India's Unified Payments Interface (UPI) processes billions of transactions monthly across dozens of Payment Service Providers (PSPs). However, a structural architectural gap persists: no single entity maintains a unified, real-time, immutable view of an individual's complete financial transaction graph across competing platforms. This fragmentation has become the primary mechanism enabling complex UPI digital payment fraud and mule-account networks. Scammers easily spin up identities across multiple platforms, quickly cascade stolen funds across PSP boundaries, and discard the handles, leaving a broken trail that takes weeks for law enforcement to piece together. This white paper proposes the Universal Financial Identity (UFI) system—a sovereign, mobile-number-anchored financial identity layer that federates all of a user's UPI handles, bank accounts, and Central Bank Digital Currency (CBDC/e-Rupee) wallets into a single, immutable distributed ledger record. Key Architectural Features: Zero-Action Trigger Mechanism: The permanent UFI record (formatted as <mobilenumber>@ufi) is generated automatically in the background at the exact moment a user links any bank account to a UPI application, requiring zero user friction. Immutable Distributed Ledger Layer: Operates as a permissioned Hyperledger Fabric network managed by sovereign nodes (RBI, NPCI, and authorized banks), rendering transaction trails cryptographically non-erasable and resilient against app-side account deletions. Graph Intelligence Layer: Integrates native Neo4j graph analytics for real-time, cross-PSP fraud ring detection, rapid fund-movement velocity tracking, and automated fan-in pattern profiling. Programmable Money Integration: Leverages India's e-Rupee infrastructure to enable purpose-bound, geography-restricted, and expiry-enforced smart contract payments for public welfare disbursements (DBT) and B2B settlements. DPDP Act 2023 Compliance: Built with privacy-by-design principles, using salted SHA-256 identity tokenization and multi-tiered, consent-gated access frameworks via the Account Aggregator network. This protocol layer sits seamlessly below existing Third-Party Application Providers (TPAPs) and above core settlement switches, offering real-time cross-PSP fraud tracing and post-deletion identity recovery without introducing latency into synchronous payment pathways. This paper is released for open public community review, protocol exploration, and technical feedback prior to formal provisional patent filings with the Indian Patent Office (IPO).
Understanding and predicting how social beliefs evolve in response to events -- from policy changes to scientific breakthroughs -- remains a fundamental challenge in social science. Given LLMs' commonsense knowledge and social intelligence, we ask: Can LLMs model the dynamics of social beliefs following social events? In this work, we introduce the concept of the Social World Model (SWM), a general framework designed to capture how social beliefs evolve in response to major events. SWM learns state-transition functions for social beliefs by mining temporal patterns in social data and optimizing the evidence lower bound, without the need for explicit human annotations linking events to belief shifts, or for expensive census data. To evaluate SWM, we introduce a benchmark, SWM-bench, derived from real-world prediction markets, specifically Kalshi and Polymarket. SWM-bench includes over 12k data points for social belief prediction tasks spanning diverse domains such as politics, finance, and cryptocurrency. Our experimental results show that SWM significantly outperforms time-series foundation models, achieving state-of-the-art results on Kalshi data and demonstrating competitive performance on Polymarket data, while offering interpretable insights into the underlying mechanisms of social belief dynamics.
CIMx introduces a fundamentally different approach to identity and cryptographic integrity. Traditional cryptocurrency systems rely on external computational trust models, whereas CIMx maintains conceptual alignment with the NIST AIMS framework while employing embedded material‑based identifiers and synchronized soft/hard IQT™ coherence states to establish a deterministic, non‑replicable identity substrate. This identity layer is not blockchain, not reversible, and not computationally derived; it is a coherence‑anchored foundation that enables a standalone cryptographic architecture beyond the limits of digital‑only systems.
Open access
Physical Unclonable Functions (PUFs) and Hardware Security
Walid Mensi, Rim El Khoury, Abdullah AlGhazali, S K Kang
Abstract The increasing integration of green cryptocurrencies into financial markets raises critical questions about their effectiveness as diversification and hedging instruments. This study examines their role relative to traditional green assets, including the S&P Green Bond Index, S&P Global Clean Energy Index, and S&P ESG Leaders Index, via quantile vector autoregression (QVAR) over the period November 2017–July 2024. The results reveal a U-shaped connectedness pattern, where spillovers between green assets intensify under extreme market conditions, diminishing their diversification benefits. Green cryptocurrencies, particularly Cardano (ADA) and Stellar (XLM), function as primary transmitters of volatility, especially during extreme market conditions. Conversely, green assets, traditionally perceived as low risk, act as net receivers of volatility, failing to provide consistent downside protection and challenging their reliability in risk mitigation. Hedging analysis demonstrates limited risk mitigation from traditional green assets, with certain cryptocurrencies, such as NANO, providing superior hedging potential. These findings have important implications for investors and policymakers. Investors should reassess their reliance on traditional green assets for risk management and consider adaptive hedging strategies incorporating green cryptocurrencies. Regulators must address systemic risks associated with the growing influence of clean cryptocurrencies by implementing volatility thresholds and transparency measures. Future research should examine the regulatory impact and the evolving role of green financial instruments in sustainable portfolio management.
