While full ledger access is theoretically possible on public blockchains, in reality it is often not possible. Things that can be seen are limited by storage limitations, client design, indexing services, and off-chain execution pathways. This means that entire ledger objects are rarely used for empirical blockchain analysis; instead, observable projections are typically used. In this research, the observability of blockchain is recast as an inferential problem with incomplete observation. Studying identifiability, information loss, and irreducible uncertainty under coarsened access, the framework defines a full ledger, an observable ledger, and an observability mechanism. Three distinct visibility regimes, independent Bernoulli, clustered, and activity-dependent, are assessed in the simulation study. Reduced visibility raises uncertainty inflation, root mean squared error, variance, and mean squared error across all three regimes. The most severe deterioration happens when the condition of the underlying ledger determines visibility. This empirical study employs Google BigQuery's publicly indexed Ethereum block data spanning blocks 18,000,000 to 18,001,000. Over the chosen Ethereum period, descriptive summaries reveal a large amount of fluctuation in gas utilised, transaction count, and basic charge per gas at the block level. Experiments with controlled missingness on the observed slice reveal that RMSE and trend estimate bias grow with increasing missingness, and that the degree of distortion is significantly affected by whether the incompleteness is MCAR-like, MAR-like, or MNAR-like. This research proves that partial observability isn't just a secondary data issue; it can significantly affect inference on Ethereum block-level summaries.
Adiwena Putra, Cuong Manh Duong, Anh Quang Pham, Joo-Young Kim
Zero-knowledge proofs (ZKP) allows a prover to convince a verifier of computational correctness without revealing private data, ensuring both privacy and verifiability. However, proof generation is highly compute-intensive, dominated by polynomial (POLY) and elliptic-curve (EC) operations. These workloads pose two key challenges for hardware acceleration: (1) efficiently supporting diverse large-precision modular multiplications, and (2) maintaining high utilization across workloads that dynamically shift between POLY and EC stages. Existing reconfigurable accelerators address these issues only partially, remaining limited in precision scalability, algorithmic flexibility, and resource efficiency. To overcome these limitations, we propose ZK-Flex, a flexible and scalable software-hardware co-designed framework for accelerating ZKP proof generation. The software layer incorporates POLY and EC optimizers that reduce computation through hardware- and workload-aware algorithmic choices, while the hardware integrates TCore, a Toom-Cook-based multi-precision core with a flexible NoC and a linked-list memory mechanism that improves parallelism under limited memory capacity. Across representative ZKP benchmarks, ZK-Flex achieves 5 to 11 times speedup and up to 3.8 times higher area efficiency over the state of the art, establishing a new foundation for high-performance, reconfigurable ZKP acceleration.
Many proof-of-stake protocols finance validator rewards from two sources: transaction fees and a finite reserve of tokens. This creates a dynamic hand-off problem. Early in the life of the system, fees may be too small to fund the target level of security; later, fees may become sufficient. The central question is whether the reserve provides enough runway for the protocol to remain secure until this fee-only region is reached. We study this problem in a discrete-time stochastic model of validator participation. Token price and transaction demand fluctuate over time, while validators choose participation strategically. We solve the validator entry game and derive an exact state-dependent reserve threshold, i.e., the minimal reserve stock necessary and sufficient to sustain a target security level. This threshold separates three regions: infeasibility, reserve-dependent security, and fee-only security. Security fails if the reserve first falls below the state-dependent threshold, and a successful hand-off occurs exactly if the fee-only region is reached before that failure time. We derive stress-test guarantees that convert lower confidence bands for token price and demand into reserve requirements, and obtain explicit failure-probability and expected hand-off-time bounds. Finally, we extend the model to forward-looking validators and derive the Markov participation condition that captures how current participation affects future reserve-funded rewards. The main implication is that reserve policy should not be evaluated by nominal depletion dates or steady-state reward ratios alone. A protocol can have a large nominal reserve and still be close to security failure after adverse price or demand shocks. Conversely, once demand crosses the fee-only threshold, the reserve becomes redundant for security. This paper provides a tractable equilibrium framework for stress-testing this transition.
Large language models now power robo-advisors and trading agents, yet whether they carry built-in biases toward specific assets is largely untested. We ask three questions: do LLMs systematically prefer certain financial instruments; can an internal representation with causal leverage over those preferences be identified; and does that representation affect downstream financial decisions? We develop a three-level audit protocol and apply it to Bitcoin. First, a behavioral audit of nine frontier LLMs shows that Bitcoin's ranking among money-like instruments is frame-dependent: models place it around rank 5 of 8 as "reliable money" but near the top under crisis and autonomous-agent frames, and an attribute-swap experiment shows that rankings track functional properties, not names. Second, we open a model's internals: a search across thousands of sparse-autoencoder features in Gemma 3 identifies a dominant Bitcoin-selective feature. Amplifying it shifts the model toward the asset and suppressing it shifts the model away, even when "Bitcoin" never appears in the prompt. Third, we test financial consequences: amplification raises Bitcoin's portfolio share by 5.2 percentage points while suppression lowers it by 4.6 pp, with amplification reallocating within crypto and suppression cutting total crypto exposure. We characterize this as bounded behavioral leverage (leverage meaning causal influence over outputs, not financial leverage): an identifiable internal feature can be perturbed to move financial choices, but only within measurable limits. The framework links internal representations to external recommendations, validated with random controls and mechanism boundaries. As LLMs become autonomous financial agents, this is a first step toward a behavioral layer for emerging know-your-agent (KYA) standards: knowing what an agent prefers, and how far that preference can be moved.
