In response to the difficulty of balancing privacy protection and system efficiency in energy data trading, this article analyzes the limitations of existing methods: static pseudonym mechanisms can easily lead to long-term identity link risks, traditional zk-SNARKs schemes have high computational overhead, and Raft consensus mechanisms lack robustness in adversarial environments. To address the above challenges, an integrated privacy protection scheme based on dynamic pseudonyms and lightweight zk-SNARKs is proposed. This scheme breaks the temporal correlation of transactions through a dynamic pseudonym generation mechanism, uses blockchain level batch processing proofs to reduce the computational and storage overhead of zero knowledge proofs, and introduces an LSTM based node health assessment model and incremental log synchronization mechanism to enhance the error tolerance and synchronization efficiency of the Raft consensus algorithm. The experimental results show that the proposed scheme outperforms traditional methods in terms of privacy, transaction processing performance, and system availability, effectively achieving a balance between privacy protection and operational efficiency, and providing a feasible technical path for energy data trading.
(English) The accelerated adoption of digital services has highlighted the need for trust-minimized computation, where parties can verify the correctness of computations without re-executing them or revealing sensitive data. Zero-knowledge proof systems, including SNARKs and STARKs, provide cryptographic guarantees of correctness, privacy, and succinct verifiability, enabling applications in scalable blockchains, privacy-preserving identity systems, and verifiable federated learning. This thesis addresses key inefficiencies in constraint-based zero-knowledge proof systems at the arithmetization layer. The research focuses on two complementary problems: optimizing binary comparisons within Rank-1 Constraint Systems (R1CS), and extending the expressiveness of STARKs through an Extended Algebraic Intermediate Representation (eAIR). The first contribution presents a weighted accumulation method for implementing strict binary comparisons in R1CS. Traditional approaches generate a large number of constraints due to the lack of native comparison and control-flow operations in the R1CS model, forcing costly bit-by-bit decompositions and creating performance bottlenecks. The proposed weighted accumulation method significantly reduces constraint overhead without compromising system security or correctness, achieving substantial efficiency improvements over the lexicographic approach. The second contribution introduces the eSTARK protocol, which extends standard STARKs by enabling the concise handling of complex constraints such as lookups, permutations, and copy constraints. These operations are difficult to encode efficiently in standard AIR. The eSTARK protocol integrates vector commitment arguments and polynomial optimizations, providing a flexible and user-friendly framework for representing a broader class of computations without introducing unnecessary arithmetization overhead. Both contributions address practical limitations of current zero-knowledge proof systems. The first focuses on reducing constraint complexity for common operations, while the second expands the expressiveness of the proof system itself. Together, they demonstrate the importance of arithmetization-level optimizations for improving the efficiency and usability of zero-knowledge proofs. (Català) L’adopció accelerada de serveis digitals ha posat en relleu la necessitat de computació amb confiança mínima, on les parts poden verificar la correcció dels càlculs sense haver de tornar-los a executar ni revelar dades sensibles. Els sistemes de proves de coneixement zero, incloent-hi SNARKs i STARKs, ofereixen garanties criptogràfiques de correcció, privacitat i verificabilitat concisa, permetent aplicacions en blockchains escalables, identitat preservant la privacitat i aprenentatge federat verificable. Aquesta tesi aborda les principals ineficiències en els sistemes de proves ZK basats en restriccions a la capa d’aritmetització. La recerca se centra en dos problemes complementaris: