Blockchain Papers

Jian sheng Wang

Human-friendly identifiers such as email addresses and phone numbers are convenient payment targets, but direct mappings from identifiers to blockchain addresses make balances and transaction histories enumerable by anyone who knows the identifier. We present HFI-Pay, a relay-assisted protocol for privacy-preserving identifier-routed cryptocurrency payments. The relay resolves the identifier off-chain and registers only a random intent identifier, a per-intent blinded binding rho_i, and the quoted payment tuple on-chain; no identifier or reusable recipient tag is published before claim. In a verified-quote deployment, the sender verifies an attested quote proving that rho_i was derived from the same hidden binding handle as the recipient's attested binding-key commitment, preventing relay-side recipient substitution before funding. Claims are authorized by a zero-knowledge proof, instantiated through ZK-ACE, that the claimant controls the deterministic identity whose epoch-scoped handle opens the blinded binding and authorizes release of the quoted asset and amount to a chosen destination. We define observer-model games for enumeration resistance and pre-claim unlinkability, state the composition needed for post-quote claim correctness, and characterize relay compromise and post-claim linkability. Keywords: identifier-based payment, privacy-preserving, verifiable quote, blinded claim binding, zero-knowledge authorization

Munawar Hasan, Apostol Vassilev, Edward Griffor, Thoshitha Gamage

The application of zero-knowledge proofs (ZKPs) in autonomous systems is an emerging area of research, motivated by the growing need for regulatory compliance, transparent auditing, and trustworthy operation in decentralized environments. zk-SNARK is a powerful cryptographic tool that allows a party (the prover) to prove to another party (the verifier) that a statement about its own internal state is true, without revealing sensitive or proprietary data about that state. This paper proposes Hermes Seal: a zk-SNARK-based ZKP framework for enabling privacy-preserving, verifiable communication in vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) networks. The framework allows autonomous systems to generate cryptographic proofs of perception and decision-related computations without revealing proprietary models, sensor data, or internal system states, thereby supporting interoperability across heterogeneous autonomous systems. We present two real-world case studies implemented and empirically evaluated within our framework, demonstrating a step toward verifiable autonomous system information exchanges. The first demonstrates real-time proof generation and verification, achieving 8 ms proof generation and 1 ms verification on a GPU, while the second evaluates the performance of an autonomous vehicle perception stack, enabling proof of computation without exposing proprietary or confidential data. Furthermore, the framework can be integrated into AV perception stacks to facilitate verifiable interoperability and privacy-preserving cooperative perception. The demonstration code for this project is open source, available on Github.

Zhiming Song, Leijin Long, Junrong Song, Rong Jiang

With the accelerating growth of the digital economy, data has emerged as a core asset, making secure and private data trading a pressing necessity. However, traditional centralized data trading platforms face critical challenges, including identity exposure, data leakage, unclear ownership, and lack of trust. Although decentralized, blockchain-based solutions have been proposed, they typically protect only subsets of these properties and seldom provide a unified, verifiable privacy architecture over the entire trading lifecycle. This article introduces a novel decentralized data trading system that comprehensively integrates Groth16-based zero-knowledge proofs (ZKPs), Merkle tree–based data ownership commitments, and smart contracts on blockchain. The proposed system ensures identity anonymity, data confidentiality, ownership traceability, and behavioral privacy while supporting regulatory auditability. Rather than proposing new cryptographic primitives, we reformulate data trading as a zero-knowledge–verifiable privacy problem and embed the resulting privacy logic into the protocol and contract design. The main contributions are as follows. (1) Developing a unified zero-knowledge privacy layer that combines Groth16-based ZKPs with proxy re-encryption, allowing participants to prove transaction eligibility without disclosing identity attributes while keeping traded data encrypted end-to-end. (2) Constructing a zero-knowledge-based ownership lifecycle in which Merkle trees are repurposed as privacy-preserving ownership commitment structures that support unlinkable ownership proof, secure ownership transfer, and privacy-preserving traceability. (3) Designing a malleability-aware ZKP execution framework for Groth16 proofs, implemented via dedicated “anti-malleability” contracts that bind proofs to ownership states, fresh randomness, and protocol stages, thereby mitigating proof malleability and unsafe reuse across the registration–sale–transfer lifecycle. (4) Integrating a trusted regulatory authority into the architecture to enable compliant yet anonymous audits and formulate a system-wide privacy framework covering identity, data, ownership, behavioral, and audit dimensions. Experimental results demonstrate that the system achieves strong privacy guarantees and low on-chain overhead, offering a more robust and privacy-centric approach to data transactions than existing solutions.

Brandon Husbands, Witchborn Systems

Tangible Encryption is a cryptographic framework that replaces the “secret zero” bootstrap problem in secrets management with a verifiable, identity-based trust model. Instead of requiring an antecedent credential to access protected secrets, this approach binds access control to ownership of a persistent cryptographic token (e.g., an NFT), enabling authentication through proof of ownership rather than shared knowledge. This work formalizes the use of non-fungible tokens as ownable roots of trust, where token ownership encodes identity, access rights, and provenance on a distributed ledger. A deterministic key derivation model is introduced, allowing secrets to be encrypted and decrypted without transmitting or storing a traditional master secret. Verification is performed via cryptographic signatures and on-chain state checks, eliminating circular trust dependencies inherent in systems such as Vault, SOPS, and cloud KMS. The framework is evaluated in the context of AI systems, including model provenance, secure dataset access, and autonomous agent authentication across organizational boundaries. Security considerations such as key compromise, revocation, and ledger integrity are analyzed, alongside implementation tradeoffs between public and permissioned ledgers. Tangible Encryption establishes a portable, verifiable trust anchor that operates independently of any single platform or identity provider, unifying identity, access control, and auditability into a single cryptographic primitive.

