This chapter addresses the emerging challenges of consumer protection in the context of non-fungible tokens (NFTs). Although NFTs are generally excluded from financial regulation under instruments such as the Markets in Crypto-Assets Regulation (MiCAR), they are increasingly marketed and sold to consumers as speculative digital assets. Many of these transactions rely on techniques that obscure key information, such as price, scarcity, or value, or employ design strategies that steer user behaviour. The chapter examines how the Unfair Commercial Practices Directive (UCPD) can apply to such cases, with a focus on misleading actions and omissions, aggressive practices, and interface-driven manipulation. While NFTs are not expressly recognized as consumer products, they fall within the material scope of EU consumer law when offered in a business-to-consumer setting. The analysis reveals that the UCPD remains a relevant, albeit under-enforced, tool for addressing deceptive and exploitative practices in NFT markets. However, enforcement is hindered by the novelty of the technology and the opacity of many digital marketplaces. The chapter calls for more precise regulatory guidance, greater awareness among enforcement authorities, and renewed attention to the structural risks posed by token-based commerce.
This chapter investigates the complex contractual dynamics that underpin the transfer of non-fungible tokens (NFTs) within online marketplaces. Particularly, it offers a critical assessment of the impact that platform terms of service (ToS) have on shaping rights and obligations in NFT transactions. Case studies from prominent NFT online marketplaces illustrate that, while these marketplaces claim to act as third-party intermediaries, their ToS significantly dictate the enforceability of IP rights, the extent of user protections, and the procedural rules surrounding NFT transactions. The chapter then examines the relevance and application of the EU Digital Services Act (DSA) to NFT marketplaces, pressing NFT marketplaces to reassess and align their ToS towards the goals of transparency and fairness. Ultimately, the discussion aims to provide insights into the intersection of contract law, NFTs, and the EU DSA, offering an initial understanding of the enforcement challenges and opportunities presented by the EU’s platform governance approach in the evolving NFT marketplace ecosystem.
This chapter examines the potential of non-fungible tokens (NFTs) as an instrument to enhance access to justice, highlighting the necessity for judicial and legislative innovation to accommodate them into existing legal frameworks. We analyze two instances of such innovations: judicial adaptations in the Anglo-American legal systems that recognize NFTs as a tool to improve access to justice from both a procedural and substantive perspective and legislative measures in the European Union that incorporate NFTs into regulatory schemes, potentially improving substantive justice access to justice. Our findings suggest that while judicial creativity has played a crucial role in advancing justice in the discussed cases, it is insufficient on its own. Comprehensive legislative reforms are essential, and this requires regulators to be more attendant to market developments, specifically in the field of legal tech, and mechanisms need to be developed to reduce information asymmetries. In designing any new legislative scheme for crypto-assets, access to justice should be a key consideration in mitigating the risks of this new technology.
Non-fungible tokens (NFTs) are unique digital tokens built on blockchain technology that represent and provide public evidence of the ownership of underlying assets. They have reshaped the digital landscape by offering a novel paradigm for ownership authenticity and value exchange across various sectors, including art, entertainment, and real estate. NFTs have given rise to complex legal issues, particularly in data protection law. This chapter investigates the intersection of NFTs and data protection, emphasizing the legal challenges arising from the processing of personal data within NFT ecosystems. The General Data Protection Regulation (GDPR or Regulation), known for its stringent requirements and broad territorial scope, serves as the primary framework for the analysis. This study examines how NFTs, which often store or reference various forms of data including metadata, assets, ownership records, and transaction histories, fall under the GDPR’s strict data protection regime. It explores the challenges posed by blockchain’s inherent characteristics, such as immutability and decentralization, in aligning with GDPR principles.
This chapter delves into the complex legal landscape surrounding the ownership of non-fungible tokens (NFTs). Initially distinguishing NFTs from fungible cryptocurrencies like Bitcoin, the discussion highlights how NFTs leverage blockchain technology to certify the authenticity and ownership of unique digital objects. While NFTs record ownership and enable exchanges, they do not inherently confer legal ownership or copyright of the underlying asset. The chapter examines various perspectives on classifying NFTs within existing legal frameworks in common law and civil law, noting the challenges posed by their ability to represent diverse assets and rights. It explores analogies to property and intellectual property law, ultimately advocating for treating NFTs as a form of private property. This approach aligns with recent recommendations by the English Law Commission to adapt property law for digital assets. By recognizing NFTs as personal property, the chapter argues, we can provide robust legal protections for valuable NFTs and support their future development in digital marketplaces.
