from real-world case scenarios, the study highlights the urgent need for adaptive legal strategies that align with the decentralized, borderless, and immutable nature of blockchain infrastructures. The findings aim to support business leaders, legal practitioners, and policymakers seeking to innovate responsibly within the emerging Web3 economy.
تتناول الدراسة أبعاد وطبيعة الفجوة المتسعة بين التطور السريع في الابتكار المالي وخاصة صعود التمويل اللامركزي (بذراعيه الأصول والعملات المشفرة ومنصات التداول الرقمية) من جهة، والتطور البطيء في الأطر القانونية والتنظيمية والرؤى الشرعية من جهة أخرى. تركز الدراسة على تحليل بعدين رئيسيين هما: تحليل القضايا المرتبطة بتلك الفجوة والإضاءات الرئيسة لبناء نظام تمويل لا مركزي إسلامي قائم على سلسلة القيم والمبادئ الأخلاقية ومتوافق مع أحكام ومقاصد الشريعة الإسلامية، ومن أبرز القضايا: قضايا الهوية والمشروعية وعلاقة التمويل اللامركزي بنظام التمويل التقليدي، إضافة إلى قضايا الاقتصاد الحقيقي مقابل الاقتصاد المالي. وتبين الدراسة طبيعة القضايا من منظور الاقتصاد الإسلامي، وأبرزها العمل على بناء نظام تمويل (لا مركزي أو مركزي) إسلامي قائم على تمويل أنشطة اقتصادية حقيقية تحقق قيمة مضافة لبناء اقتصاد حقيقي يحقق الاستقرار الاقتصادي والاجتماعي والعدل والإنصاف وحماية الحقوق واستدامة التنمية الشاملة. حيث قدمت الدراسة بعض الإضاءات الرئيسة لسلسلة القيم والمبادئ الأخلاقية التي يقوم عليها نظام التمويل الإسلامي أكان لا مركزيًا أو مركزيًا. وانتهت الدراسة إلى بعض الاستخلاصات والنتائج، ومنها حالة الرؤية الشرعية للأصول والمنصات الرقمية. وبينت الدراسة أن الاتجاه الغالب في هذه الرؤى هو التريث والانتظار في إصدار الحكم الشرعي النهائي، وأنّ هناك تعددًا في الرؤى الشرعية مع تغيّرها أحيانًا، فهي ليست مستقرة بعد ويغلب عليها الحذر. وبالرغم من ذلك تؤكد الدراسة على أهمية الاستفادة من هذا التطور التقني المالي المتسارع في ابتكار منتجات تمويلية متوافقة مع أحكام الشريعة، وهذا هو التحدي القائم من منظور الاقتصاد الاسلامي. الكلمات المفتاحية: الفجوة بين الابتكار والتنظيم الشرعي، التمويل اللامركزي الإسلامي، سلسلة القيم، الأصول الرقمية، العملات الرقمية.
