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.
Abstract The high rate of healthcare digitalization and the extensive use of electronic healthcare records (EHRs) have heightened the issues of data privacy, interoperability, and the ability of the system to scale. Blockchain has emerged as a promising paradigm of decentralizing trust and improving security in healthcare information systems, and its real implementation is still divided. The paper highlights a systematic review of 94 peer-reviewed articles published in the years 2019–2025, which investigate architectural designs, privacy designs, scalability designs, federated learning designs, cross-chain interoperability designs, and novel cryptography designs in health care blockchain systems. The review contributes to the research in two ways: (i) a full taxonomy of blockchain design methods of healthcare applications and (ii) an organized discussion of the gaps in the research and future trends. The results demonstrate that hybrid constructions of lightweight zero-knowledge proofs, federated learning, adaptive consensus mechanisms, and cross-chain frameworks have better potential in privacy, scalability, and regulatory compliance than blockchain-based EHR solutions.
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.
K Vigneshkumar, A R JayaSudha, P Nandini, Jana Murugesan · 7 authors
The system architecture presented in this work uses blockchain technology in conjunction with cryptographic authentication methods and real-time anomaly detection to validate the integrity of artificial intelligence models. By integrating tokenization-based model tracking, zero-knowledge proof verification, and machine learningbased integrity monitoring, the suggested solution fills important holes in current methods. We use Ethereum smart contracts to construct a prototype system and assess it using several AI model designs. In comparison to signature-based methods alone, experimental findings show 96.7% fewer false positives, sub-second verification latency for the real-time component, and 99.4% detection accuracy for model tampering attempts. Every day, the system effectively processes 10,000 model inference records while upholding cryptographic security requirements.