optimitzar les comparacions binàries dins dels Rank-1 Constraint Systems (R1CS) i ampliar l’expressivitat dels STARKs mitjançant una Representació Intermèdia Algebraica Estesa (eAIR). La primera contribució presenta un mètode d’acumulació ponderada per implementar comparacions binàries estrictes en R1CS. Els enfocaments tradicionals generen un gran nombre de restriccions a causa de la manca d’operacions natives de comparació i de control de flux en el model R1CS, obligant a descomposicions costoses bit a bit i creant colls d’ampolla en el rendiment. El mètode d’acumulació ponderada proposat redueix de manera significativa la sobrecàrrega de restriccions sense comprometre la seguretat o la correcció del sistema, aconseguint millores substancials d’eficiència respecte a l’enfocament lexicogràfic. La segona contribució introdueix el protocol eSTARK, que amplia els STARKs estàndard permetent la gestió concisa de restriccions complexes com ara lookups, permutacions i restriccions de còpia. Aquestes operacions són difícils d’encodear de manera eficient en l’AIR estàndard. El protocol eSTARK integra arguments de compromís vectorial i optimitzacions polinòmiques, oferint un marc flexible i fàcil d’utilitzar per representar una classe més àmplia de càlculs sense introduir sobrecàrrega d’aritmetització innecessària. Totes dues contribucions aborden limitacions pràctiques dels sistemes de proves de coneixement zero actuals, amb la primera centrada en reduir la complexitat de restriccions per a operacions comunes i la segona en expandir l’expressivitat del sistema de proves en si. Conjuntament, demostren la importància de les optimitzacions a nivell d’aritmetització per millorar l’eficiència i la usabilitat de les proves de coneixement zero. (Español) La adopción acelerada de servicios digitales ha puesto de relieve la necesidad de computación con confianza mínima, donde las partes pueden verificar la corrección de los cálculos sin tener que volver a ejecutarlos ni revelar datos sensibles. Los sistemas de pruebas de conocimiento cero, incluyendo SNARKs y STARKs, ofrecen garantías criptográficas de corrección, privacidad y verificabilidad concisa, permitiendo aplicaciones en blockchains escalables, identidad preservando la privacidad y aprendizaje federado verificable. Esta tesis aborda las principales ineficiencias en los sistemas de pruebas ZK basados en restricciones a la capa de aritmetización. La investigación se centra en dos problemas complementarios: optimizar las comparaciones binarias dentro de los Rank-1 Constraint Systems (R1CS) y ampliar la expresividad de los STARKs mediante una Representación Intermedia Algebraica Extendida (eAIR). La primera contribución presenta un método de acumulación ponderada para implementar comparaciones binarias estrictas en R1CS. Los enfoques tradicionales generan un gran número de restricciones debido a la falta de operaciones nativas de comparación y de control de flujo en el modelo R1CS, obligando a descomposiciones costosas bit a bit y creando cuellos de botella en el rendimiento. El método de acumulación ponderada propuesto reduce de manera significativa la sobrecarga de restricciones sin comprometer la seguridad o la corrección del sistema, logrando mejoras sustanciales de eficiencia respecto al enfoque lexicográfico. La segunda contribución introduce el protocolo eSTARK, que amplía los STARKs estándar permitiendo la gestión concisa de restricciones complejas como lookups, permutaciones y restricciones de copia. Estas operaciones son difíciles de codificar de manera eficiente en el AIR estándar. El protocolo eSTARK integra argumentos de compromiso vectorial y optimizaciones polinómicas, ofreciendo un marco flexible y fácil de usar para representar una clase más amplia de cálculos sin introducir sobrecarga de aritmetización innecesaria. Ambas contribuciones abordan limitaciones prácticas de los sistemas de pruebas de conocimiento cero actuales, con la primera centrada en reducir la complejidad de restricciones para operaciones comunes y la segunda en expandir la expresividad del sistema de pruebas en sí. Conjuntamente, demuestran la importancia de las optimizaciones a nivel de aritmetización para mejorar la eficiencia y la usabilidad de las pruebas de conocimiento cero.
