We present PromptChain, a decentralized Web3 architecture that establishes AI prompts as first-class digital assets with verifiable ownership, version control, and monetization capabilities. Current centralized platforms lack mechanisms for proper attribution, quality assurance, or fair compensation for prompt creators. PromptChain addresses these limitations through a novel integration of IPFS for immutable storage, smart contracts for governance, and token incentives for community curation. Our design includes: (1) a comprehensive metadata schema for cross-model compatibility, (2) a stake-weighted validation mechanism to align incentives, and (3) a token economy that rewards contributors proportionally to their impact. The proposed architecture demonstrates how decentralized systems could potentially match centralized alternatives in efficiency while providing superior ownership guarantees and censorship resistance through blockchain-anchored provenance tracking. By decoupling prompts from specific AI models or outputs, this work establishes the foundation for an open ecosystem of human-AI collaboration in the Web3 era, representing the first systematic treatment of prompts as standalone digital assets with dedicated decentralized infrastructure.
Decentralized finance (DeFi) has introduced a new era of permissionless financial innovation but also led to unprecedented market manipulation. Without centralized oversight, malicious actors coordinate shilling campaigns and pump-and-dump schemes across various platforms. We propose a Multi-Agent Reinforcement Learning (MARL) framework for decentralized manipulation detection, modeling the interaction between manipulators and detectors as a dynamic adversarial game. This framework identifies suspicious patterns using delayed token price reactions as financial indicators.Our method introduces three innovations: (1) Group Relative Policy Optimization (GRPO) to enhance learning stability in sparse-reward and partially observable settings; (2) a theory-based reward function inspired by rational expectations and information asymmetry, differentiating price discovery from manipulation noise; and (3) a multi-modal agent pipeline that integrates LLM-based semantic features, social graph signals, and on-chain market data for informed decision-making.The framework is integrated within the Symphony system, a decentralized multi-agent architecture enabling peer-to-peer agent execution and trust-aware learning through distributed logs, supporting chain-verifiable evaluation. Symphony promotes adversarial co-evolution among strategic actors and maintains robust manipulation detection without centralized oracles, enabling real-time surveillance across global DeFi ecosystems.Trained on 100,000 real-world discourse episodes and validated in adversarial simulations, Hide-and-Shill achieves top performance in detection accuracy and causal attribution. This work bridges multi-agent systems with financial surveillance, advancing a new paradigm for decentralized market intelligence. All resources are available at the Hide-and-Shill GitHub repository to promote open research and reproducibility.
Perkembangan teknologi informasi telah mendorong transformasi ekonomi digital yang ditandai dengan kemunculan cryptocurrency sebagai instrumen keuangan baru. Di balik manfaatnya, cryptocurrency juga menimbulkan tantangan serius dalam aspek hukum, khususnya terkait tindak pidana pencucian uang (TPPU). Penelitian ini bertujuan untuk menganalisis bagaimana hukum positif di Indonesia mengatur kejahatan ekonomi berbasis digital melalui cryptocurrency serta mengkaji modus operandi yang digunakan dalam praktik pencucian uang tersebut. Metode penelitian yang digunakan adalah metode yuridis normatif dengan pendekatan perundang-undangan, doktrin hukum, serta studi literatur. Hasil penelitian menunjukkan bahwa meskipun Indonesia telah memiliki regulasi terkait TPPU melalui UU No. 8 Tahun 2010 dan pengawasan oleh BAPPEBTI terhadap platform aset kripto, praktik pencucian uang berbasis cryptocurrency masih sulit dideteksi karena sifat anonim, desentralisasi, dan kompleksitas transaksinya. Modus yang umum digunakan meliputi tahapan placement, layering, dan integration, yang seringkali melibatkan pihak ketiga seperti mixer, tumbler, dan broker OTC. Oleh karena itu, dibutuhkan pembaruan regulasi dan peningkatan kapasitas pengawasan untuk mengantisipasi dan memberantas kejahatan ekonomi digital melalui cryptocurrency di masa mendatang.