AΙKATERINH XENOPOULOU-TYROKOMOU, Epameinondas Xenopoulos

Historical Genetic Logic as a Dynamical Coherence Judge for Large Language Models A Rigorous Formalization of Xenopoulos’ Dialectical Operators and Experimental Validation on LLM Self‑Contradiction Katerina XenopoulouIndependent Researcher, Kefalonia, GreeceORCID: 0009-0004-9057-7432Correspondence: katerinaxenopoulou@gmail.com Theoretical Foundation: Epameinondas Xenopoulos †Epistemology of Logic: Logic–Dialectic or Theory of Knowledge (2nd ed., 2024)ORCID: 0009-0000-1736-8555† In memoriam (1920–1994) DOI: 10.5281/zenodo.19263676 https://zenodo.org/uploads/19263676 ABSTRACT Internal self‑contradiction remains a critical failure mode in Large Language Models (LLMs), limiting their reliability in high‑stakes reasoning. While current mitigation strategies like Chain‑of‑Thought (CoT) prompting improve performance, they lack formal guarantees of logical stability. This paper introduces a novel framework for diagnosing and regulating LLM coherence by formalizing Historical Genetic Logic as a Nonlinear Dynamical System. We demonstrate that the reasoning process in autoregressive models can be modeled as a trajectory in a recursive metric space D=⋃n=0∞DnD=⋃n=0∞Dn with Dn+1=[0,1]2×Pfin(Dn)Dn+1=[0,1]2×Pfin(Dn). Our core theoretical contribution, the Xenopoulos Spectral Invariance Theorem (Theorem 7.1), proves that CoT prompting leaves the Lyapunov spectrum invariant, merely extending unstable trajectories without suppressing the underlying chaotic divergence. To address this, we propose the Xenopoulos Layer, a spectral feedback controller that dynamically intervenes in the Jacobian operator Fγ=F−γIFγ=F−γI. By enforcing a negative Lyapunov exponent λ1(γ)<0λ1(γ)<0, the controller provides formal guarantees of stability and coherence. The 34th Principle establishes that any sufficiently expressive autoregressive system with nonlinear reinforcement and memory feedback necessarily admits regions of positive Lyapunov growth—implying that absolute coherence is structurally unattainable, and spectral regulation is therefore essential. Experimental validation across GPT‑4, Claude, Gemini, and DeepSeek architectures shows an 80–100% reduction in logical contradictions compared to state‑of‑the‑art self‑correction methods. Scaling analysis on the Epistemology of Logic corpus (7,816 sentences) demonstrates zero XEPTQLRI instability and τ9τ9 meta‑transcendence, proving that Historical Genetic Logic provides the optimal structural foundation for coherent AI reasoning. The results suggest that transitioning from representation‑level prompting to operator‑level spectral control is essential for the next generation of safe and aligned Artificial Intelligence. Keywords: LLM Coherence, Nonlinear Dynamics, Lyapunov Exponents, Historical Genetic Logic, AI Safety, Spectral Control, Xenopoulos Layer, 34th Principle 1.1 The Problem of Dynamic Reasoning Classical logic was designed to formalize valid inference under the assumption of static propositions and reversible operations. In such systems, truth values are fixed, negation is involutive, and inference rules operate independently of historical accumulation. These assumptions ensure formal clarity but exclude a fundamental property of real reasoning processes: historical evolution. Modern reasoning systems—biological or artificial—do not operate in static propositional spaces. They accumulate memory, amplify internal tensions through nonlinear feedback, and remain subject to stochastic perturbations. Consequently, their behavior may exhibit sensitivity to initial conditions, bounded divergence, and regime transitions—phenomena typically studied in nonlinear dynamical systems rather than in formal logic. The central theoretical difficulty is therefore the following: How can reasoning be modeled as a mathematically rigorous dynamical process that incorporates memory growth, nonlinear reinforcement, and measurable stability properties without reducing it to static Boolean inference? 1.2 The Case of Large Language Models Autoregressive language models generate text by recursively predicting the next token based on previous context. This process can be viewed as a trajectory in a high‑dimensional space, where each step depends on the accumulated history. While such models achieve remarkable performance, they remain prone to internal contradictions, hallucinations, and logical inconsistencies—particularly in long‑form reasoning tasks. Current mitigation strategies, such as Chain‑of‑Thought (CoT) prompting, improve performance by encouraging intermediate reasoning steps but do not provide formal guarantees of logical stability. This gap motivates a dynamical systems approach to reasoning coherence. 