Current blockchain research and analytics tend to prioritize observable on-chain transactions, obscuring the processes through which cryptocurrencies are created, publicised, retained, and disposed of. In response, this paper considers distributed ledger technologies from records management principles in ISO 15489-1:2016. Setting off by specifying the parallels -- that is transactions as "records", crypto-asset units as "information assets", and blockchains as "aggregations" -- we introduce a seven-stage lifecycle for blockchain data. We apply the framework to Bitcoin, a fungible token, and a non-fungible token. On this basis, we argue that blockchain systems are not merely transactional infrastructures but record management systems with distinctive characteristics. We discuss how the on-chain/off-chain boundary and privacy-enhancing technologies can complicate lifecycle visibility, with particular relevance for crypto-crime research and investigation. As a meta-level framework, the lifecycle perspective enables positioning existing research, decomposing legal, regulatory, technological, and operational challenges by stage, and informing lifecycle-aware approaches to blockchain governance, analytics, and regulation.
Cryptocurrencies have transformed the modern financial system by introducing decentralized digital payment methods that operate without the need for traditional banking institutions. Bitcoin, introduced in 2009 by Satoshi Nakamoto, was the first successful cryptocurrency and remains the most dominant digital currency in the market. Built on blockchain technology, Bitcoin enables secure peer-to-peer transactions through cryptographic techniques and distributed ledger systems. As the popularity of cryptocurrencies has grown, large volumes of transaction data, market trends, and online user activity have created opportunities for advanced data analysis. Artificial Intelligence (AI) and Machine Learning (ML) techniques are increasingly being applied to cryptocurrency-related challenges such as price prediction, trend analysis, fraud detection, volatility forecasting, portfolio management, and mining optimization. At the same time, issues such as privacy, security, scalability, and cyber threats continue to affect the cryptocurrency ecosystem. This paper explores the relationship between Bitcoin, blockchain technology, and artificial intelligence, while examining how AIbased approaches can improve the efficiency, reliability, and security of cryptocurrency systems. It also discusses important concepts such as blocks, blockchain structure, proof of work, and the Bitcoin mining process.
Cryptocurrencies have become an important variable in the global financial system. With the maturity of blockchain technology, new applications such as stablecoins, Decentralized Finance (DeFi), Non-Fungible Tokens (NFTs) and Real-World Asset (RWA) tokenization have emerged continuously, and the crypto-asset system has gradually formed a multi-layered and multi-functional complex structure. However, as the market scale expands, problems such as price volatility risks, systemic financial risks and illegal financial activities have become increasingly prominent, prompting the continuous evolution of regulatory policies in various countries. Especially after the concentrated outbreak of multiple industry risk incidents around 2022, the global regulatory attitude has been significantly tightened, and the regulatory framework has gradually evolved from fragmentation to systematization. At the same time, Central Bank Digital Currencies (CBDCs) have entered an important stage of transition from experimental research to large-scale pilots, becoming one of the core paths for the digital transformation of national monetary systems. This paper systematically sorts out the evolutionary logic of cryptocurrencies, compares the changes in regulatory policies of major countries and regions, conducts an in-depth analysis of the development trends of CBDCs and the changes in the regulatory structure of crypto-assets based on the latest global practices from 2020 to 2026, and further explores the evolutionary direction of the asymmetric regulatory framework.
Alireza Kavousi, István András Seres, Zhipeng Wang
Regulatory compliance is increasingly mandatory for decentralized finance and privacy-enhancing technologies. Current approaches rely on binary inclusion/exclusion lists or retroactive graph analysis by centralized blockchain intelligence firms. This approach strips honest users of their financial privacy, leads to false positives and negatives, and forces decentralized platforms to bear the burden of on-chain transaction monitoring. In this work, we propose a paradigm shift: moving from platform-side surveillance to user-side provenance. We introduce Proof of Source of Funds (PoSoF), a novel cryptographic framework that shifts the burden to the user. Rather than the platform tracing funds, the user locally generates a zero-knowledge proof demonstrating that their deposit originates exclusively from a set of compliant sources. The platform is thus relieved of chain-analysis duties, requiring a constant-time, O(1) verification to enforce admission control. We formulate a unified temporal Directed Acyclic Graph (DAG) abstraction that formalizes both UTXO and account-based ledger histories within a generalized value-flow model. Users extract a compliant sub-DAG of their transaction history and utilize Incrementally Verifiable Computation (IVC) to prove rigorous state-transition predicates that protect against various attack vectors. Crucially, PoSoF provides verifiable cryptographic provenance; it guarantees the legitimacy of the funds without leaking the intermediate transaction topology, intermediary addresses, or the specific origins utilized. We formally define the security properties of PoSoF and evaluate an Ethereum-compatible prototype. Our benchmarks demonstrate that fully private, proactive compliance is highly practical, requiring only ~1.8 s to incrementally update a user's PoSoF per new transaction, and a constant-time ~1.5 ms (~800k gas) for final on-chain EVM verification.