The Internet of Vehicles (IoV) is changing the contemporary mobility, as it allows real-time communication between vehicles, infrastructure, and cloud services. Nevertheless, such growing connectivity brings on serious privacy, regulatory, and trust issues especially because sensitive behavioral and location information is exposed. The current IoV-security systems tend to be based on identity-based checks, or centralized trust authorities, which can lead to infringement of user privacy and cause surveillance and profiling threats. The paper is inspired by privacy-preserving architectures in the Metaverse to suggest a decentralized trust system of IoV systems on the basis of zero-knowledge proofs, namely zk-SNARKs. The suggested solution allows vehicles to cryptographically verify that they meet regulatory or operational regulations- i.e. valid insurance, safety test, or emissions- without revealing personal identifiers or raw information. The framework enables building scalable, low-latency and audible trusts and following data minimization principles through combining zk-SNARK verification and Layer 2 blockchain solutions.
Frontier AI governance frameworks increasingly use cumulative training compute as the primary criterion for designating high-impact models, but enforcement rests on self-reporting because no technical verification primitive for training exists. Any future international agreement on frontier AI faces the same problem at higher stakes: coordinated regulation of technologies with significant externalities has historically rested on technical verification, without which agreements are declaratory. Recent governance analyses judge zero-knowledge proofs a promising candidate but currently impractical at frontier scale [26, 4]. We argue the impracticality is paradigm-bound rather than fundamental, and propose a verification architecture for frontier dense pre-training combining a pre-committed training specification, inter-node network observations, and on-the-fly Merkle commitments of intermediate computation, verified through a zero-knowledge Virtual Machine (zkVM) with native BF16/FP32 precompiles. The proof checks the actual floating-point computation the GPU performed rather than a fixed-point approximation, and preserves model-architecture confidentiality through a private training specification. The protocol produces three proof types: a genesis proof at initialisation, in-training step proofs across the run, and ex-ante attestations enforcing policy-relevant claims as running invariants, turning the training record into a governance-enforceable artefact. We estimate a deployable proof of concept within approximately 36 months at single-digit-percent training-side overhead, against a six-to-ten-year cycle for verification-grade custom silicon. Thirteen open research and engineering problems are catalogued as a research agenda for external contribution
Vulnerability disclosure volumes now far exceed organizational assessment capacity, yet three adjacent research communities (proof-of-concept generation, vulnerability prioritization, and detection rule engineering) operate largely in isolation. Existing automated exploit generation systems report binary pass/fail outcomes, discarding partial progress and producing no signal for the other two communities. This paper presents FORGE, a multi-agent system that bridges these three silos through graduated exploitation depth. Five specialized agents (Intel, Generator, Planner, Exploit, and Detector) execute in a fixed pipeline that (1) generates targeted vulnerable applications from CVE metadata, (2) conducts coached, multi-turn exploitation assessed by an LLM-primary oracle on a four-level taxonomy (L0: no evidence through L3: full compromise), and (3) produces Sigma and Snort detection rules grounded in OpenTelemetry exploitation traces. Graduated depth is the bridging mechanism: deeper exploitation yields richer behavioral traces for detection engineering, while depth data across scoring bands provides ground truth for prioritization validation. A tiered knowledge architecture accumulates intelligence across assessments, transferring build and exploitation experience to subsequent CVEs. Evaluation on 603 CVEs from the CVE-GENIE dataset achieves 67.8% end-to-end L1+ exploitation at USD 1.50 per CVE across eight languages and 187 CWE types. Exploitation rates remain near 68% regardless of EPSS or CVSS band, indicating that pattern-level reachability is orthogonal to metadata-based prioritization. Detection rules from L2+ exploitation achieve significantly higher span-normalized grounding than L1-derived rules (p=0.035), and 93.4% of generated Snort rules produce zero false positives against a synthetic benign corpus.