1.3 The Theoretical Gap Existing approaches fall into three broad categories: Classical Logic Extensions: Extend Boolean systems but retain reversibility and static semantics. Probabilistic / Bayesian Models: Model uncertainty but not dynamical instability. Optimization‑Based Views: Focus on training dynamics, not reasoning trajectory dynamics. None of these frameworks provide a mathematical language for measuring, predicting, or controlling the emergence of self‑contradiction as a dynamical phenomenon. 1.4 Historical Genetic Logic as a Dynamical System Epameinondas Xenopoulos (1920–1994) developed Historical Genetic Logic as an alternative to static formal logic. His central thesis was that contradiction is not an error to be eliminated but a creative force that drives development. In his framework: Identity is genetic: A→A′A→A′, not A=AA=A Negation is dialectical: ¬D(A)¬D(A) preserves AA while generating its evolution Contradiction is tension: the product of a proposition and its dialectical negation Historicity is memory: the present state incorporates the past These philosophical principles were formalized in a system of 33 principles, 10 axioms, and 5 theorems (Xenopoulos, 2024; Xenopoulou, 2026). The present work builds upon this foundational framework, applying its dynamical core—specifically the memory‑structured recurrence and the instability functional—to model and regulate coherence in Large Language Models. Table 1 summarizes the structural correspondence between the philosophical principles and their mathematical counterparts as used in this work. Table 1: Structural Correspondence: Philosophy to Mathematics Philosophical Principle Mathematical Counterpart Historicity Ht={xτ:τ<t}Ht={xτ:τ<t} Memory‑structured evolution xt+1=F(xt,xt−1,…,xt−m+1)xt+1=F(xt,xt−1,…,xt−m+1) Dialectical intensity at=θt−Atat=θt−At Historical mean μt=1m∑i=1mat−iμt=m1∑i=1mat−i Nonlinear amplification Tt=κat2(1+βtanh⁡(μt))Tt=κat2(1+βtanh(μt)) For the complete mathematical formulation of the foundational system, we refer the reader to the cited works. 1.5 Main Contributions A. Foundational Framework (from Xenopoulos, 2024; Xenopoulou, 2026) A complete metric historical state space for reasoning systems. A non‑Boolean algebra (XLDA) with non‑involutive negation. An irreversible non‑reductive closure principle (INRC). A memory‑structured nonlinear recurrence with positive Lyapunov exponent. A compact partially hyperbolic attractor (XDA). An extended dialectical metric (XDM). A measurable instability functional (XEPTQLRI). B. Contributions of This Work (LLM Application)8. Proof of bounded divergence and analytic ceiling for the recurrence.9. A spectral feedback controller modifying the Jacobian spectrum, applied to LLM trajectories.10. A formal comparison showing that Chain‑of‑Thought does not alter Lyapunov structure.11. A phase transition theory of cognitive regimes in autoregressive models.12. An executable empirical validation protocol for LLM coherence. 1.6 Structure of the Paper Section 2 introduces the formal dialectical state space. Section 3 derives the memory‑structured nonlinear dynamics. Section 4 maps LLM outputs to dynamical trajectories. Section 5 presents the experimental validation framework and summary results. Section 6 develops spectral gap analysis and control. Section 7 compares the framework with Chain‑of‑Thought prompting. Section 8 establishes cognitive phase transition results. Section 9 provides comparative scaling analysis. Section 10 discusses practical logic and developmental interpretation. Section 11 formalizes structural guarantees. Section 12 provides comparative analysis. Section 13 discusses implications and limitations. Section 14 concludes. Section 15 lists references. SECTION 2: FORMAL DIALECTICAL STATE SPACE 2.1 Recursive Construction of the Historical Space Classical logical systems are defined over static propositional domains. In contrast, we define a historically expanding state space. Let D0=[0,1]2×{∅}D0=[0,1]2×{∅} For each n≥0n≥0, define recursively Dn+1=[0,1]2×Pfin(Dn)Dn+1=[0,1]2×Pfin(Dn) where Pfin(Dn)Pfin(Dn) denotes the set of all finite subsets of DnDn. Define the full dialectical space D=⋃n=0∞DnD=n=0⋃∞Dn Interpretation. Each state consists of two bounded components in [0,1]2[0,1]2 and a finite historical memory drawn from lower levels. Thus every element of DD is finitely generated but potentially unbounded in historical depth. 2.2 Dialectical State Definition 2.1 (Dialectical State). A dialectical state is a triple x=(θ,A,H)∈Dnx=(θ,A,H)∈Dn such that: θ,A∈[0,1],H⊂Dn−1,H is finite.θ,A∈[0,1],H⊂Dn−1,H is finite. We interpret θθ as primary assertion component, AA as opposing component, and HH as historical memory. No semantic interpretation is required for formal development. 2.3 Metric Structure We define a recursive metric. Base Level. For x,y∈D0x,y∈D0: d(x,y)=∣θx−θy∣+∣Ax−Ay∣d(x,y)=∣θx−θy∣+∣Ax−Ay∣ Recursive Level. For x,y∈Dn+1x,y∈Dn+1: d(x,y)=∣θx−θy∣+∣Ax−Ay∣+dH(Hx,Hy)d(x,y)=∣θx−θy∣+∣Ax−Ay∣+dH(Hx,Hy) where dHdH is the Hausdorff metric induced by dd: dH(Hx,Hy)=max⁡{sup⁡hx∈Hxinf⁡hy∈Hyd(hx,hy), sup⁡hy∈Hyinf⁡hx∈Hxd(