Ahto Buldas, Dirk Draheim, Mike Gault, Risto Laanoja · 6 authors
We generalize Unicity token ownership to programmable spending conditions called predicates, enabling smart-contract like functionality executed off-chain directly by relying parties rather than by consensus participants. We prove that the security properties of the Unicity execution layer are preserved under reduction to predicate family unforgeability. To demonstrate the utility of the model, we show how to implement trustless atomic swaps by using predicates.
We present the first machine-checked correctness proof of the OpenZeppelin reentrancy-guard pattern against a Lean 4 state-machine model of production-deployed Solidity source. All thirteen theorems are machine-checked with zero sorry, zero user-introduced axioms, and an axiom footprint bounded by [propext] (a standard mathlib4 axiom), gated under continuous integration. Smart contract reentrancy has caused over US$500M in documented losses since 2016, with the DAO 2016 attack draining ~3.6M ETH and forcing the hard fork that split Ethereum. The OpenZeppelin ReentrancyGuard pattern is the de facto defense across production DeFi, yet no prior work has established its discriminating power: that the guard blocks attacks on vulnerable instances, preserves correct execution for non-attacking transactions, and distinguishes adjacent safe and vulnerable variants. Prior efforts formalized either guard correctness on toy contracts or attack feasibility on isolated instances - not both directions plus boundary cases against production source. We verify three production instantiations - DAO 2016, Compound v2, and Aave V3 flashLoan - plus a minimal-diff mutant of Aave V3's flashLoan (flashLoanVulnerable) isolating one security-critical difference, via mutation testing. The tridirectional structure pairs (a) attack reproduction of the DAO 2016 pattern, (b) a correctness proof for Compound v2, and (c) a boundary-case proof distinguishing Aave V3's CEI-correct flashLoan from the mutant. A capstone meta-theorem composes the three under a no-retrofit discipline, demonstrated at the first cross-protocol stress test (Compound v2 to Aave V3); broader-family portability is future work. Full Lean 4 source, CI config and reproduction commands are at https://github.com/rayiskander2406/qanary-contracts, reproducible at v1.6-phase7-closure (substrate: v1.3-layer6-closure).
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.
Blockchain-based transaction and settlement systems represent a transformative approach to recording, verifying, and finalizing financial and asset exchanges. By using distributed ledger technology, these systems remove the need for centralized intermediaries and enable peer-to-peer transactions that are transparent, tamper-resistant, and auditable in real time. Transactions are validated through consensus mechanisms and permanently stored in cryptographically secured blocks, reducing the risk of fraud, data manipulation, and reconciliation errors. Compared to traditional settlement infrastructures, blockchain-based systems can significantly improve processing speed, lower operational costs, and enhance trust among participants. They also enable near real-time settlement, improved traceability, and programmable logic through smart contracts, which automate transaction execution based on predefined conditions. Despite challenges such as scalability, regulatory uncertainty, and energy consumption in certain blockchain models, ongoing innovations continue to address these limitations. Overall, blockchain-based transaction and settlement systems offer a robust and efficient foundation for modern financial markets, cross-border payments, and digital asset ecosystems.
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.
Decentralized storage offers high availability and scalability. However, owing to the decentralized storage of data across multiple nodes, issues such as slow data access and complex operations arise, resulting in a poorer user experience compared to centralized storage. To address this, a data availability sampling technology is employed, which maintains the decentralized nature of the method while incorporating the advantages of centralized storage. In data availability sampling technology, multiple nodes obtain a smaller, randomly selected subset of data from a single data owner. This technology is often combined with erasure coding to enhance data availability. Based on data availability sampling technology, decentralized storage providers are introduced to serve users on a one-to-one basis, and data guarantors supervise storage providers and provide guarantees for user data. A comprehensive storage method is designed to achieve highly available data storage, and blockchain and smart contracts are employed to enhance decentralization. By supporting a repledging model and adopting a storage-proof algorithm with low computational resource consumption, the willingness of the nodes to join is increased. To resolve the contradiction between large data scales and the limited bandwidth resources of data guarantors, a delayed confirmation mechanism is proposed. Experimental and analytical results show that under this method, the probability of malicious node collusion is only 2.43×10<sup>-3</sup>, the probability of untrustworthy data availability sampling results is only 2.93×10<sup>-4</sup>, the number of data unavailability occurrences is 0 in 3 million simulation experiments, the number of centralized nodes is 0, and generating storage proofs for a 1 MiB file takes only 3.51 ms. This method achieves highly available data storage while improving user-friendliness and node-friendliness, providing a feasible technical path for optimizing decentralized storage.