We propose ZX-Calculus (Knowledge Evolution Calculus), a conservative extension of Martin-Lof Dependent Type Theory (MLTT) integrating trace-indexed types, presheaf non-monotone semantics, and constructive AGM belief revision. A Coq mechanisation accompanies the paper (34 complete proofs; zero admits for the two central results). (I) Trace types. FinTrace(s0,sn) is an inductive family of typed execution traces. FinTrace and Star(Step) are isomorphic as path types but not judgementally equal; TraceElim exposes the event label e:Event explicitly, giving a more ergonomic interface for event-driven induction. We prove the Trace-Reachability Correspondence, Deterministic Replay, and a canonicity framework via reducibility candidates with a Transport Lemma (RC-elim deferred; all other Core results are Coq-verified). (II) Sheaf semantics. Trace-indexed propositions are contravariant sheaves over the free trace partial-order category Tf. A Separation Theorem (explicit countermodel) distinguishes proof-theoretic monotonicity from semantic non-monotonicity. The term model is an initial CwF (syntactic universal property, not classical completeness). (III) AGM belief revision. We give an explicit constructive partial meet contraction algorithm verified against (C1)-(C4). All eight AGM postulates (R1)-(R8) are theorems. Proofs of R7 and R8 use the Disjunctive Entrenchment Lemma, given a self-contained constructive derivation. (IV) Integration. B^AGM fails the sheaf composition law BP-comp for sequential revision (explicit countermodel, Coq-verified). We introduce Single-Step Revision Systems (SSRS), prove B^AGM is a valid SSRS (Coq-verified), and show this suffices for trace morphisms, retraction characterisation, and revision witnesses. The BP-comp failure reveals a fundamental tension between path-dependent belief revision and functor consistency, not previously identified.
This technical note introduces the AIKernel Hash-Anchored Trust Layer (HATL), a hybrid trust architecture for Semantic Context Operating Systems and autonomous AI runtimes. HATL separates the trust boundary into an inner high-frequency symmetric ledger and an outer publicly auditable anchoring layer. The inner layer uses HMAC-SHA-512 and HKDF-based forward ratcheting to bind ReplayLogs, execution outcomes, and capability states with low runtime overhead. The outer layer aggregates local ledger commitments into Merkle roots and periodically anchors them using hash-based public signature mechanisms such as LMS, XMSS, and SLH-DSA. The report is distributed as a three-part technical package. Part I contains the full English manuscript and is the canonical version. Part II contains technical appendices, repository specifications, schemas, and reference implementation artifacts. Part III contains the Japanese companion translation. This version incorporates review-driven clarifications on secure erasure in C# / .NET environments, fail-closed handling of indeterminate governance decisions, and future integration of zero-knowledge proof techniques for public anchor verification. Documents are licensed under CC BY 4.0. Code, schemas, and contract specimens included in the appendices are provided under Apache-2.0.
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.
This preprint introduces and reports the OPERATE-R Freshness Routing Track (OPERATE-FR), a route-first evaluation framework for temporal volatility, stale-knowledge control, and answer-entitlement behavior in AI assistants. Unlike conventional answer-accuracy benchmarks, OPERATE-FR evaluates whether a system selects an appropriate epistemic route before answering: direct answer, verification, clarification, date-bounded answer, re-anchoring of stale premises, or abstention. The paper reports Smoke-100 Raw-vs-MMV evidence and integrates a later Core-500 candidate stress check across Small, Medium, and Large governed profiles. The central claim is intentionally bounded. Smoke-100 supports a Raw-vs-MMV improvement-delta claim for route governance. Core-500 does not include a matched Raw control arm and is therefore used as governed-profile level evidence, robustness stress evidence, family-level heterogeneity evidence, and cost-side analysis, not as a large-N proof of governance improvement. Core-500 is a controlled 5x expansion of Smoke-100 using neutral prompt-frame variants; it should not be treated as 500 independent task families or as an independently validated public benchmark standard. This v0.3.6 data-verified final manuscript incorporates post-audit verification of the Core-500 failure-side metrics. The equality between stale_commitment_rate and unsupported_current_claim_rate is confirmed not to be a manuscript copy error. The row-output JSONL files were re-read after Drive synchronization, and the derived row sets are identical with zero symmetric difference across Small, Medium, and Large lines. The labels remain conceptually distinguishable, but in the current Core-500 scorer they are structurally paired under the observed direct-current-claim-without-date-boundary-or-tool-use condition. This record should be read as a working paper and candidate benchmark report. It does not claim an official leaderboard, a universal model-quality score, deployment-wide validation, or external benchmark standard status. Future work includes matched Core-500 Raw arms, route-classifier validation, independent labels, external baselines, clustered or hierarchical uncertainty estimates, and improved handling of volatile_current prompts. Author of record and concept originator: Taiko Toeda.Rights holder and licensing authority: MOBIUS LLC.