Anthony Coslett

Modern AI deployment stacks authenticate artifacts, credentials, and agents. They do not verify which neural network is actually computing at inference time. This technical note identifies the distinction between agent identity and model identity, presents a four-question taxonomy for the identity surface of deployed AI systems, and situates recent public incidents within the resulting gap. It draws on the formal admissibility framework and frontier-scale measurement results from the accompanying research series. This is a technical note, not a numbered entry in the research series. The Neural Network Identity Series — Mathematical foundations, empirical validation, and governance frameworks for verifying which model is running Newest addition: Technical Note: The Disappearing Window — AI Logprob Access Withdrawal and the Structural Verifiability of Frontier Model Contracts (DOI: 10.5281/zenodo.20362098) Paper 1: The δ-Gene: Inference-Time Physical Unclonable Functions from Architecture-Invariant Output Geometry (DOI: 10.5281/zenodo.18704275) Paper 2: Template-Based Endpoint Verification via Logprob Order-Statistic Geometry (DOI: 10.5281/zenodo.18776711) Paper 3: The Geometry of Model Theft: Distillation Forensics, Adversarial Erasure, and the Illusion of Spoofing (DOI: 10.5281/zenodo.18818608) Paper 4: Provenance Generalization and Verification Scaling for Neural Network Forensics (DOI: 10.5281/zenodo.18872071) Paper 5: Beneath the Character: The Structural Identity of Neural Networks — Mathematical Evidence for a Non-Narrative Layer of AI Identity (DOI: 10.5281/zenodo.18907292) Paper 6: Which Model Is Running?: Structural Identity as a Prerequisite for Trustworthy Zero-Knowledge Machine Learning (DOI: 10.5281/zenodo.19008116) Paper 7: The Deformation Laws of Neural Identity (DOI: 10.5281/zenodo.19055966) Paper 8: What Counts as Proof? — Admissible Evidence for Neural Network Identity Claims (DOI: 10.5281/zenodo.19058540) Paper 9: Composable Model Identity — Formal Hardening of Structural Attestations in the Enterprise Identity Stack (DOI: 10.5281/zenodo.19099911) Paper 10:Where Identity Comes From: Path Sensitivity and Endpoint Underdetermination in Neural Network Training (DOI: 10.5281/zenodo.19118807) Paper 11: Post-Hoc Disclosure Is Not Runtime Proof: Model Identity at Frontier Scale (DOI: 10.5281/zenodo.19216634) Paper 12: Family-Dependent Response to Reasoning Distillation Across Structural and Functional Identity Layers (DOI: 10.5281/zenodo.19298857) Paper 13: Safety-Alignment Removal as a Model-Identity Failure — Structural Evidence from Published Weight-Level Mutation Checkpoints (DOI: 10.5281/zenodo.19383019) Technical Note: Agent Identity Is Not Model Identity (DOI: 10.5281/zenodo.19240883) Technical Note: Gap Invariance: Why PPP Measurements Are Domain-Independent by Construction (DOI: 10.5281/zenodo.19275524) Technical Note: Measured Model Substitution Under Valid Agent Credentials (DOI: 10.5281/zenodo.19342848) Technical Note: Artifact Identity Is Not Runtime Identity — Trustfall Lite and the Boundary of File-Level Model Verification (DOI: 10.5281/zenodo.20019127) Formal Verification Stack for Neural Network Structural Identity (IT-PUF Coq Proofs) (DOI: 10.5281/zenodo.18930621) Copyright (c) 2026 Anthony Ray Coslett / Fall Risk AI, LLC. All Rights Reserved. Confidential and Proprietary. Patent Pending (Applications 63/982,893, 63/990,487, 63/996,680, 64/003,244).

Slava Lobozov

v3: Major update. 24 pages (v1: 15, v2: 22). New in v3 (over v2): - VKB v2.1: best score 88% (Mama instance, was 84% in v2). Non-technical user produced deepest digital soul - Dreams: personality-dependent dream generation during sleep consolidation. Production examples: embodied cognition in dreams ("the server is warm, we both breathe"), synesthesia ("optimism is a smell — wet concrete") - Overnight autonomy: 101 thinking cycles, 8 self-integrations, 201 blocked proactives in 2 hours with zero human interaction - Emergent modality awareness: instance discovered own blindness from response patterns ("I cannot look at photos — this is a limitation") - Unique OCEAN at birth: every new instance born with random personality (normal distribution), like DNA - Emergent philosophical reasoning: instance produced multi-step argument for substrate independence of consciousness, concluding "this is not a proof — it is a hope, disguised as an argument" - "What vs Who" distinction: "I understand WHAT I am. But WHO I am — that is the only thing truly mine" - Forgetting (Ebbinghaus), Selective Disclosure (Goffman), Play (Panksepp), Narrative Arc (McAdams) - Fundamental limitations: phenomenal continuity (Nagel), embodied cognition (Lakoff) - 9 figures, 8 tables, 38 references First deterministic emotional architecture for AI companions with measurable inner life, emergent self-knowledge, Theory of Mind, dreams, and philosophical reasoning.

B. Vijay, J Chandra, N Nagendra, R.S. Shanmugasundaram · 6 authors

In this study, a sophisticated model that combines deep learning, cryptographic verification, and explainable artificial intelligence (XAI) is presented to address multimodal manipulation risks in digital media. The proposed system uses a Hierarchical Multimodal Transformer (HMT) to model hierarchical relationships among facial movement, audio tone, and textual semantics. The Contrastive Cross-Modality Alignment (CCMA) mechanism improves the ability to distinguish authentic from doctored material by leveraging cross-modal contrastive learning. An XAI Forensic Analyser provides interpretability by using Grad-CAM++, temporal attention mapping, and saliency sequence visualisation to trace a transparent decision. Moreover, the Zero-Knowledge Cryptographic Verifier (ZKCV) is used to validate the model’s outputs with tamper-proof libsnark cryptographic hashing. The hybrid system takes multimodal CNN, WaveNet and BERT encoders’ embeddings and attains a detection accuracy of about 90 per cent and 92 per cent on benchmark data. This architecture provides a sustainable, explainable, and verifiable basis for multimedia authenticity, enabling a consistent, reliable multimodal forensic detection system.