Cryptocurrency has become a significant innovation in the digital financial system, sparking various perspectives on its compatibility with sharia. This study aims to analyze the legality of cryptocurrency from a sharia perspective, including its transaction mechanisms and investment implications. The primary focus is on examining the elements of gharar (uncertainty) and maysir (gambling), which could potentially render it impermissible under sharia. The research employs a normative analysis approach to explore contemporary scholars' views and their relevance to maqasid sharia, which emphasize the protection of wealth and societal welfare. The findings indicate that, despite cryptocurrency's benefits, such as transaction efficiency and accessibility, its high speculative risks and value uncertainty pose major obstacles to its acceptance under sharia. Therefore, clear and comprehensive regulations are needed to accommodate cryptocurrency use in sharia-compliant financial institutions without violating Islamic principles. This study provides a significant contribution to clarifying the position of cryptocurrency within the Islamic financial system and encourages the development of sharia-based regulations for digital transactions.
AI coding agents such as Claude Code and Gemini CLI increasingly extend themselves with third-party skills: markdown packages bundling natural-language instructions, executable scripts, and tool permissions. Because a skill is at once code and agent-facing instruction, it introduces a supply chain dependency whose risk is neither pure code nor pure prompt. Detection tools have never been measured against verified ground truth spanning this hybrid space, leaving their effectiveness unknown and wild-only evaluations biased. We present MalSkillBench, the first runtime-verified benchmark of malicious agent skills: 3,944 malicious skills labeled along a three-dimensional taxonomy of 108 cells. Of these, 3,214 come from a closed-loop Generate-Verify-Feedback pipeline admitting only samples whose malicious behavior fires inside a Docker sandbox under system-call monitoring and an LLM judge; we add 703 in-the-wild and 4,000 matched benign skills. Our measurements are consistent: code injection reaches 94.5% verification yield but prompt injection only 75.8%, the same fragility that later makes it hard to detect; the wild sample is narrow, dominated by one cryptocurrency-theft campaign (86.6% one behavior, 81% from two accounts) with a small but architecturally new tail attacking the agent control plane; the strongest skill-specific detector reaches 98.4% recall on code injection yet collapses on prompt-injection and agent-control attacks, and wild-only scoring swings the ranking by up to 66 recall points; supply-chain scanners and prompt-injection defenses each see only half of a skill, and no combination recovers the code-instruction relationship. Detecting malicious skills therefore requires reasoning jointly over task intent, code, and instructions. We release the dataset, pipeline, baselines, and results.
Version 2.4.0 supersedes v2.3.0 (DOI: 10.5281/zenodo.20355497) and is the sixth paper in the immo.quick Core technical series (10.5281/zenodo.19634279 → 19799660 → 19969948 → 20078326 → 20355497 → this paper). Overview This paper presents the complete institutional specification of immo.quick Core — a nine-layer deterministic compliance enforcement infrastructure operating across 47 jurisdictions. It is not a paper about technology. It is a paper about institutional legitimacy — about what it means, in a world of deterministic machines, for an institution to prove that it acted correctly. Every previous compliance document in history has answered the question: "Did we follow the process?" This paper answers a different question: "Can we prove, with mathematical certainty, that no impermissible movement produced a consequence — and that no unknown party could have caused one?" The answer is yes. The architecture enforces it. The enforcement is not optional. What v2.4.0 Adds to v2.3.0 v2.3.0 established the complete epistemological foundation, the nine-layer architecture, 15 jurisdictions, complete sector analysis, geopolitical dimensions, and the economic case. v2.4.0 adds four structural elements not present in v2.3.0: Element 1 — The Nine Gamechangers: The first systematic documentation of the capability advances that place immo.quick Core in a categorically different strategic position. These are not product features. They are architectural consequences of the nine-layer system — capabilities that emerge from the architecture and could not exist without it: EPA Offline-First Verification (SSL for compliance decisions), Bi-Temporal Legal State Replay (compliance time machine), Cross-Institution Proof Network (SWIFT for compliance verdicts), Regulatory DNA Sequencing (live law tracking to zero-downtime deploy), Intraday Settlement Finality (T+0 in under 2 seconds), Legal Pathway Optimizer (optimal jurisdiction in 9ms), Machine Law Constitution (immutable rule foundation on Ethereum and IPFS), Compliance Credit Score (compliance as a balance sheet asset), and Post-CMOS Governance Readiness (investor track — strategic roadmap signal). Element 2 — Law as Code / German Federal Government Initiative: The Bundesregierung's Digitalcheck program and the formal Law-as-Code initiative (2023–2026) represent the first sovereign government mandate for machine-readable law. immo.quick Core's Machine Law Engine is the only production implementation of this paradigm at institutional scale. This is not coincidence. It is architectural convergence. Element 3 — White House National Cybersecurity Strategy (2023) and EO 14028: The US Executive Order on Improving the Nation's Cybersecurity and the National Cybersecurity Strategy mandate zero-trust architecture, post-quantum cryptography migration, and SBOM requirements for critical infrastructure. immo.quick Core satisfies all three mandates simultaneously — by architectural