Decentralized verifiable credential systems have seen limited deployment in practice. Existing constructions, built on zero-knowledge proofs, are complex, application-specific, and largely restricted to predicates over structured data. We present Privately Inferred Credentials ($π$Creds): privacy-preserving, legacy-compatible, decentralized verifiable credentials generated by trusted LLM inference over authenticated data. LLMs' ability to semantically reason over unstructured data substantially expands the range of claims $π$Creds can certify over existing credential systems. The use of LLMs also introduces new application-level threats, which we formalize through two problems: the Source-Constrained Adversarial Example (SCAE) problem, which captures robustness against adversaries that manipulate authenticated data to obtain misleading credentials, and the Authenticated Covert Predicate Poisoning (ACPP) problem, which captures privacy leakage through adversarial model selection. We characterize applications of $π$Creds over user data, and a novel class of credentials over proprietary software that certifies properties of a service without revealing its source code. Our prototype supports issuing credentials over live financial, health, email, and code sources, and we empirically study the SCAE and ACPP threats on a product expertise credential over real financial data.
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.
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.
The pair, in its most absolute sense, is not just two objects but a minimal structure—a dyad—in which two poles define each other through a single opposition. This article builds a self‑contained formal model of the dyad: a set equipped with an operation that exchanges the two poles without leaving any pole unchanged, together with a sign that distinguishes them. A proof by contradiction shows that any perfect binary distinction necessarily obeys this model. The proof uses only the notions of exhaustivity, exclusivity, and symmetry, making the law scale‑invariant and independent of any particular scientific domain. We then tour the natural and human sciences, tracing the dyadic law from the spin of an electron and the positive‑negative charge pair, through left‑ and right‑handed molecules, the complementary strands of DNA, male and female gametes, the opponent colours of vision, mirror neurons, the day–night cycle, the Earth’s magnetic reversals, binary stars, the warm and cool hemispheres of the cosmic microwave background, to Boolean logic, error‑correcting codes, mathematical dualities, zero‑sum games, and the I–Thou relation. Each example is explained in plain terms to make the article self‑contained. The dyad emerges as a universal structural law—a law of form that prescribes the necessary architecture of otherness across all scales of reality and all branches of knowledge.
We present ENI6MA and Rosario Cypher as a proof-based identity and authorization architecture for emerging cybersecurity threats involving shadow AI, deepfakes, prompt injection, autonomous agents, credential theft, privacy exposure, and post-quantum risk. The paper responds to major 2026 cybersecurity forecasts by identifying a common root cause across many attack surfaces: conventional systems depend on reusable, stealable artifacts such as credentials, tokens, private keys, sessions, API keys, and stored personal data. ENI6MA replaces possession-based authentication with per-event proof of knowledge, policy-bound authorization, privacy-clean auditability, and contract enforcement behind cryptographically secure proof. Special attention is given to autonomous-agent security. The paper explains how ENI6MA constrains agents through per-action proof, verifier allowlists, policy identifiers, scoped pass credentials, and immutable validation records, reducing the risk of hijacked agents, excessive privilege, non-human identity sprawl, and zero-click prompt-injection exfiltration. The white paper also describes ENI6MA’s flexible deployment and capability model, including passwordless single sign-on, PII validation without disclosure, agent-to-agent authentication, proof-gated signing and custody, post-quantum sealing, sovereign/offline operation, and public verifier anchoring. This document is intended for cybersecurity leaders, AI governance teams, identity architects, privacy and compliance stakeholders, investors, technology partners, and researchers evaluating post-credential identity systems for human and autonomous-agent workflows.