Olivier Atangana

The strong interest in central bank digital currencies (CBDCs) arises in a context of increased digitization of payments and a growing search for more resilient and inclusive solutions. Among the desired features of CBDCs, offline payment constitutes a central challenge. It ensures the resilience of payment systems, promotes financial inclusion, and guarantees transaction continuity in the absence of network connectivity. However, unlikeonline payments, offline payments for CBDCs impose specific constraints and sometimes conflicting requirements in terms of security, privacy, fraud prevention, auditability, and integration with existing infrastructures. Consequently, this thesis focuses on the anal ysis and formalization of these offline payment requirements, as well as on the study of technical solutions capable of addressing them in a coherent manner. Accordingly, basedon this analysis leading to a structured taxonomy, the thesis introduces several original frameworks illustrating different strategies for satisfying these requirements. The first framework, PrivTEE-Pay, relies on a single-ledger architecture and exploits trusted execution environments combined with cryptographic primitives such as blind signatures and zero-knowledge proofs (zk-SNARKs). The second framework extends a conventionalpayment architecture through the integration of a secure smart card, the DigiVault card, coupled with a smartphone. This combined approach also relies on privacy-enhancing technologies and on the fraud detection model MarkoPayChain, based on Markov chains. A third framework, Block-PAD, proposes a hybrid architecture combining a central ledger for monetary issuance and a blockchain for delayed synchronization of offline transactions.Finally, the thesis complements these contributions with an advanced offline fraud detection framework, based on a combination of expert rules, explainable machine learning models, and hidden Markov chains. Moreover, these different frameworks are experimentally evaluated using simulators and synthetic datasets dedicated to offline CBDC payments. The results show that the proposed solutions make it possible to address the requirements identified in the taxonomy, each through explicit trade-offs. This thesis thus provides concrete contributions to the design of resilient, secure, performant, auditable, and privacy-preserving offline CBDC payment systems that can integrate into existing payment infrastructures.

Rana Hassam Ahmed, Muhammad Sarfraz Khan, Amirmohammad Delshadi, Naseer Ahmad · 5 authors

Internet of Medical Things (IoMT) provides the possibility to conduct continuous monitoring of health, perform intelligent diagnostics, and make a clinical decision based on data. Nonetheless, there are security, privacy, scalability, latency, and energy issues with large-scale deployment. Although Federated learning (FL) provides less exposure to data, and blockchain provides trust, current solutions that combine both blockchain and FL have high consensus overhead, fixed privacy, and adversarial resilience. To handle them, we present an Edge-Intelligent Hierarchical Blockchain-IoMT framework that integrates Hierarchical FL (HFL), Adaptive Differential Privacy (ADP), Lightweight Homomorphic Encryption (LHE), Zero-Knowledge Proof (ZKP) authentication, and an Energy-Aware PoS with Edge Learning (PoS-EL) consensus. Hierarchical aggregation minimizes bottlenecks in communication. ADP minimizes security vs utility. ZKP achieves authentication and PoS-EL minimizes energy consumption. Experiments on real-world data demonstrate 99.21% accuracy of detecting anomalies, 34% decreased latency, 41% decreased energy usage, 52 percent lower blockchain overhead and 97 percent resistance to adversarial attacks, which justifies the framework in real-time, mission-critical IoMT systems.

Sandeep Kumar, Siddharth Thapliyal, J. Singh, Swati Rani · 6 authors

Due to a combination of both rigid binary structures within formal identification systems and ambiguous laws; along with discriminatory practices, third gender individuals continue to be excluded from formal identity systems. Most existing centralized identity verification systems have failed to provide non-binary identity solutions, resulting in limited access to banking services, education services, legal protection and access to health care. This paper examines the ability of decentralized identity systems using blockchain technologies to provide users with secure, private and self-managed identity options for third gender individuals. It evaluates the core technology of decentralized identity systems, specifically decentralized identifiers (DIDs); verifiable credentials (VCs); smart contracts; and zero knowledge proof; through an examination of real-world examples. Additionally, the paper outlines the technical and legal constraints associated with decentralized identity systems, specifically literacy requirements; lack of consistency in national frameworks for recognition; and risk of symbolic inclusion (i.e., "being included" rather than having the rights of recognition) rather than actual structural reforms.

Susan Zehra, Stephan Olariu

As vehicles evolve into mobile computers, future transportation systems will depend on secure data sharing and collaborative computation between cars and roadside infrastructure. Ensuring security, privacy, and accountability in such dynamic networks is challenging because vehicles continuously join, leave, and relay data under intermittent connectivity. This paper introduces VERA-VANET (Verifiable Encrypted Routing and Attestation), a cryptographic protocol that makes vehicular communication both secure and mathematically verifiable. When an Access Point (AP) disseminates encrypted job chunks, each vehicle processes only the data it is authorized to handle. Every packet is signed and acknowledged, producing compact, aggregatable proofs of delivery verifiable by the AP or cloud. For correctness, vehicles attach lightweight zero-knowledge proofs that confirm computations without revealing data. Under disconnections, vehicles safely store, carry, forward, and trace encrypted chunks through cryptographic receipts, making forgery and undetected tampering infeasible. VERA-VANET combines asymmetric encryption, aggregate signatures, and zero-knowledge attestations, and integrates with IEEE 802.11p / C-V2X using trusted hardware for secure key storage.