I Putu Bramasta Priadinata, I Gede Iwan Sudipa, Ni Putu Suci Meinarni, I Made Leo Radhitya · 5 authors
Cryptocurrency is a highly volatile digital asset that requires accurate predictive methods. This study compares the performance of three deep learning architectures LSTM, GRU, and Bi-LSTM in forecasting the prices of Bitcoin (BTC), Ethereum (ETH), and Binance Coin (BNB) using univariate historical data. Evaluation was conducted through regression metrics (RMSE and MAPE) and classification of price movement into five categories, ranging from very bearish to very bullish, assessed using a confusion matrix. The results show that GRU performed best for BTC (RMSE 974.72, MAPE 1.18%), while Bi-LSTM outperformed others for ETH and BNB (RMSE 43.19 and 6.83; MAPE 1.16% and 1.08%) and achieved the highest classification accuracy (55% and 52%). However, overall classification accuracy remains low, reflecting the complexity of cryptocurrency price patterns. The study is limited by its univariate approach without incorporating external variables. Its contribution lies in combining regression and classification evaluation, and it recommends exploring multivariate and ensemble models in future research.
Senior Financial Markets Dealer, Nassau, The Bahamas, Vladyslav Yakymashko
This article investigates the phenomenon of volatility clustering in the cryptocurrency markets, focusing on Bitcoin (BTC) and Ethereum (ETH), through empirical time-series analysis. The study employs quantitative methods, including GARCH modeling, to identify persistent patterns in the price fluctuations of the two leading digital assets. The analysis is based on trading data over an extended period, encompassing both phases of high market turbulence and periods of relative stability. Adopting an interdisciplinary approach that integrates behavioral finance, econometrics, and financial market theory, particular attention is given to identifying autocorrelation, memory effects, and the structure of market shocks. The findings demonstrate that volatility clustering in BTC and ETH significantly differs from similar phenomena in traditional financial markets, largely due to their speculative nature, asset novelty, and the influence of both institutional and retail participants. The identified patterns enhance risk profiling for crypto assets and may be applied in hedging strategies, automated trading algorithm development, and investment portfolio optimization. Additionally, the study highlights the importance of accounting for both micro- and macroeconomic factors influencing market behavior. The article is intended for researchers in digital finance, risk managers, analysts, investors, and anyone examining unstable assets in conditions of high uncertainty and a rapidly changing informational landscape.
This study proposes a secure authentication framework based on Non-Fungible Tokens (NFTs) for enterprise microservices in big data environments.The objective was to overcome the limitations of traditional mechanisms such as JSON Web Tokens (JWTs), which lack built-in traceability, revocation, and protection against session-based attacks.Developed as a conceptual system design project, the framework was simulated using a custom private blockchain built in Go (Golang).NFTs were minted and transferred through smart contracts to act as temporary, verifiable access tokens.A custom hardware wallet, built on the ESP32-S3 microcontroller and programmed using the Arduino framework, was used to establish a physical barrier between the user and the system.This approach ensured that token ownership remained tamper-resistant and device-bound, ensuring that it remained secure.No human subjects were involved in this study; instead, the system was evaluated through functional test scenarios to assess authentication flow, session control, and token lifecycle management.Tools such as Docker and Redis were used to support simulation and deployment.Results indicated that the NFT-based system demonstrated improved resistance to token hijacking, session fixation, and replay attacks compared to JWT-based alternatives.Unlike conventional systems, it does not rely on static credentials and allows token revocation via on-chain burning and session-bound control.The study recommends future validation in real-world enterprise settings to assess scalability, fault tolerance, and real-time integration.The proposed framework offers a pathway toward decentralised, privacy-preserving authentication solutions suitable for secure and distributed environments.
Juan Camilo Pazos-Alfonso, Ana Luisa Mendoza-Barrera
This argumentative essay explores the psychological and clinical implications of using non-fungible tokens (NFTs) as symbolic tools in real-world psychological therapy. Adopting a constructive and pro-NFT perspective, the text argues that NFTs—unique digital assets recorded on a blockchain—offer novel opportunities to represent therapeutic milestones, identity processes, and meaningful personal experiences. The essay examines how NFTs can enhance patient motivation, promote self-reflection, support engagement, and reinforce a sense of autonomy throughout the therapeutic process. Drawing from concepts such as token economies, gamification, and narrative psychology, NFTs are proposed as symbolic reinforcers that validate personal growth, improve adherence to treatment, and help patients actively shape their therapeutic journey. The discussion highlights potential use cases, such as NFT-based achievements, digital identity representations, and collectible artifacts created during therapy. Technical and ethical considerations—such as accessibility, privacy, and informed consent—are addressed to ensure responsible implementation. Grounded in recent peer-reviewed research, the essay concludes that NFTs have the potential to enrich mental health interventions, especially in digital and immersive therapy environments. It recommends future empirical studies to assess the effectiveness of NFT-based systems in clinical practice. Ultimately, NFTs could serve as a bridge between emerging technologies and psychology, providing patients with symbolic tools to represent, commemorate, and take ownership of their therapeutic progress.