This dissertation investigates how tokenised claims and algorithmic governance reshape interactions in Web3, with a particular focus on business-to-business (B2B) settings. Building on the insight that digital platforms and infrastructures are mutually entangled—platforms acquiring infrastructural roles and infrastructures accumulating platform logics—the study examines how this entanglement reappears in blockchain-based systems and what it means for value creation, value distribution, and institutional control. Rather than assuming decentralization as an outcome, the dissertation asks how governance is actually assembled across code, organizations, and markets, and how these assemblies channel rights, risks, and rents over time. In this sense, the thesis extends platform/infrastructure scholarship into the Web3 domain, showing how infrastructuring and platformization remain co-constitutive under new technical conditions (e.g., programmable settlement, public ledgers, composability). The research is guided by the following question: How does algorithmic governance of tokenised claims affect dynamics of value creation and distribution in Web3? The thesis addresses a gap in extant work by analysing the combined economic and governance consequences of tokenisation in commercial contexts, rather than treating governance as either purely technical (smart contracts) or purely institutional (foundations, standards, regulators). Methodologically, this research adopts a qualitative, interpretive design centred on semi-structured interviews with founders and leads of Web3 projects oriented toward commercialization and enterprise use. Interview evidence is triangulated with document analysis (white papers, governance docs, upgrade logs) to trace how decision rights are allocated, which boundary resources act as chokepoints, and how incentives and accountability are engineered. The sample focuses on projects that tokenise rights and obligations to orchestrate inter-firm exchanges (e.g., guarantees, attribution, royalties), enabling a consistent comparison of governance choices and their distributional signatures. Theoretically, the thesis contributes a layered view of Web3 governance that differentiates transaction governance (smart-contract rules that execute exchanges) from platform governance (meta-rules that structure participation, evolution, and control)—layers that are interdependent yet analytically distinct. Across cases, transaction governance supplies deterministic settlement (escrows, splits, auctions), while platform governance defines constitutional levers (eligibility schemas, listings, parameter updates, treasury policy, emergency powers). This distinction clarifies why “more on-chain” does not automatically imply “more decentralised”: instruments can be automated while decision rights remain concentrated. The framing resonates with and extends platform governance scholarship that locates governance in the ongoing division of decision rights, control mechanisms, and incentives among interdependent actors. Empirically, the thesis identifies three governance models—monocentric, moderately polycentric (P2), and highly polycentric (P1)—and analyses how each allocates rights and rents. Monocentric configurations recentre constitutional authority in a focal hub (firm, foundation, tightly bonded coalition), delivering speed, legal legibility, and coherent risk management, while concentrating surplus upstream via control of boundary resources (standards, registries, upgrade cadence, listings). Moderately polycentric arrangements disperse constitutional authority across overlapping venues (token voters, stewards, committees, standards groups), pairing automated execution at the edge with contestable meta-rules and auditable, replaceable discretion. Highly polycentric designs thin the platform layer and push coordination into markets and minimal, auditable rules (fee markets, open listings, plural oracles), improving neutrality and exit but requiring continuous work to diffuse emergent chokepoints (indices, bridges, relays). The patterns observed align with infrastructure/platform research on how control points shape innovation and value capture and with blockchain governance work emphasizing the allocation of decision and control rights. For B2B contexts, the analysis suggests a pragmatic equilibrium. Applications that demand auditability, finality, and accountable remediation (e.g., elections, trade guarantees) gravitate toward monocentric settlements; applications with heterogeneous actors and rapid iteration (e.g., creator and talent markets) benefit from moderately polycentric designs that preserve micro-level determinism with macro-level contestability. Across models, tokenisation expands what can be coordinated, but distributional outcomes hinge on who controls admission, measurement, and upgrade pathways. Accordingly, the thesis proposes design heuristics: separate transaction and platform governance, publish change logs and revocation paths, pluralise attestors at measurement junctions, time-box mandates, and keep credible exit technically and institutionally real. In sum, the dissertation advances an integrated account of Web3 as a political economy of programmable claims and layered governance. It shows how infrastructuring and platformization fold into one another under blockchain conditions, how distinct governance models redistribute rights and rents, and how B2B value propositions depend as much on constitutional design as on code. The framework equips scholars and practitioners to evaluate Web3 systems not by decentralisation rhetoric, but by the concrete allocation of decision rights, boundary resources, and incentives across layers and venues.
Traditional single-proposer blockchains suffer from miner extractable value (MEV), where validators exploit their serial monopoly on transaction inclusion and ordering to extract rents from users. While there have been many developments at the application layer to reduce the impact of MEV, these approaches largely require auctions as a subcomponent. Running auctions efficiently on chain requires two key properties of the underlying consensus protocol: selective-censorship resistance and hiding. These properties guarantee that an adversary can neither selectively delay transactions nor see their contents before they are confirmed. We propose a multiple concurrent proposer (MCP) protocol offering exactly these properties.