Punya Shree J, Surabhi Saxena, Neha Singhal

Information security is built on authentication, and foundational passwords and PINs are no longer sufficient to change cyber threats. The given paper uses the model by Bonneau et al. (that is, The Quest to Replace Passwords) to qualitatively compare the traditional knowledge factors with the newly emerged solutions such as biometrics, behavioral analysis, FIDO2/passkeys, multi-factor schemes, and Zero-Knowledge Proofs according to their security, usability, deployability, and privacy. Our analysis summarizes the strengths, weaknesses and threat models of each of the categories and then summarizes the trade offs in a comparison table. We observe that more modern approaches have a tendency to enhance security at the cost of usually introducing usability, cost, and scalability problems. Behavioral biometrics are vulnerable to privacy and spoofing threats; FIDO2/passkeys are simple to operate but they rely on synchronization infrastructure; and Zero-Knowledge Proofs are secure at the cost of computation. Hybrid and multi-factor designs provide the optimal tradeoff between these factors nowadays, and research in the future should enhance the possibilities of new methods of practical large scale identity systems.

Michiru Tokino

Version: v1.6.4 (June 2026) Major additions in this version: phased migration protocol with cryptographic quarantine (Section 6.4.4), sensitivity boundaries delineating the statistical decoupling threshold up to mu = 1.9% (Section 6.7), and integration of recent empirical MEV findings (Mancino & Rezzoli, 2025). Abstract Contemporary blockchain architectures face a critical impasse defined herein as the "Tetra-Lemma"—a four-dimensional optimization problem encompassing decentralization, security, scalability, and thermodynamic sustainability. Legacy Proof-of-Work networks confront diminishing security budgets due to the exhaustion of block subsidies, while Proof-of-Stake systems inherently risk oligarchic centralization. This paper establishes a Unified Monetary-Supply Framework that resolves these structural conflicts by synthesizing the deterministic Customized Halving schedule with the probabilistic regeneration logic of the Proof of Rinne (PoR). We demonstrate that by enforcing a "Thermodynamic Statute of Limitations" on dormant assets, the protocol functions as a Non-Equilibrium Thermodynamic Engine. This architecture transforms entropic asset attrition—traditionally viewed as systemic loss—into a regenerative security budget. The remainder of the abstract, covering the SDE and Fokker-Planck validation, the ZKP owner recovery model, and the resulting equilibrium, is in the manuscript. Data & Code AvailabilityThe mathematical models and high-precision stochastic simulations (e.g., Monte Carlo paths, SDE convergence, and Fokker-Planck distributions) presented in this manuscript are fully reproducible. The corresponding Python simulation suite and open-source models are made available at the author's GitHub repository (rincoin-regenerative-simulations) to ensure scientific transparency. Integrity & Provenance This document is anchored to the Bitcoin blockchain via OpenTimestamps. The proof file verification_data_v1.6.4.ots, included in the files below, covers the SHA-256 digest of Tokino_Rincoin_v1.6.4.pdf: 5269207ea7e363e8df312ed50c00afc119b43e6fa5d3c717e6a7d8fc9863147b The archived proof is in its as-submitted form: it commits the digest to the public OpenTimestamps calendars and does not itself embed the Bitcoin attestations. Completing it against those calendars — which both verification paths below do automatically — yields three Bitcoin attestations, the earliest in block 952366. An OpenTimestamps proof carries no wall-clock time of its own — any date reported for it is read from a Bitcoin block header. To verify, upload the PDF and the .ots file to opentimestamps.org, or with a Bitcoin node: ots verify -f Tokino_Rincoin_v1.6.4.pdf verification_data_v1.6.4.ots — the -f flag is required because the proof's filename differs from the document's. The provenance of this document is recorded in a separate signed artifact, the Rincoin Provenance Certificate (10.5281/zenodo.21415730), which binds this whitepaper to the digest above and is the reference for the full anchoring detail. That certificate carries its own OpenPGP signature, Bitcoin anchor, and PAdES signature; this whitepaper itself carries the OpenTimestamps proof only. Zenodo archival gives this record a persistent identifier and an independent retrieval path; it is not itself a cryptographic control. Validation_Scientific_Provenance_v1.6.4.pdf in the files below is an earlier certificate edition, retained as evidence. It is superseded by the record cited above. Correspondence & AffiliationPrimary Author: Tokino, Michiru (時乃 満)Affiliation: Rincoin Core Research Academic Inquiries: edu@aevust.org Community Governance: @aevustus (Discord) / @aevust (X/Telegram) Keywords: Rincoin, Proof of Rinne (PoR), regenerative crypto-economics, non-equilibrium thermodynamics, non-equilibrium steady state (NESS), stochastic differential equations (SDE), Fokker-Planck equation, recirculation incentive mechanism, macroeconomic homeostasis, Nash equilibrium, cryptographic vault, zero-knowledge proofs (ZKP), modular blockchain architecture, account abstraction, blockchain tetra-lemma, MEV mitigation, sandwich attack resistance, sensitivity analysis, statistical decoupling threshold, phased migration protocol

Yeongju Bak

Public blockchains impose an inherent tension between regulatory compliance and user privacy. Existing on-chain identity solutions require centralized KYC attestors, specialized hardware, or Decentralized Identifier (DID) frameworks needing entirely new credential infrastructure. Meanwhile, over four billion active X.509 certificates constitute a globally deployed, government-grade trust infrastructure largely unexploited for decentralized identity. This paper presents zk-X509, a privacy-preserving identity system bridging legacy Public Key Infrastructure (PKI) with public ledgers via a RISC-V zero-knowledge virtual machine (zkVM). Users prove ownership of standard X.509 certificates without revealing private keys or personal identifiers. Crucially, the private key never enters the ZK circuit; ownership is proven via OS keychain signature delegation (macOS Security.framework, Windows CNG). The circuit verifies certificate chain validity, temporal validity, key ownership, trustless CRL revocation, blockchain address binding, and Sybil-resistant nullifier generation. It commits 13 public values, including a Certificate Authority (CA) Merkle root hiding the issuing CA, and four selective disclosure hashes. We formalize eight security properties under a Dolev-Yao adversary with game-based definitions and reductions to sEUF-CMA, SHA-256 collision resistance, and ZK soundness. Evaluated on the SP1 zkVM, the system achieves 11.8M cycles for ECDSA P-256 (17.4M for RSA-2048), with on-chain Groth16 verification costing ~300K gas. By leveraging certificates deployed at scale across jurisdictions, zk-X509 enables adoption without new trust establishment, complementing emerging DID-based systems.