Transparency is one of the key benefits of public blockchains. However, the public visibility of transactions potentially compromises users' privacy. The fundamental challenge is to balance the intrinsic benefits of blockchain openness with the vital need for individual confidentiality. The proposal suggests creating a confidential version of wrapped Ethereum (cWETH) fully within the application layer. The solution combines the Elliptic Curve (EC) Twisted ElGamal-based commitment scheme to preserve confidentiality and the EC Diffie-Hellman (DH) protocol to introduce accessibility limited by the commitment scheme. To enforce the correct generation of commitments, encryption, and decryption, zk-SNARKs are utilized.
Introduction: The rapid development of digital technology has introduced new challenges in the practice of inheritance distribution, particularly concerning digital assets such as cryptocurrency and NFTs.Purposes of the Research: This study aims to explore gender justice in the distribution of digital inheritance, focusing on the Islamic fiqh perspective toward cryptocurrency and NFTs assets in Dubai.Methods of the Research: Using a qualitative approach, the research analyzes fiqh texts, existing regulations, and real-life cases related to digital inheritance. Data were collected through document analysis, case studies, and expert interviews involving Islamic scholars and digital asset practitioners in Dubai.Results of the Research: The findings reveal that digital inheritance, characterized by unique properties such as anonymity and the need for secure access, presents significant challenges in ensuring fair distribution, especially for women. The study highlights cultural and technological barriers that limit women’s access to digital inheritance, despite their growing economic contributions. The novelty of this research lies in proposing a contemporary fiqh framework that integrates traditional Islamic principles with modern technological solutions such as blockchain, aiming to ensure transparency and fairness in inheritance distribution. By addressing the gender gap in digital inheritance, this research contributes to the development of equitable and practical fiqh guidelines for Muslim communities in the digital era.
Sofia Sakka, Nikolaos Pavlidis, Vasiliki Liagkou, Ioannis Panges · 7 authors
The growing influence of technology in the healthcare industry has led to the creation of innovative applications that improve convenience, accessibility, and diagnostic accuracy. However, health applications face significant challenges concerning user privacy and data security, as they handle extremely sensitive personal and medical information. Privacy-Enhancing Technologies (PETs), such as Privacy-Attribute-based Credentials, Differential Privacy, and Federated Learning, have emerged as crucial tools to tackle these challenges. Despite their potential, PETs are not widely utilized due to technical and implementation obstacles. This research introduces a comprehensive framework for protecting health applications from privacy and security threats, with a specific emphasis on gamified mental health apps designed to manage Attention Deficit Hyperactivity Disorder (ADHD) in children. Acknowledging the heightened sensitivity of mental health data, especially in applications for children, our framework prioritizes user-centered design and strong privacy measures. We suggest an identity management system based on blockchain technology to ensure secure and transparent credential management and incorporate Federated Learning to enable privacy-preserving AI-driven predictions. These advancements ensure compliance with data protection regulations, like GDPR, while meeting the needs of various stakeholders, including children, parents, educators, and healthcare professionals.
Antonino Castelli, Paolo Giudici, Alessandro Piergallini
The rapid growth of crypto markets has opened new opportunities for investors, but at the same time exposed them to high volatility. To address the challenge of managing dynamic portfolios in such an environment, this paper presents a practical application of a multi-agent system designed to autonomously construct and evaluate crypto-asset allocations. Using data on daily frequencies of the ten most capitalized cryptocurrencies from 2020 to 2025, we compare two automated investment strategies. These are a static equal weighting strategy and a rolling-window optimization strategy, both implemented to maximize the evaluation metrics of the Modern Portfolio Theory (MPT), such as Expected Return, Sharpe and Sortino ratios, while minimizing volatility. Each step of the process is handled by dedicated agents, integrated through a collaborative architecture in Crew AI. The results show that the dynamic optimization strategy achieves significantly better performance in terms of risk-adjusted returns, both in-sample and out-of-sample. This highlights the benefits of adaptive techniques in portfolio management, particularly in volatile markets such as cryptocurrency markets. The following methodology proposed also demonstrates how multi-agent systems can provide scalable, auditable, and flexible solutions in financial automation.