Smart contract-based automation of financial derivatives offers substantial efficiency gains, but its real-world adoption is constrained by the complexity of translating financial specifications into gas-efficient executable code. In particular, generating code that is both functionally correct and economically viable from high-level specifications, such as the Common Domain Model (CDM), remains a significant challenge. This paper introduces a Reinforcement Learning (RL) framework to generate functional and gas-optimized Solidity smart contracts directly from CDM specifications. We employ a Proximal Policy Optimization (PPO) agent that learns to select optimal code snippets from a pre-defined library. To manage the complex search space, a two-phase curriculum first trains the agent for functional correctness before shifting its focus to gas optimization. Our empirical results show the RL agent learns to generate contracts with significant gas savings, achieving cost reductions of up to 35.59% on unseen test data compared to unoptimized baselines. This work presents a viable methodology for the automated synthesis of reliable and economically sustainable smart contracts, bridging the gap between high-level financial agreements and efficient on-chain execution.
Zhifan Ye, Jiachi Chen, Zhenzhe Shao, Lingfeng Bao · 6 authors
The rise of blockchain has brought smart contracts into mainstream use, creating a demand for smart contract generation tools. While large language models (LLMs) excel at generating code in general-purpose languages, their effectiveness on Solidity, the primary language for smart contracts, remains underexplored. Solidity constitutes only a small portion of typical LLM training data and differs from general-purpose languages in its version-sensitive syntax and limited flexibility. These factors raise concerns about the reliability of existing LLMs for Solidity code generation. Critically, existing evaluations, focused on isolated functions and synthetic inputs, fall short of assessing models' capabilities in real-world contract development. To bridge this gap, we introduce SolContractEval, the first contract-level benchmark for Solidity code generation. It comprises 124 tasks drawn from real on-chain contracts across nine major domains. Each task input, consisting of complete context dependencies, a structured contract framework, and a concise task prompt, is independently annotated and cross-validated by experienced developers. To enable precise and automated evaluation of functional correctness, we also develop a dynamic evaluation framework based on historical transaction replay. Building on SolContractEval, we perform a systematic evaluation of six mainstream LLMs. We find that Claude-3.7-Sonnet achieves the highest overall performance, though evaluated models underperform relative to their capabilities on class-level generation tasks in general-purpose programming languages. Second, current models perform better on tasks that follow standard patterns but struggle with complex logic and inter-contract dependencies. Finally, they exhibit limited understanding of Solidity-specific features and contextual dependencies.
Blockchain is an emerging technology that is being used to create innovative solutions in many areas, including healthcare. Nowadays healthcare systems face challenges, especially with security, trust, and remote data access. As patient records are digitized and medical systems become more interconnected, the risk of sensitive data being exposed to cyber threats has grown. In this evolving time for healthcare, it is important to find a balance between the advantages of new technology and the protection of patient information. The combination of blockchain–InterPlanetary File System technology and conventional electronic health record (EHR) management has the potential to transform the healthcare industry by enhancing data security, interoperability, and transparency. However, a major issue that still exists in traditional healthcare systems is the continuous problem of remote data unavailability. This research examines practical methods for safely accessing patient data from any location at any time, with a special focus on IPFS servers and blockchain technology in addition to group signature encryption. Essential processes like maintaining the confidentiality of medical records and safe data transmission could be made easier by these technologies. Our proposed framework enables secure, remote access to patient data while preserving accessibility, integrity, and confidentiality using Ethereum blockchain, IPFS, and group signature encryption, demonstrating hospital-scale scalability and efficiency. Experiments show predictable throughput reduction with file size (200 → 90 tps), controlled latency growth (90 → 200 ms), and moderate gas increase (85k → 98k), confirming scalability and efficiency under varying healthcare workloads. Unlike prior blockchain–IPFS–encryption frameworks, our system demonstrates hospital-scale feasibility through the practical integration of group signatures, hierarchical key management, and off-chain erasure compliance. This design enables scalable anonymous authentication, immediate blocking of compromised credentials, and efficient key rotation without costly re-encryption.