construction, not by configuration. Element 4 — The Legacy Integration Protocol: Precisely how immo.quick Core connects to, validates, wraps, and structurally elevates existing compliance infrastructure without requiring system replacement. The anti-rip-and-replace architecture. Architecture Summary The nine-layer enforcement system comprises: Layer 0 (DEPE — Deterministic Execution Proof Engine, 49ms total from proposal to permanent proof), Layer 1 (PAS — Prior Admissibility Space, closed-world assumption with five mandatory conjunctive conditions), Layer 2 (BTL — Bi-Temporal Ledger, BFT quorum n=9 f=3 q=7, WORM architecture), Layer 3 (EAP — Exogenous Anchor Protocol, hardware-attested dual-channel measurement, 28ms maximum heartbeat gap), Layer 4 (SOTB — Sensor/Oracle Trust Bridge), Layer 5 (MLE — Machine Law Engine, 7-stage compilation pipeline), Layer 6 (ZKP — Zero-Knowledge Proof subsystem, Groth16/PLONK/Bulletproofs), Layer 7 (PQC — Post-Quantum Cryptography, CRYSTALS-Kyber-1024/Dilithium-3/SPHINCS+, NIST FIPS 203/204/205), Layer 8 (GLD — Governance Logic Divergence engine, maker-checker independence quantification). Document Structure Part I — The Complete Problem Statement. Part II — The Nine-Layer Architecture. Part III — The Nine Gamechangers (v2.4.0 new). Part IV — Law as Code: The German Federal Government Initiative (v2.4.0 new). Part V — The White House Cybersecurity Strategy and EO 14028 (v2.4.0 new). Part VI — Complete Legal and Jurisdictional Grounding (47 jurisdictions). Part VII — What immo.quick Core Does to Existing Systems: The Legacy Integration Protocol (v2.4.0 new). Part VIII — The Complete Platform: Every Module. Part IX — Complete Sector Analysis (Banking, Insurance, Real Estate, Government, Cloud). Part X — The Geopolitical Dimension. Part XI — The Economic Case: Monopoly, Moat, FOMO, EBITDA. Part XII — The Falsifiability Standard. Conclusion — For the Permanent Record. Key Claims Established The Boundary-Behavior Gap — the space between process documentation and governance proof — is closed by mathematical construction for the first time. The Past Irreversibility Principle: every transaction processed without immo.quick Core produces a compliance history that is permanently unrecoverable. The Falsifiability Standard: all claims in this document are falsifiable by counter-proof. No counter-proof has been produced. None is expected. Historical Compliance Failures Addressed Wirecard AG (2020, €1.9B), Libor manipulation (2012, $9B+ fines), UBS rogue trader (2011, $2.3B), Cum-Ex dividend stripping (ongoing, €55B+ EU-wide), 1MDB (2015, $4.5B), Danske Bank AML (2018, €200B flow), Credit Suisse/Archegos (2021, $5.5B). immo.quick Core produces a PAS BLOCK with DPA on every one of these at T=0 — not after the fact, not during audit, at the moment of formation. Version Series 10.5281/zenodo.19634279 → 19799660 → 19969948 → 20078326 → 20355497 → 20562464 (this paper) Related Work Economics of Deterministic Compliance Infrastructure: DOI 10.5281/zenodo.20229204. immo.quick Serverless Edition v1.1.0: DOI pending.
Pierluigi Martino, Christian Fisch, Cristiano Bellavitis
The emergence of new and powerful technologies has introduced novel players and innovative methods for financing entrepreneurial ventures. Blockchain technology is one example of a transformative technology that has significantly affected entrepreneurial finance in recent years, paving the way for a variety of alternative financial channels centered on digital technology, decentralization, and disintermediation. This chapter provides an overview of the current landscape of blockchain-based funding mechanisms by describing (1) initial coin offerings (ICOs), (2) initial exchange offerings (IEOs), (3) security token offerings (STOs), (4) non-fungible tokens (NFTs), and (5) decentralized autonomous organizations (DAOs). Initial DEX offerings (IDOs), airdrops, and cryptocurrency loans are also explored briefly. This overview aims to expand the academic understanding of the evolving blockchain-based financing landscape, helping researchers and practitioners gain insights into emerging trends, challenges, and opportunities.
Proof-of-work (PoW) blockchains rely on computational expenditure to secure a ledger supporting a native cryptocurrency. In existing systems such as Bitcoin, this expenditure is intentionally useless: the computation secures consensus but produces no external economic output. An emerging alternative -- proof of useful work (PoUW) -- enables the same computation to simultaneously secure the blockchain and generate economically valuable output. However, PoUW is often criticized on economic grounds: if the work is useful, attackers might be "paid to attack," potentially weakening security. We develop a competitive-equilibrium model of a PoUW blockchain in which compute can be allocated across pure mining, pure useful work -- instantiated as machine-learning inference -- or "duplex" work that produces both with computational overheads. We provide a complete closed-form characterization of equilibrium allocations and prices as a function of the duplex overheads and a single economic parameter -- the token-inference ratio -- measuring token adoption relative to the inference market. This characterization reveals three regimes: "Bitconia," in which the economy reduces to classical PoW; "Fortessia," in which duplex replaces mining, increasing security while useful output remains unchanged; and "Duplexia," in which token rewards subsidize inference, lowering prices and expanding inference supply. Contrary to the common strawman argument, PoUW does not make attacks economically cheap: once equilibrium prices are taken into account, the economic cost of a majority attack remains tied to the block reward. Moreover, in Duplexia, block rewards act as rebates on inference prices, generating additional socially useful computation that would not arise without the blockchain -- an expansion monotonically increasing in token adoption and technological efficiency.