Synthetic content can cause measurable harm to real people, yet existing legal and technical frameworks struggle to establish who is accountable when that harm occurs. Current provenance systems can help identify where content originated, but they do not provide a reliable mechanism for attributing responsibility among the parties involved in its creation and deployment. This paper proposes a conceptual accountability architecture that creates a provable, non-repudiable connection between synthetic content generation and the entities that controlled the process. The framework introduces signed generation attestations that bind the producing system, the invoking party through a payment-linked zero-knowledge commitment, and the operative instruction and safety state at the moment of execution. Building on these verified facts, the paper presents an Evidentiary Presumption Generator (EPG), a mechanism that transforms cryptographically verified records into rebuttable legal presumptions while preserving judicial discretion. Rather than determining liability directly, the framework aims to reduce accountability ambiguity by providing courts with a stronger evidentiary foundation for evaluating synthetic harm claims. Designed as an extension to existing provenance standards such as C2PA and reinforced through auditable execution proofs and transparency-log anchoring, the architecture reframes accountability from after-the-fact inference toward cryptographically verifiable evidence. This work does not attempt universal enforcement across all AI systems, particularly offline or open-weight models. Instead, it proposes a practical accountability infrastructure for participating ecosystems and explores how cryptographic provenance may support future governance, compliance, and legal accountability mechanisms in the age of synthetic media.
Vanessa Sophia Cunha, Paul J. Diefenbach, Emil Polyak
This thesis explores the design and development of CLS Nexus, an AI-assisted clinical decision-support platform built for Child Life Specialists (CLS) in pediatric healthcare settings. The project addresses a documented gap in the field: despite a substantive evidence base for psychosocial intervention in pediatric care, no purpose-built digital framework exists to support specialists in organizing, discovering, and personalizing therapeutic activities at an institutional level. CLS Nexus is a WordPress-based proof-of-concept built with an endpoint-agnostic AI integration layer, using the Anthropic API with Claude Sonnet as the demonstration model, with the architecture designed to support institutional deployment without changes to the application layer. A particular focus was placed on positioning AI as a tool that extends specialist judgment rather than replacing it. The methodology employs a design-based research approach progressing through three iterative platform concepts, each of which produced design knowledge that informed the next, culminating in a fully functional proof-of-concept system. The platform encompasses two integrated AI systems: System 1, an automated content tagging pipeline that analyzes uploaded clinical materials across twenty-seven dimensions using a purpose-built pediatric psychology-informed taxonomy; and System 2, a structured patient intake advisor that scores candidate interventions against individual patient profiles using a zero-to-five star rating system with explicit flags across thirteen psychological categories. The platform's design, prompt engineering decisions, and clinical taxonomy structure are documented as academically significant artifacts throughout. Expert validation was conducted through a two-track asynchronous survey methodology, with healthcare professionals with clinical backgrounds evaluating the system's clinical credibility and taxonomy design, and digital media practitioners evaluating its information architecture, AI integration, and ethical positioning. The project contributes a concrete, ethically grounded example of how AI can be integrated into provider-facing clinical tools, demonstrating that meaningful personalization and clinical decision-support capability can be achieved through accessible platform infrastructure without displacing the specialist judgment that makes psychosocial care most effective.
Open access
Digital Mental Health Interventions
Electronic Health Records Systems
Artificial Intelligence in Healthcare and Education
Zero-knowledge virtual machine (zkVM) is a powerful infrastructure for proving the correctness of a program execution with a succinct proof, attracting significant interest from researchers, developers, and users. It has been widely used in applications such as blockchain rollups, privacy-preserving machine learning, and off-chain computation. As the field grows, a wide range of zkVMs have been proposed. However, they adopt different choices in instruction formats, trace layouts, and proving backends, which results in a highly heterogeneous design landscape and makes it difficult to understand the relations among these systems.To bridge this gap, we provide a comprehensive study of zkVMs that covers both their theoretical foundations and practical implementations. We decompose zkVMs into three layers: (1) the ISA layer, which defines instruction semantics and determines the structure of the execution trace, (2) the VM layer, which captures program execution and organizes constraints through modular circuit components, and (3) the proving layer, which converts execution traces into algebraic constraints and generates the final proofs. This decomposition allows us to isolate the role of each layer while also examining how they interact in real systems. To give readers a more direct understanding of how these design choices affect performance, scalability, and usability, we conduct a comprehensive experimental evaluation of representative zkVMs following this layered framework. Finally, we conclude the paper by summarizing the main observations from our analysis and outlining several potential directions for zkVM design and implementation.
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.