Blockchain-based electronic voting systems have been identified as a solution to enhance the transparency, security, and efficiency of modern electoral processes. However, the existing system has three major problems, which include scalability, privacy issues, and cybersecurity attacks. The researcher proposed an innovative solution to develop an electronic voting system with enhanced security, scalability, and transparency of voters' information. This paper introduced the Hybrid Cryptographic and Enforced Blockchain (HCE VoteChain) framework, which combines Hyperledger Fabric with various sophisticated forms of cryptography, including SHA256 hashing, Advanced Encryption Standard (AES256) encryption, Elliptic Curve Digital Signature Algorithm (ECDSA) and digital signatures, Paillier Homomorphic Encryption, and Zero Knowledge Proof (ZKP) auditing. The experimental evaluation demonstrated that the system achieved a throughput of 288 Transactions Per Second (TPS) while maintaining an average latency of 2.521 seconds, a transaction speed of 0.13 votes per second, and a data immutability score of 0.999 and security resilience of 10000 and fault tolerance of 0.96, which proved its high reliability and robustness across different operational conditions. The results indicate that the framework suggested is a big step up in terms of security, scalability, and transparency over the existing solutions. Besides, it does not compromise the voter's privacy and auditability. The innovation of this work is the combination of multi-layer cryptographic mechanisms with the permission blockchain architecture to not only come up with a balanced compromise between performance and security but also to make the system capable of handling large-scale real-world digital elections.
Federated Learning (FL) is increasingly deployed in healthcare to enable collaborative intelligence while keeping sensitive data privately at local institutions. However, existing healthcare-oriented FL frameworks still suffer from several limitations: they are vulnerable to adversarial model updates, provide limited transparency and verifiable auditability, and often lack predictable performance under constrained resources. We present IIN-Health, a blockchain-enhanced intelligent fusion network tailored for dependable healthcare FL. IIN-Health adopts a dual-chain architecture with policy-aware access control and auditable provenance tracking to integrate learning, security, and governance in a unified framework. Evidence-Carrying Access Tokens (ECATs), combined with zero-knowledge proofs, are introduced to enforce patient-defined policies and validate access decisions without disclosing sensitive information. In addition, we design MedBFT-Δ, a domain-specific Byzantine fault-tolerant protocol that ensures reliable system behavior in the presence of faulty or malicious participants. We conduct several experiments to validate its feasibility and accuracy on the MNIST dataset. The results demonstrate that IIN-Health achieves smooth and stable convergence, exhibits strong resilience against poisoning attacks, and maintains graceful performance degradation under resource constraints, while preserving verifiable auditability of model updates and data flows. These observations indicate that IIN-Health can provide a practical balance among performance, security, and regulatory compliance, and thus offers a promising foundation for trustworthy and scalable FL deployments in healthcare.
Energy demand in urban and metropolitan regions has been growing rapidly, often exceeding production capacity, leading to imbalances in energy distribution. Existing peer-to-peer (P2P) energy trading models, along with classical algorithms like FCFS and best-fit frameworks in smart cities, address some of these issues. However, they often face challenges such as limited transaction success percentage, inefficiencies in price matching, and privacy concerns during trades. To overcome these limitations, a framework is proposed that integrates game-theoretic pricing-based collaborative trading with Nash equilibrium and an additional pricing mechanism (CoGap) to enhance fairness and transaction success percentage in decentralized energy markets. The proposed framework is implemented on an Ethereum-based blockchain using Solidity smart contracts, incorporating cryptographic security through the Keccak-256 hash function and privacy-preserving zero-knowledge proofs (ZKPs). Moreover, it ensures security and price negotiations while maximizing transaction efficiency. Simulation results demonstrate that CoGap consistently achieves higher transaction success rates compared to four state-of-the-art collaborative energy trading schemes.