This study addresses the growing importance of cryptocurrency (CC) as a financial asset and its increasing popularity as an investment option. Given the rapid expansion of research in this field, the main objective is to systematically synthesize the existing literature on cryptocurrency investment and decision-making, focusing on its intellectual structure, dominant themes, theoretical foundations, and emerging research trends. To achieve this, a hybrid review framework is employed, combining a theory-based systematic literature review with bibliometric analysis, following PRISMA guidelines. The analysis covers 1,184 articles indexed in Scopus and published between 2015 and 2025. Additionally, the study integrates the TCCMR, ADO, and PICO frameworks to provide a comprehensive, multidimensional evaluation of the selected body of literature. The findings reveal that cryptocurrency research is predominantly focused on volatility, market connectedness, portfolio diversification, and behavioral aspects of investment. The results also indicate a strong reliance on econometric and predictive modeling approaches. Emerging research directions highlight increasing attention to sustainability concerns, regulatory challenges, and the application of artificial intelligence in investment analytics. Based on these insights, the study proposes a future research agenda emphasizing theoretical integration, methodological diversification, sustainability perspectives, and decision-oriented modeling. The implications of the research are relevant for investors, regulators, and financial institutions, as they provide a deeper understanding of risks, governance, and decision-making processes in cryptocurrency markets. This study contributes to the literature by offering a comprehensive knowledge structure and research roadmap, representing one of the first attempts to combine bibliometric mapping with TCCMR, ADO, and PICO frameworks in the context of cryptocurrency investment research.
This study aims to determine whether the application of Deep Reinforcement Learning (DRL) as a specialized execution overlay can enhance pair trading in highly volatile cryptocurrency markets. Although classical implementations of the strategy have proven successful in traditional equities, they frequently exhibit rigidity and suffer from severe divergence risks when applied to high-variance environments. To address this need, this research introduces novel concepts. To construct a robust system, we developed a hierarchical "Filter-then-Rank" pair selection methodology and a proprietary "Fixed Risk, Adaptive Mean" execution model. The system employs a Proximal Policy Optimization (PPO) agent with a Long Short-Term Memory (LSTM) layer to govern execution decisions within strict deterministic risk management boundaries. Evaluated on 1-hour interval data from the Binance USD-M Futures market, the optimized RL policy achieved an out-of-sample performance that substantially outperformed the heuristic baseline. A stationary circular block bootstrap robustness check confirms that the agent's risk-adjusted outperformance is statistically significant at the 10 percent level. Although falling marginally short of the stricter 5 percent threshold, this result highlights the extreme idiosyncratic variance characteristic of digital assets. Ultimately, this thesis contributes to the quantitative finance literature by introducing a hybrid architecture that combines statistical arbitrage with DRL execution policies. Furthermore, it delivers a novel framework for safe reinforcement learning via deterministic shielding, proving that anchoring a neural policy to statistically robust boundaries successfully mitigates severe divergence risks.
Abstract : This article investigates the efficacy of implementing an AI-powered automated trading system on the blockchain using advanced machine learning algorithms and smart contract technology. The work addresses the challenges of cryptocurrency market volatility, the need for real-time decision making and the limitations of traditional trading approaches that often result in suboptimal returns and exposure to increased risk. This work develops a comprehensive trading platform that combines Long Short-Term Memory (LSTM) neural networks, Q-Learning reinforcement learning algorithms and blockchain-based smart contracts to create an autonomous, intelligent trading system. The methodology follows a multi-layered approach that integrates real-time market data collection from CoinGecko and Snowtrace APIs, advanced AI model training using TensorFlow.js, and smart contract deployment on the Avalanche C-Chain using Hardhat and OpenZeppelin libraries. LSTM model is used for price prediction and Q-Learning agent is used for trading strategy optimization, while comprehensive risk management is implemented using Value at Risk (VaR) calculations, portfolio rebalancing algorithms and automated stop-loss mechanisms. The trading execution is facilitated through direct integration with Pangolin DEX smart contracts to ensure decentralized and trustless trade execution. The performance of the system is evaluated using a sophisticated backtesting engine with Monte Carlo simulations, comparing the AI-driven strategy against traditional buy-and-hold approaches. The performance metrics used were Sharpe ratio, maximum drawdown, win rate, and total return. The AI-powered token prediction system demonstrates a superior performance due to its ability to process complex, non-linear market patterns and adapt to changing market conditions through reinforcement learning, and execute trades with minimal latency through blockchain integration. The findings are expected to provide cryptocurrency traders and institutional investors with a robust and automated trading solution that leverages the benefits of both artificial intelligence and blockchain technology for improved investment outcomes and risk management.