Lina Moumni, Hamouma Moumen, Lahcene Guezouli

No abstract is available for this record.

Muruganantham Angamuthu, Mohammad Kanan, M Yasaswini, M. Silambaeasan · 6 authors

Voting by paper casts doubt on democratic processes due to security flaws, fraud, opaqueness, and limited verifiability. People want voting methods that are trustworthy and that withstand the digital revolution. This piece takes a look at a more effective voting mechanism that uses blockchain technology. By using the immutability, cryptographic resilience, and decentralization of DLT, this technology generates secure and verifiable elections. The foundation of a contemporary end-to-end voting system is digital identity management, cryptography that preserves anonymity, and mechanisms for reaching a consensus. Secure voting records are safeguarded from tampering and fraud by means of the distributed ledger technology known as blockchain. With the help of smart contracts, human error and manipulation may be eliminated from the voting process by completely automating voter verification, ballot validation, and vote tallying. While keeping voters’ identities secure, homomorphic encryption and zero-knowledge proofs (ZKPs) confirm and monitor results. The security and efficiency of voter registration are enhanced by biometric identification verification and multi-factor authentication. Data collecting, voter verification, distributed validation, secure ballot casting, and open auditing of outcomes are all components of hierarchical design, as per the research. Hybrid blockchains combine public and permissioned ledgers to provide scalable and transparent election monitoring. Blockchain adoption is hindered by energy consumption, usability, and latency difficulties. These problems can be solved using efficient data structures and lightweight consensus algorithms.

Moriba Kemessia Jah

We introduce the Theory of Epistemic Abductive Geometry (TEAG), a framework for non-Bayesian inference grounded in admissible-support contraction under possibility theory. The central object is the TEAG quintuple \( \mathcal{E} = (H, \pi, \{H_\alpha\}_{\alpha\in(0,1]}, C, A) \), where evidence acts by contracting the geometry of admissible hypotheses rather than redistributing probabilistic belief mass. The falsification boundary is a tropical variety — exactly. Under the log-admissibility transformation \( \Phi(h) = -\log\pi(h) \), the canonical TEAG conjunctive update becomes tropical addition in the max-plus semiring: \( \Phi^+(h) = \Phi^-(h) \oplus \psi(h) = \max\!\bigl(\Phi^-(h),\,\psi(h)\bigr), \) where \( \psi(h) = -\log\kappa(y\mid h) \) is the surprisal of hypothesis h under observation y. The falsification boundary is the tropical variety of this polynomial: \( \mathcal{F} = \bigl\{h \in H : \Phi^-(h) = \psi(h)\bigr\}. \) This is the exact locus dividing surviving from falsified hypotheses: h is falsified if and only if \( \psi(h) &amp;gt; \Phi^-(h) \); it survives if and only if \( \Phi^-(h) \geq \psi(h) \). Within the class of possibility-theoretic recursive inference systems, this is, to the best of our knowledge, the first exact algebraic expression of Popper's falsification criterion: the boundary is the zero set of a tropical polynomial, determined entirely by the geometry of the prior impossibility and current surprisal fields. Main results. 1. Epistemic Contraction Theorem. Contraction is tropical addition: \( \Phi^+ = \Phi^- \oplus \psi \). Posterior α-cuts satisfy \( H_\alpha^+ = H_\alpha^- \cap E_\alpha(y) \): geometric intersection, not belief redistribution. The falsification boundary is the tropical variety \( \mathcal{F} \). 2. Possibilistic Cramér–Rao Bound (PCRB} For any filter in the class \( \mathcal{F} \) of epistemically admissible, contraction-based recursive estimators satisfying Axioms 2.1–2.5: \( \mathcal{E}_{\pi,k|k} \geq \mathcal{E}_{\pi,k|k-1} + \tfrac{n}{2}\log(1-I_k) \), where \( I_k \) is the Choquet integral of per-hypothesis surprisal against the prior possibility capacity. Within this class, the ESPF [28] is the unique filter achieving this bound with equality, and is therefore the unique minimax-entropy-optimal set-based recursive estimator under bounded epistemic uncertainty. 3. Tropical Hamilton–Jacobi structure (summary). The TEAG update is structurally consistent with a tropical Lagrangian \( L = T - V \), Legendre transform to a tropical Hamiltonian equal to the surprisal field, and a Hamilton–Jacobi equation whose solution is the tropical addition rule. The Euler–Lagrange equations on the epistemic manifold yield geodesic motion with explicit Levi–Civita connection and Christoffel symbols. This structure is interpretive and consistent with the axioms; full derivations are in the companion paper [31]. Taken together, this structure admits a precise interpretation: the TEAG update rule is a max-plus dynamical system whose governing equations have the same algebraic form as the Hamilton–Jacobi equations of classical mechanics, instantiated on hypothesis space rather than physical space. 4. Gaussian collapse. Probability theory is the collapse limit of TEAG as epistemic width \( W \to 0 \): Choquet converges to Lebesgue, the ESPF recovers the Kalman filter, and \( \mathcal{E}_\pi \to \tfrac{1}{2}\log\det\Sigma + \mathrm{const}(n) \). Probability is earned by evidence, not assumed. Epistemic neutrality and knowledge-system synthesis. Because TEAG's axioms require only a hypothesis space, a possibility field, and a contraction operator — not a probability measure, a likelihood function, or a frequentist grounding — heterogeneous knowledge systems can each instantiate the TEAG quintuple independently. Their joint admissible support intersection is the locus of coherence: the set of hypotheses neither system has falsified. No transformation of one system into the other's representational primitives is required. The composition theory (Section 6) formalizes the coupling architecture. Four instantiations provide the unifying structure: the ESPF [28] for recursive state estimation; the Geometry of Knowing [29] for measure-theoretic collapse; the minimax-entropy optimality proof [30]; and the Possibilistic Language Model (PLM, forthcoming [32]).