Unmanned Aerial Vehicles (UAVs) are increasingly deployed in inspection and monitoring missions, yet onboard computation and communication impose significant energy burdens that limit flight time and operational scope. In this work, we introduce a novel, blockchain-enabled framework-grounded in the Distributed Autonomous Organization (DAO) paradigm-for orchestrating distributed analytics across a swarm of UAVs. Leveraging the OASEES project's smart-contract architecture, each drone embeds a Metrics Module for real-time power monitoring, a Behavioral Module for adaptive control, and a Blockchain Agent that autonomously proposes, votes on, and executes collective decisions. Three concurrent threads-Proposal Trigger, Voting, and Action Execution-enable fully decentralized governance of swarm behavior: from detecting critical energy thresholds and formulating swarm-wide conservation maneuvers, to executing approved strategies across all members. We validate our framework in a UAV-based infrastructure inspection scenario, employing a YOLOv5 object-detection pipeline to classify four corrosion classes on a telecommunications mast under three video-capture modalities (short-distance, long-distance, and horizontally concatenated streams). Across all configurations, our system achieves near-perfect precision, recall, and mean Average Precision (mAP50-95$\approx 0.995$), demonstrating both the efficacy of distributed workload inference and the feasibility of treating a single drone as a multi-feed processor. These results underscore the potential of DAO-driven UAV swarms for energy-aware, resilient aerial analytics, and pave the way for fully decentralized 5G/6G-enabled airborne networks.
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.
This chapter examines the tax treatment applicable to non-fungible tokens (NFTs). NFTs are unique digital assets stored on a blockchain, primarily used to certify the authenticity and ownership of digital or physical items. With the rapid rise in the popularity of NFTs, discussions about their taxation have become increasingly prominent. From a tax law perspective, NFTs represent both an innovative financial product and a legal challenge, as they raise critical questions about their classification and treatment under existing tax frameworks. NFTs may represent digital or non-digital assets, and an important tax consideration is whether the NFT carries value independent of the asset it represents. In today’s digital age, characterized by technological advancements, evolving artistic values, and the ease of replication, determining the locus of value has become increasingly challenging, necessitating a nuanced, case-by-case analysis. This chapter evaluates the taxation of NFTs by examining their common use cases and exploring the implications for tax systems. It seeks to provide insights into the legal and practical challenges posed by the taxation of NFTs.
This chapter examines the intersection of blockchain technology and its environmental impact, focusing on the energy-intensive validation protocols underlying blockchain systems. It provides a brief overview of blockchain technology and non-fungible tokens (NFTs), highlighting their unique characteristics and growing popularity. The transition from proof of work (PoW) to proof of stake (PoS) is analyzed in terms of their differing environmental footprints. The chapter explores climate change legislation from the United Nations Framework Convention on Climate Change (UNFCCC) to the Paris Agreement and offers an overview of European climate policies. It then assesses emerging legislative trends in the European Union, the United States, and China concerning blockchain’s environmental impact. Finally, it evaluates the legitimacy of PoW and PoS mechanisms within the framework of international and European climate regulations, offering insights into aligning blockchain technology with global sustainability goals.
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.
The intersection of non-fungible tokens (NFTs) and decentralised autonomous organisations (DAOs) highlights two transformative, yet divergent, applications of blockchain technology. NFTs focus on establishing unique digital ownership, emphasising individuality and exclusivity, while DAOs represent a collective governance model based on community-driven decision-making. This dynamic mirrors the contrasting personalities in The Odd Couple , symbolising the challenge of balancing uniqueness with collective action. Despite their differences, NFTs and DAOs are increasingly integrated in innovative ways, enabling new forms of collaboration and legal challenges. This chapter explores how NFTs and DAOs coexist, examining their legal structures, governance mechanisms and practical applications. Ultimately, it asks whether these two concepts are truly an ‘odd couple’ or a symbiotic pairing that is redefining ownership, governance and digital interaction.
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.
Emerging applications need dynamic, deterministic performance across network and cloud providers. We demonstrate a blockchain-based solution with smart contract execution to establish network and cloud services on a multi-provider, multi-layer IP-optical network and cloud infrastructure.
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.