We present a hybrid news sentiment engine that continuously learns market sentiment from paired news headlines and concurrent asset-price snapshots without requiring any neural network training or GPU compute. The system uses a three-way ensemble combining (1) a financial-domain lexicon (FinBERT-style keyword scoring), (2) an adaptive statistical TF-IDF cluster learner that organizes headlines into semantic neighborhoods and tracks their average realized price reactions, and (3) an auto-calibrating weighting mechanism that adjusts ensemble contributions based on each signal's historical correlation with actual price movements. The engine runs on a 3-hour polling cycle from the Tradeflags NewsFeed API, which provides 22 price-snapshot fields per news item spanning equity indices (ES, NQ, SPY, DJIA, NDX, IWM), commodities (CL), and cryptocurrencies (BTC, ETH). All processing occurs at sub-second latency on a CPU-only server at effectively zero marginal cost per analytic cycle. We compare our approach against established methods -- FinBERT, GPT-based scoring, VADER, and commercial sentiment APIs -- across dimensions of cost, latency, accuracy, and adaptability. Our statistical cluster learner, which adapts to changing market regimes without retraining, represents a novel contribution not found in existing sentiment systems.
We present Veil, a decentralized messaging protocol that unifies metadata protection, spam prevention, and offline message delivery through a single mechanism: Proof-of-Relay. In Veil, sending a message requires a zero-knowledge proof that the sender has faithfully relayed messages for others through a stratified mixnet. The relay work itself constitutes the anonymizing infrastructure, eliminating the need for cryptocurrency tokens, blockchain consensus, or trusted third parties. We make three contributions. First, we prove that bilateral non-transferable credits with epoch-bound nullifiers achieve incentive compatibility without a global state, a general result applicable beyond messaging to any peer-to-peer system requiring fair exchange. Second, we establish a Growth-Isolation Impossibility theorem showing that no CRDT merge function can simultaneously resist inflation and guarantee completeness for monotonically growing verifiable evidence, and present a resolution via penalty-log CRDTs with locally-computed growth. Third, we prove a constructive adversary bound: any adversary controlling a fraction f of relay nodes necessarily contributes to sender anonymity entropy, while the individual deanonymization probability remains bounded, ensuring that adversarial participation requires a productive contribution while individual targeting remains negligible. Veil requires no economic investment to participate; privacy is earned through device contribution alone. We analyze the protocol's security under a global passive adversary with formal indistinguishability definitions, bound Sybil infiltration under depth-limited social vouching, and demonstrate mobile feasibility with verified constraint counts via Nova folding over BabyJubjub.
The paper examines the volatility spillover effects and long-term relationship between cryptocurrencies and traditional financial markets in Türkiye using BEKK-GARCH and DCC-GARCH models. It analyses the perception of crypto assets as a “digital safe haven” in an economy marked by high inflation, exchange rate fragility, and financial uncertainty. Using monthly price data for Bitcoin, Ethereum, BIST-100, and Republic Gold from January 2010 to February 2025, the study applies unit root tests, Johansen cointegration, ARDL bounds, and Engle-Granger tests. Results show no long-term price cointegration, but Bitcoin and Ethereum returns are strongly correlated, with DCC-GARCH results showing a dynamic correlation above 50%, while gold and BIST-100 correlate weakly or negatively. BEKK-GARCH highlights significant volatility transmission from Bitcoin to Ethereum, with BIST-100 maintaining persistent volatility. The study concludes that crypto and traditional markets in Türkiye are not integrated long-term, but short-term interactions exist at the return level, with implications for portfolio diversification and financial stability.
Abstract This study investigates racial and ethnic disparities in cryptocurrency (crypto) ownership using data from the 2021 Survey of Household Economics and Decision-Making (SHED). While prior research has explored general determinants of crypto market participation, such as risk tolerance, financial literacy, and investment experience, this study specifically focuses on how these factors differ across racial groups. Using logistic regression and Fairlie decomposition analysis, we find that Black respondents are significantly more likely to invest in crypto compared to White respondents. Key contributors to this disparity include age, financial literacy, risk tolerance, and stock ownership. Notably, while some factors, such as younger age and higher risk tolerance, narrow the participation gap, others, including differences in total savings and stock ownership, widen it. These findings highlight the need for targeted financial education and inclusive investment policies to promote equitable participation in emerging digital financial markets. Implications for financial literacy, consumer protection, and broader economic policy are discussed.