The Generalized Tempered Stable (GTS) distribution extends classical stable laws through exponential tempering, preserving the power-law behavior while ensuring finite moments. This makes it especially suitable for modeling heavy-tailed financial data. However, the lack of closed-form densities poses significant challenges for simulation. This study provides a comprehensive and systematic comparison of GTS simulation methods, including rejection-based algorithms, series representations, and an enhanced Fast Fractional Fourier Transform (FRFT)-based inversion method. Through extensive numerical experiments on major financial assets (Bitcoin, Ethereum, the S&P 500, and the SPY ETF), this study demonstrates that the FRFT method outperforms others in terms of accuracy and ability to capture tail behavior, as validated by goodness-of-fit tests. Our results provide practitioners with robust and efficient simulation tools for applications in risk management, derivative pricing, and statistical modeling.
Chornolius Hendreo, Nia Pratiwi, Syarif Muhammad Ilham
In this study, researchers identified a holistic approach integrating risk and opportunity analysis of Decentralized Finance (DeFi) within the Indonesian context, addressing the limited local research gap. The researchers identified the primary risks of DeFi, namely smart contract vulnerabilities (AHP weight 0.54), market volatility (0.30), and regulatory uncertainty (0.16), with case examples such as the 2021 Poly Network attack and the 2022 TerraUSD collapse. Additionally, significant opportunities were highlighted, including financial inclusion (AHP weight 0.54) for 50% of Indonesia’s unbanked population, technological innovation through layer-2 solutions like Optimism, and cost efficiency of up to 50% compared to traditional finance. The novelty of this research lies in the application of the Analytical Hierarchy Process (AHP) to prioritize risk and opportunity factors, as well as the development of a blockchain-data-driven SWOT strategy to support financial inclusion in remote areas. The researchers recommend enhancing smart contract security and user education to foster a secure and inclusive digital financial ecosystem in Indonesia, supporting sustainable DeFi growth.
Noha E. El-Attar, Marwa Salama, Mohamed Abdelfattah, Sanaa Taha
Detecting, tracking, and preventing cryptocurrency money laundering within blockchain systems is a major challenge for governments worldwide. This paper presents an anomaly detection model based on blockchain technology and machine learning to identify cryptocurrency money-laundering accounts within Ethereum blockchain networks. The proposed model employs Particle Swarm Optimization (PSO) to select optimal feature subsets. Additionally, three machine learning algorithms—XGBoost, Isolation Forest (IF), and Support Vector Machine (SVM)—are employed to detect suspicious accounts. A Genetic Algorithm (GA) is further applied to determine the optimal hyperparameters for each machine learning model. The evaluations demonstrate the superiority of the XGBoost algorithm over SVM and IF, particularly when enhanced with GA. It achieved accuracy, precision, recall, and F1-score values of 0.98, 0.97, 0.98, and 0.97, respectively. After applying GA, XGBoost’s performance metrics improved to 0.99 across all categories.
The evolution of technology has sparked significant interest in transforming traditional voting into efficient, secure online systems.This study introduces a novel approach that enhances voter privacy and data security by utilizing a UniqueBlend ID algorithm to generate unique identifiers for voters, obscuring Aadhar numbers and preventing identity disclosure.Blockchain technology is integrated to enhance transparency, eliminate fraud, and create an immutable voting record.However, integrating decentralized applications (dApps) with legacy web2 systems presents challenges in data storage and retrieval.To address these issues, this research presents Optima, an interface that simplifies data segregation between web2 and web3 storage systems using a JSON-based structure.Optima optimizes storage efficiency, minimizes gas fees, and reduces development overhead, allowing developers to focus on application logic.This streamlined data segregation approach significantly improves the efficiency and security of online voting, ensuring voter anonymity and maintaining the integrity of the voting process.