Extreme volatility, nonlinear dependencies, and systemic fragility are characteristics of cryptocurrency markets. The assumptions of normality and centralized control in traditional financial risk models frequently cause them to miss these changes. Four components-volatility stress testing, stablecoin hedging, contagion modeling, and Monte Carlo simulation-are integrated into this paper's modular simulation framework for crypto portfolio risk analysis. Every module is based on mathematical finance theory, which includes stochastic price path generation, correlation-based contagion propagation, and mean-variance optimization. The robustness and practical relevance of the framework are demonstrated through empirical validation utilizing 2020-2024 USDT, ETH, and BTC data.
Benjamin Gillen, Rashmi Ranjan Bhuyan, Gourab Mukherjee, Austin Pollok
The Ethereum blockchain plays a central role in the broader cryptocurrency ecosystem, enabling a wide range of financial activity through the use of smart contracts. This paper investigates how individual Ethereum wallets responded to the collapse of FTX, one of the largest centralized cryptocurrency exchanges. Moving beyond price-based event studies, we adopt a bottom-up approach using granular wallet-level data. We construct a representative sample of Ethereum addresses and analyze their transaction behavior before and after the collapse using an explainable artificial intelligence (XAI) framework. Our proposed framework addresses data scarcity in high-resolution wallet-level daily transactions by employing a calibrated zero-inflated generalized linear fixed effects model. Our analysis quantifies distinct shifts in transaction intensity and stablecoin usage, highlighting a flight to safety within the ecosystem. These findings underscore the value of a bottom-up methodology for quantifying the user-level impact of blockchain-based shocks, offering insights beyond traditional price-level analysis through wallet-level data.
Aufa Nasywa Rahman, Bimo Sunarfri Hantono, Guntur Dharma Putra
Open banking framework enables third party providers to access financial data across banking institutions, leading to unprecedented innovations in the financial sector. However, some open banking standards remain susceptible to severe technological risks, including unverified data sources, inconsistent data integrity, and lack of immutability. In this paper, we propose a layered architecture that provides assurance in data trustworthiness with three distinct levels of trust, covering source validation, data-level authentication, and tamper-proof storage. The first layer guarantees the source legitimacy using decentralized identity and verifiable presentation, while the second layer verifies data authenticity and consistency using cryptographic signing. Lastly, the third layer guarantees data immutability through the Tangle, a directed acyclic graph distributed ledger. We implemented a proof-of-concept implementation of our solution to evaluate its performance, where the results demonstrate that the system scales linearly with a stable throughput, exhibits a 100% validation rate, and utilizes under 35% of CPU and 350 MiB memory. Compared to a real-world open banking implementation, our solution offers significantly reduced latency and stronger data integrity assurance. Overall, our solution offers a practical and efficient system for secure data sharing in financial ecosystems while maintaining regulatory compliance.
There is growing interest in giving AI agents access to cryptocurrencies as well as to the smart contracts that transact them. But doing so, this position paper argues, could lead to formidable new vectors of AI harm. To support this argument, we first examine the unique properties of cryptocurrencies and smart contracts that could give rise to these new vectors of AI harm. Next, we describe each of these new vectors of AI harm in detail, providing a first-of-its-kind taxonomy. Finally, we conclude with a call for more technical research aimed at preventing and mitigating these new vectors of AI , thereby making it safer to endow AI agents with cryptocurrencies and smart contracts.
This paper presents an integrated Smart City platform that combines digital twin technology, advanced machine learning, and a private blockchain network to enhance data-driven decision making and operational efficiency in both public enterprises and small and medium-sized enterprises (SMEs). The proposed cloud-based business intelligence model automates Extract, Transform, Load (ETL) processes, enables real-time analytics, and secures data integrity and transparency through blockchain-enabled audit trails. By implementing the proposed solution, Smart City and public service providers can significantly improve operational efficiency, including a 15% reduction in costs and a 12% decrease in fuel consumption for waste management, as well as increased citizen engagement and transparency in Smart City governance. The digital twin component facilitated scenario simulations and proactive resource management, while the participatory governance module empowered citizens through transparent, immutable records of proposals and voting. This study also discusses technical, organizational, and regulatory challenges, such as data integration, scalability, and privacy compliance. The results indicate that the proposed approach offers a scalable and sustainable model for Smart City transformation, fostering citizen trust, regulatory compliance, and measurable environmental and social benefits.