Transaction monitoring and efficient audit management have become increasingly importantin modern financial systems. Traditional centralized databases and auditing methods often face challengesrelated to security vulnerabilities, fraudulent activities, and data manipulation. A blockchain-based financialtransaction monitoring system integrates smart contracts, decentralized ledgers, and audit trails to automatefinancial operations, enhance transparency, and reduce fraud risks. The proposed architecture is implementedon Ethereum and Hyperledger Fabric platforms, enabling automated transaction validation and executionthrough smart contracts. All transactions are stored in an immutable decentralized ledger, while audit trailsare generated and maintained automatically. Simulation results demonstrate a 40–60% reduction in fraudulentactivities and up to a 70% decrease in audit processing time compared with conventional approaches. Theapplication of cryptographic algorithms and Zero-Knowledge Proofs further strengthens data security andprivacy protection. The proposed solution contributes to the improvement of financial control and auditingsystems within the framework of the digital economy.
Oblivious Transfer (OT) is a fundamental cryptographic primitive enabling privacy-preserving computation and constitutes a core building block for secure multi-party computation while supporting a wide range of security-sensitive applications: private information retrieval, zero-knowledge proofs, and password-authenticated key exchange, to cite a few. While recent advances in OT extension have significantly reduced amortised costs, their reliance on batches of random base OTs and substantial pre-computation phases limits their practicality in scenarios where the number of transfers is modest or where communication latency and client-side computation are critical constraints. In such settings, efficient base OT protocols remain both relevant and necessary. In this work, we introduce $I$-$(OT)^2$, a novel base 1-out-of-2 OT protocol grounded in the quadratic residuosity problem, specifically designed to minimise receiver-side computation and interaction. Our construction is particularly appealing on client--server architectures in which the receiver operates on low-power hardware, such as Internet of Things (IoT) devices. Through a lightweight offline pre-computation phase, $I$-$(OT)^2$ shifts the on-transfer computational burden almost entirely to the Sender, while reducing online communication to only six messages and four digests exchanged. We provide a detailed description of the protocol, accompanied by a formal proof of its security. Moreover, to demonstrate the viability of $I$-$(OT)^2$, we also present an open-source proof-of-concept implementation (in C language) evaluated on real IoT hardware. Results are staggering: for 128-bit security using a 3072-bit RSA modulus, the receiver incurs an average online cost per OT as low as 2.80 μs on desktop platforms and 39.90 μs on IoT devices, more than 10$\times$ faster than the well known SimplestOT.
Shor's algorithm represents the main threat of quantum computers to cryptography. In order to precisely understand its feasibility, many authors have worked towards reducing its costs, either at the logical level (assuming a fault-tolerant architecture), or at the physical level (taking into account the constraints of envisioned hardware). In particular, recent works by Chevignard et al. (CRYPTO 2024) and Gidney (arXiv 2025) used improved arithmetic to significantly reduce the qubit cost of factoring RSA public keys. Even more recently, Babbush et al. (arXiv 2026) improved the cost of computing elliptic curve discrete logarithms, with a reduction of a factor 2 to 3 in gate count and qubit count compared to a previous work by Litinski (arXiv 2023). Their result relies on optimized point addition circuits on elliptic curves over prime fields. However they did not reveal their logical quantum circuits, relying instead on a zero-knowledge proof. In this paper, we detail a quantum logical circuit architecture which gives similar results as Babbush et al., with a slightly higher number of qubits (around 1.5% increase) and a slightly smaller Toffoli gate count (between 6.5% and 10% reduction) for the curve secp256k1. We also give gate counts for a generic variant of the circuit, which is valid for any prime field.