У статті проаналізовано ключові недоліки централізованих афілійованих платформ, зокрема брак прозорості, складність виплат і надмірні витрати на інтеграцію. Запропоновано інтеграцію Web3-технологій (блокчейну, смарт- контрактів) як ефективну альтернативу для підвищення довіри та оптимізації процесів, що підтверджується попередніми дослідженнями. Робота наголошує на відсутності детальних методів та моделей інтеграції Web3-технологій в системи афілійованого маркетингу і формулює низку дослідницьких питань, які охоплюють криптографію, розробку смарт-контрактів, графовий аналіз взаємодій та OO-моделювання децентра- лізованих застосунків. Представлено методологічний підхід, що складається з аналізу існуючих моделей, огляду літератури, розробки Web3-базованої системи та формаль- ного тестування прототипів. Бібл. 8, іл. 2, табл. 1
Rowdy Chotkan, Bulat Nasrulin, Jérémie Decouchant, Johan Pouwelse
Spam poses a growing threat to blockchain networks. Adversaries can easily create multiple accounts to flood transaction pools, inflating fees and degrading service quality. Existing defenses against spam, such as fee markets and staking requirements, primarily rely on economic deterrence, which fails to distinguish between malicious and legitimate users and often exclude low-value but honest activity. To address these shortcomings, we present StarveSpam, a decentralized reputation-based protocol that mitigates spam by operating at the transaction relay layer. StarveSpam combines local behavior tracking, peer scoring, and adaptive rate-limiting to suppress abusive actors, without requiring global consensus, protocol changes, or trusted infrastructure. We evaluate StarveSpam using real Ethereum data from a major NFT spam event and show that it outperforms existing fee-based and rule-based defenses, allowing each node to block over 95% of spam while dropping just 3% of honest traffic, and reducing the fraction of the network exposed to spam by 85% compared to existing rule-based methods. StarveSpam offers a scalable and deployable alternative to traditional spam defenses, paving the way toward more resilient and equitable blockchain infrastructure.
An automated market maker (AMM) provides a method for creating a decentralized exchange on the blockchain. For this purpose, individual investors lend liquidity to the AMM pool in exchange for a stream of fees earned from its operations as a market maker. Within this work, we reinterpret the loss-versus-rebalancing as the implied fee stream generated by an AMM so that a risk-neutral investor is indifferent in the decision of providing liquidity. With this implied fee structure, we propose a novel fixed-for-floating swap on the fees generated by an AMM in order to quote the implied volatilities and implied correlations of digital assets. We apply this theory to realized fees in different markets to empirically validate the relevance of the deduced fee-based volatility.
Blockchain Business applications and cryptocurrencies such as enable secure, decentralized value transfer, yet their pseudonymous nature creates opportunities for illicit activity, challenging regulators and exchanges in anti money laundering (AML) enforcement. Detecting fraudulent transactions in blockchain networks requires models that can capture both structural and temporal dependencies while remaining resilient to noise, imbalance, and adversarial behavior. In this work, we propose an ensemble framework that integrates Graph Convolutional Networks (GCN), Graph Attention Networks (GAT), and Graph Isomorphism Networks (GIN) to enhance blockchain fraud detection. Using the real-world Elliptic dataset, our tuned soft voting ensemble achieves high recall of illicit transactions while maintaining a false positive rate below 1%, beating individual GNN models and baseline methods. The modular architecture incorporates quantum-ready design hooks, allowing seamless future integration of quantum feature mappings and hybrid quantum classical graph neural networks. This ensures scalability, robustness, and long-term adaptability as quantum computing technologies mature. Our findings highlight ensemble GNNs as a practical and forward-looking solution for real-time cryptocurrency monitoring, providing both immediate AML utility and a pathway toward quantum-enhanced financial security analytics.