Purpose: The article aims to explore how blockchain-based smart contracts, when integrated into Enterprise Resource Planning (ERP) systems, can enhance efficiency, transparency, and security of logistics operations. It seeks to address the limitations of traditional ERP platforms and proposes a new model leveraging blockchain technology to overcome these issues. Methodology: The research adopts a comprehensive literature review and secondary data analysis approach. This involves: Synthesizing existing academic and industry knowledge, identifying key technological trends, evaluating challenges and benefits of implementing smart contracts within ERP systems. Findings: The integration of blockchain smart contracts with ERP systems can: Streamline key logistics processes such as procurement, shipment tracking, customs clearance, and proof of delivery. Enable automated payment settlements, real-time compliance validation, and fraud prevention in complex supply chains. Enhance real-time visibility, trust in multi-party transactions, and process integrity. Pose challenges related to technological architecture, interoperability, and implementation in platforms like SAP S/4HANA and Oracle ERP Cloud. Unique Contribution to Theory, Policy and Practice: Offers a synthesized view of how two advanced technologies can be integrated to evolve traditional supply chain theories around trust, decentralization, and process automation. The study provides insight into the regulatory and data governance implications of blockchain in enterprise systems. Highlights the need for updated compliance frameworks that accommodate smart contracts in logistics. It also presents actionable insights and use cases from industry case studies and pilot projects. Offers a roadmap for implementation, helping logistics enterprises understand how to practically adopt and scale blockchain-enabled ERP solutions. Discusses workforce transformation needed to support such digital initiatives.
Jul 11, 2025·The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences
Abstract. Environmental surveillance, emergency response, and smart city planning all require the use of geospatial data, which includes satellite imagery, cartographic records, and real-time GPS coordinates. The high sensitivity and value of location-specific information make it unsafe to store and transmit it through conventional, centralized means, which can result in privacy breaches, unauthorized manipulations, and potential misuse. This paper aims to design and implement a secure, blockchain-based framework that blends AES (Advanced Encryption Standard) and RSA (Rivest–Shamir–Adleman) key management, which addresses these challenges. The aim is to guarantee strong data confidentiality by using symmetric encryption, and to use public-key cryptography for granular access control and secure key distribution. The proposed system uses Ethereum smart contracts to connect encrypted data references to a decentralized ledger, ensuring tamper resistance and auditability. In the proposed system, a Python-based FastAPI backend is responsible for data ingestion, cleaning, encryption, and blockchain interaction, while a React frontend can upload datasets, generate encryption keys, and retrieve access permissions. Modular microservices and well-defined APIs can seamlessly integrate various components, such as data processing scripts and on-chain contract logic, during development. The system's scalability is demonstrated by evaluating its performance against various dataset sizes, which involves metrics such as encryption overhead, blockchain transaction costs, and smart contract execution times. The practical usability of the system in actual scenarios is demonstrated through user acceptance testing, which is crucial for adoption in resource-limited environments. The results show the proposed crypto-enhanced blockchain framework can significantly enhance geospatial data security while still maintaining operational efficiency. Integration with zero-knowledge proofs may be explored in future work to enhance privacy, mitigate energy costs through alternative consensus algorithms, and enhance resilience in multi-network ecosystems through cross-chain interoperability.
The rapid digital transformation of the sports industry has opened up unprecedented opportunities for efficiency, transparency, and innovation. Traditional transaction models still suffer from significant challenges, including centralized control, lack of trust, and inefficiencies in revenue distribution. These issues often stem from reliance on intermediaries that introduce risks such as data manipulation, high operational costs, and delays in processing financial transactions. Blockchain technology presents a promising solution by enabling decentralized, secure, and transparent transactions, fostering greater trust among all stakeholders within the sports ecosystem. Existing approaches to digital transactions in the sports industry primarily depend on centralized financial institutions and third-party service providers, which not only limit transparency but also create barriers to financial inclusivity for athletes, clubs, sponsors, and fans. To address these critical limitations, we propose a blockchain-based digital transaction model that leverages smart contracts and distributed ledger technology (DLT) to enhance the efficiency, security, and fairness of transactions across the entire sports industry value chain. Our model integrates key economic principles with advanced network analysis to optimize revenue distribution, mitigate fraudulent activities, and enable real-time transaction verification. Through extensive simulations and empirical analysis, our results demonstrate a significant improvement in transaction speed, cost reduction, and overall transparency compared to conventional models. By decentralizing financial transactions, the proposed approach not only enhances financial inclusivity for all participants but also aligns with the broader vision of sustainable and equitable growth in the digital sports economy.