Τα τελευταία χρόνια, οι Αποκεντρωμένες Εφαρμογές (Decentralized Applications - DApps) γνωρίζουν σημαντική ανάπτυξη, και η ικανότητά τους να διαχειρίζονται ψηφιακά περιουσιακά στοιχεία υψηλής αξίας έχει οδηγήσει σε σημαντική καινοτομία σε τομείς όπως η αποκεντρωμένη χρηματοοικονομική (Decentralized Finance - DeFi), η διακυβέρνηση (governance) και η διαχείριση της εφοδιαστικής αλυσίδας (supply chain management), με τη δημιουργία διαφόρων δικτύων blockchain για την κάλυψη της ζήτησης για τέτοιες υπηρεσίες. Παράλληλα, έχουν αναπτυχθεί διάφορες μεθοδολογίες για την προστασία αυτών των δικτύων από κακόβουλους παράγοντες (malicious actors) που επιχειρούν να εκμεταλλευτούν αδυναμίες (vulnerabilities) που υπάρχουν στα έξυπνα συμβόλαια (smart contracts) τα οποία εκτελούν μια προκαθορισμένη επιχειρηματική λογική (business logic), με σκοπό να κλέψουν μεγάλα χρηματικά ποσά μέσω αυτών. Αν και το οικοσύστημα του Ethereum επωφελείται από μια ώριμη σουίτα εργαλείων ασφαλείας, αυτά είναι κυρίως σχεδιασμένα για τον εντοπισμό συντακτικών αδυναμιών (syntactic vulnerabilities), παραλείποντας συχνά σφάλματα που προκύπτουν από την απόκλιση μεταξύ του επιδιωκόμενου σχεδιασμού ενός έξυπνου συμβολαίου και της υλοποίησής του στην αλυσίδα (on-chain implementation), επιτρέποντας έτσι στους επιτιθέμενους να χειραγωγήσουν τη λειτουργικότητα του συμβολαίου για κακόβουλο όφελος. Νέα δίκτυα blockchain και γλώσσες προγραμματισμού, όπως το δίκτυο Sui και η γλώσσα του Sui Move, έχουν δημιουργηθεί προσφέροντας νέες δυνατότητες και χαρακτηριστικά, αλλά ταυτόχρονα εισάγουν νέες κατηγορίες κινδύνου. Ορισμένα παραδείγματα είναι η διαρροή δυνατοτήτων (capability leakage) και οι παραβιάσεις του προτύπου μάρτυρα (witness pattern violations), οι οποίες είναι αόρατες στις παραδοσιακές ταξινομίες ασφαλείας που βασίζονται στο Ethereum, λόγω των διαφορετικών υποδομών και προγραμματιστικών μοντέλων. Η πρόληψη τέτοιων επιχειρηματικών αδυναμιών (business vulnerabilities) απαιτεί κατάλληλη τυπική μοντελοποίηση και επαλήθευση (formal modeling and verification) της επιδιωκόμενης επιχειρηματικής διαδικασίας εντός των έξυπνων συμβολαίων, διασφαλίζοντας ότι όλες οι πιθανές αλληλεπιδράσεις παραμένουν συνεπείς με την αναμενόμενη συνολική συμπεριφορά του συστήματος. Η παρούσα έρευνα αντιμετωπίζει αυτό το πρόβλημα αναπτύσσοντας ένα τυπικά θεμελιωμένο, καθοδηγούμενο από οντολογίες πλαίσιο ανάλυσης ασφάλειας (formally grounded, ontology-driven security analysis framework) ειδικά για τη γλώσσα Sui Move, κωδικοποιώντας τις σημασιολογικές σχέσεις μεταξύ των δομών κώδικα (code constructs) της Sui Move, των προτύπων ασφαλείας (security patterns) και των κατηγοριών αδυναμιών. Για την επίτευξη αυτού του στόχου, η παρούσα διατριβή ακολουθεί τη μεθοδολογία Design Science Research (DSR), προκειμένου να γεφυρώσει το χάσμα μεταξύ της αρχιτεκτονικής πρόθεσης υψηλού επιπέδου (το «γιατί» - the why) και των ελαττωμάτων κώδικα χαμηλού επιπέδου (το «πώς» - the how). Τα συμπεράσματα που προέκυψαν από μια συστηματική μελέτη χαρτογράφησης (systematic mapping study) και τη σύγκριση των γλωσσών προγραμματισμού Solidity και Sui Move χρησιμοποιούνται για τη δημιουργία δύο τεχνουργημάτων (artifacts): 1) ενός οντολογικού πλαισίου έξι επιπέδων (six-layer ontological framework) για τη Sui Move και 2) ενός εργαλείου ανάλυσης (Sui Move Analyzer). Όσον αφορά το οντολογικό πλαίσιο, περιλαμβάνονται η χαρτογράφηση γραμματικής (grammar mapping), η ταξινόμηση ασφαλείας, τα αρχιτεκτονικά πρότυπα και η τυπική μοντελοποίηση συμπεριφοράς (formal behavioral modeling), σε συνδυασμό με τη δημιουργηθείσα ταξινόμηση Sui-Unified Weakness Classification (SUWC), η οποία κατηγοριοποιεί τα ελαττώματα που σχετίζονται ειδικά με την πλατφόρμα (platform-specific defects) σε τέσσερις ομάδες, ευθυγραμμισμένες με μια βιβλιοθήκη τεσσάρων επαληθευμένων σχεδιαστικών προτύπων ασφαλείας (security design patterns) της ενσωματωμένης οντολογίας. Όσον αφορά το δεύτερο τεχνούργημα, αυτό αναπτύχθηκε για να αξιολογήσει την πρακτική χρησιμότητα του οντολογικού πλαισίου, χρησιμοποιώντας μια αρχιτεκτονική διπλής ροής (dual-pipeline architecture) που συνδυάζει την παραδοσιακή εξαγωγή ευρετικών κανόνων (heuristic extraction) με την οντολογική συλλογιστική που βασίζεται σε SPARQL (SPARQL-based ontological reasoning). Χρησιμοποιώντας αυτή τη μεθοδολογία, ο αναλυτής μπορεί να εντοπίσει κινδύνους σε σημασιολογικό επίπεδο (semantic-level risks), ενώ παράλληλα βοηθά τους προγραμματιστές προτείνοντας αυτοματοποιημένες αποκαταστάσεις βασισμένες σε πρότυπα (pattern-based remediations), οι οποίες βασίζονται σε καθιερωμένα παραδείγματα ασφάλειας (security paradigms). Η αξιολόγηση των τεχνουργημάτων ακολουθεί το Framework for Evaluation in Design Science (FEDS), συνδυάζοντας τεχνητή αθροιστική αξιολόγηση (artificial summative evaluation) μέσω ειδικά κατασκευασμένων συμβολαίων με γνωστή αντικειμενική αλήθεια (ground truth), και φυσιοκρατική αθροιστική αξιολόγηση (naturalistic summative