Frederic Haase, Tom Celig, Oliver Rath, Detlef Schoder
Abstract The emergence of cryptocurrencies and decentralized finance (DeFi) applications brings unique challenges, including high volatility, limited fundamental valuation methods, and significant informational reliance on social media. Consequently, traditional trading algorithms and decision support systems (DSS) often fall short in effectively capturing these dynamics, underscoring the need for tailored solutions. Recent research on sentiment analysis in cryptocurrency trading has provided mixed evidence regarding its predictive power, highlighting limitations in generalizability and reliability due to the inherent noise of social media content. Addressing these limitations, this study explores crowd-based trading signals, explicit buy and sell recommendations shared by users on social media platforms including X (formerly Twitter), Reddit, Stocktwits, and Telegram. We apply an event study methodology to analyze over 28,000 trading signals extracted using natural language processing (NLP) techniques based on large language models (LLMs). Our findings demonstrate that these explicit crowd-based signals significantly predict short-term cryptocurrency price movements, particularly for assets with lower market capitalization and recent negative returns. An out-of-sample trading strategy using these signals achieves superior risk-adjusted returns, outperforming both a standard cryptocurrency index (CCI30) and the S&P 500. Additionally, we uncover the role of automated accounts (signal bots) actively disseminating trading recommendations. This research advances literature by introducing a precise alternative to sentiment analysis, contributing to the understanding of social media as a distributed financial information environment, and raising theoretical considerations about algorithmic agency and trust. Practical implications span investors, social media platforms, and regulators.
The emergence of quantum computing presents profound challenges to existing cryptographic infrastructures, whilst the development of central bank digital currencies (CBDCs) has raised concerns regarding privacy preservation and excessive centralisation in digital payment systems. This paper proposes the Quantum-Resilient Privacy Ledger (QRPL) as an innovative token-based digital currency architecture that incorporates National Institute of Standards and Technology (NIST)-standardised post-quantum cryptography (PQC) with hash-based zero-knowledge proofs to ensure user sovereignty, scalability, and transaction confidentiality. Key contributions include adaptations of ephemeral proof chains for unlinkable transactions, a privacy-weighted Proof-of-Stake (PoS) consensus to promote equitable participation, and a novel zero-knowledge proof-based mechanism for privacy-preserving selective disclosure. QRPL aims to address critical shortcomings in prevailing CBDC designs, including risks of pervasive surveillance, with a 10-20 second block time to balance security and throughput in future monetary systems. While conceptual, empirical prototypes are planned. Future work includes prototype development to validate these models empirically.
Abstract This article examines the integration of Decentralized Autonomous Organizations (DAOs) into the existing legal framework of the United Kingdom, proposing a novel legal entity model termed the Decentralized Autonomous Organization Limited Liability Partnership (DAOLLP). It explores the distinctive characteristics of DAOs, including their decentralized governance, reliance on smart contracts operating on blockchain and the challenges they face under current UK law and underscores the necessity for legal adaptations that accommodate these innovative structures. The suggested model seeks to provide legal personhood, limited liability protection and a framework for compliance with existing laws and regulations while maintaining the core principles of decentralization and transparency. By comparative analysis of legislative approaches towards DAOs in jurisdictions such as Wyoming, Vermont and Malta, this article promotes a proactive regulatory framework for DAOs that fosters innovation and positions the UK as a leader in blockchain governance.
This paper presents a comprehensive refutation of the so-called "blockchain trilemma," a widely cited but formally ungrounded claim asserting an inherent trade-off between decentralisation, security, and scalability in blockchain protocols. Through formal analysis, empirical evidence, and detailed critique of both methodology and terminology, we demonstrate that the trilemma rests on semantic equivocation, misuse of distributed systems theory, and a failure to define operational metrics. Particular focus is placed on the conflation of topological network analogies with protocol-level architecture, the mischaracterisation of Bitcoin's design--including the role of miners, SPV clients, and header-based verification--and the failure to ground claims in complexity-theoretic or adversarial models. By reconstructing Bitcoin as a deterministic, stateless distribution protocol governed by evidentiary trust, we show that scalability is not a trade-off but an engineering outcome. The paper concludes by identifying systemic issues in academic discourse and peer review that have allowed such fallacies to persist, and offers formal criteria for evaluating future claims in blockchain research.