evaluation) μέσω της ανακατασκευής μιας πραγματικής εκμετάλλευσης (exploit reconstruction), προκειμένου να διασφαλιστεί τόσο η εσωτερική όσο και η εξωτερική εγκυρότητα (internal and external validity). Σε 14 συμβόλαια Sui Move, 42 περιπτώσεις δοκιμών (test cases) και ένα σενάριο εκμετάλλευσης (exploit scenario), χρησιμοποιούνται 13 ποσοτικές μετρικές που καλύπτουν την ορθότητα (precision, recall, F1-score), την κάλυψη (taxonomy and pattern completeness) και την πρακτική χρησιμότητα (false-positive rate, runtime performance). Η εγκυρότητα και των δύο τεχνουργημάτων αξιολογείται επιπλέον σε πέντε διαστάσεις (μέσου, τεχνική, σχεδιασμού, σκοπού και γενίκευσης - instrument, technical, design, purpose, and generalization), επιβεβαιώνοντας ότι το πλαίσιο αποδίδει σταθερά σε όλες τις στοχευμένες διαστάσεις εγκυρότητας και πληροί την απαιτούμενη αυστηρότητα (rigor) για να κλείσει επαρκώς τον κύκλο DSR.
Reza Abtahi, Sayyed Ahmad Abtahi, Burkhard Stiller
This paper proposes an extensible taxonomy for profiling cryptocurrency arbitrage strategies across on-chain and offchain environments. Organized around ten analytical dimensions, the framework supports structured classification and comparison of arbitrage mechanisms with respect to execution setting, underlying drivers, capital requirements, temporal characteristics, risk exposure, and contextual overlays. Two representative cases, Cross-Rollup Arbitrage and Cash-and-Carry Arbitrage, illustrate its use in comparing structurally different strategies within a common schema. By offering a shared vocabulary across blockchain and financial perspectives, the taxonomy helps reduce conceptual fragmentation and supports more systematic analysis of cryptocurrency arbitrage strategies.
Este Trabajo Fin de Grado presenta un análisis criptográfico y matemático de la arquitectura de Monero, una criptomoneda diseñada con la privacidad como propiedad fundamental de su protocolo. El estudio comienza con la formalización de los fundamentos algebraicos que conforman el sistema, como las curvas de Edwards retorcidas y la completitud de su ley de grupo, característica que contribuye a mitigar vulnerabilidades asociadas a ataques de canal lateral. Sobre esta base se estudia el protocolo Ring Confidential Transactions (RingCT), núcleo de los mecanismos de privacidad de la red. En particular, se analizan las direcciones sigilosas (stealth addresses), que garantizan la no vinculabilidad de los receptores mediante intercambios Diffie–Hellman sobre curvas elípticas; las firmas de anillo CLSAG y las imágenes de clave, que proporcionan anonimato al emisor y previenen el doble gasto; y los compromisos de Pedersen, utilizados para ocultar las cantidades transferidas. Asimismo, se estudian las pruebas de rango Bulletproofs+, destacando su función en la reducción del tamaño de las transacciones mediante argumentos de producto interno. Finalmente, se examinan diversas vulnerabilidades históricas y técnicas de análisis de trazabilidad aplicadas a Monero, evaluando el grado de resistencia que ofrece el protocolo frente a distintos ataques. Los resultados ponen de manifiesto cómo la integración de herramientas avanzadas de criptografía de clave pública, pruebas de conocimiento cero y estructuras algebraicas sobre curvas elípticas permite construir un sistema financiero con garantías de privacidad, seguridad y fungibilidad. ABSTRACT This Bachelor’s Thesis presents a cryptographic and mathematical analysis of the architecture of Monero, a cryptocurrency designed with privacy as a fundamental property of its protocol. The study begins with the formalization of the algebraic foundations underlying the system, including twisted Edwards curves and the completeness of their group law, a feature that helps mitigate vulnerabilities associated with side-channel attacks. Building upon this mathematical framework, the Ring Confidential Transactions (RingCT) protocol, which forms the core of Monero’s privacy mechanisms, is examined. In particular, the thesis analyzes stealth addresses, which ensure receiver unlinkability through Diffie–Hellman key exchanges over elliptic curves; CLSAG ring signatures and key images, which provide sender anonymity and prevent double-spending; and Pedersen commitments, which are used to conceal transferred amounts. Furthermore, Bulletproofs+ range proofs are studied, highlighting their role in reducing transaction size through efficient inner-product arguments. Finally, several historical vulnerabilities and traceability analysis techniques applied to Monero are reviewed in order to evaluate the protocol’s resistance against different types of attacks. The results demonstrate how the integration of advanced public-key cryptography, zero-knowledge proofs, and algebraic structures based on elliptic curves makes it possible to build a financial system with strong guarantees of privacy, security, and fungibility.