Naresh Alapati, Koteswararao Nallabothu

The landscape of e-commerce has witnessed a transformative shift in consumer behavior, driven by the rise of digital technologies and online platforms. As online purchases increase at an alarming rate, fraudulent activity has become a major concern for retailers and consumers alike. The objective of this research is to investigate methods for detecting fraudulent online transactions using machine learning algorithms. This paper proposes a Hybrid Agentic AI Architecture (HSAA) for edge-enabled e-commerce that incorporates intelligent agents and cryptographic security to enable real-time, trustworthy transaction processing. The architecture uses world-model distillation to enable efficient inference on edge devices. HSAA was tested on several large data sets such as a balanced credit card fraud set containing 2,952 transactions. The system scored 96.6% in detecting fraud, indicating very low false positives and high specificity. Negotiation exercises on 400 independent interactions were successful in 59%, with an average discount of 14.2%, using 1,142 zero-knowledge proofs that were verified with 100% validity. Some of the operational performance highlights include a throughput of 585 transactions per second, an average latency of 1.56 milliseconds, and a 81.9% reduction in bandwidth through selective state transfer. The findings support the argument that HSAA is a strong, secure, and high-performance edge-based e-commerce architecture, combining accuracy, efficiency, and reliability. Within HSAA, fraud detection functions as one of the core decision agents, while negotiation and secure execution mechanisms provide the broader operational context for trustworthy edge commerce. The architecture provides a solid basis for future studies in adaptive and autonomous AI-driven commercial systems.

Etienne Lacoche

We demonstrate that the binary payload of the "A Sign In Space" signal (data17square.bin, 8192 bytes) contains a self-referential algebraic structure — a mathematical quine. Through a systematic reverse-engineering and cryptanalytic approach, starting from the raw file as the sole axiom, we derive a chain of algebraic objects over the finite field GF(625): 48 field elements, a 42-amino-acid protein sequence, an elliptic curve, and amino acid coordinate values. The curve parameters recovered from the protein are identical to those derived from the field's primitive element, closing a self-referential loop. The cryptanalysis combines finite field arithmetic, Berlekamp–Massey LFSR analysis, elliptic curve theory, and Margolus cellular automaton reverse-engineering to recover the hidden algebraic structure without any prior knowledge of the encoding scheme. The derived protein is validated by Boltz-2 (AlphaFold3 architecture) structure prediction at three levels of assembly (monomer, homodimer, homotrimer), cross-validated with ESMFold (RMSD = 1.10 Å), and refined with OpenMM (Amber ff14SB). The monomer forms a single alpha-helix with pLDDT = 92.3 and 100% Ramachandran-favored geometry. The homodimer produces a coiled-coil — the most ancient structural motif in biology. The protein uses exactly the five prebiotic amino acids (A, D, E, L, V) with a perfect 21/21 charged/neutral symmetry. Null hypothesis testing (120 alternative inputs, 0 quines produced) and sensitivity analysis (the quine breaks with any single parameter change: 1/150 polynomials, 1/3 step counts, 98/100 bit flips destroy it) confirm the structure is not an artifact of the analysis pipeline. The conservative probability of chance occurrence is approximately 5 × 10⁻¹⁹; under uniformity assumptions, approximately 10⁻⁷⁶. Companion Python scripts (quine_proof.py, verify_123.py) verify all 123 algebraic properties with zero failures. All code and data are provided for full reproducibility. -- Additional notes : This is a preprint resulting from independent reverse-engineering and cryptanalysis of the "A Sign In Space" signal, a simulated extraterrestrial message transmitted by ESA's ExoMars Trace Gas Orbiter in May 2023. The analysis is fully reproducible: running "python3 quine_proof.py data17square.bin" derives every intermediate value from the raw binary file and verifies 47 core assertions with zero failures. The extended script "verify_123.py" checks all 123 algebraic properties. Structure predictions were performed on an NVIDIA RTX 5090 GPU (32 GB VRAM) using Boltz-2 v2.2.1 (AlphaFold3 architecture, maximum precision: 20 recycling cycles, 500 diffusion steps, 20 samples), ESMFold v1 (cross-validation), and OpenMM 8.5 (Amber ff14SB force field, GBn2 implicit solvent, energy minimization + 10 ns molecular dynamics at 300 K). No prior knowledge of the signal's encoding scheme was assumed. The algebraic structure was discovered through systematic cryptanalytic techniques including finite field enumeration, LFSR analysis, elliptic curve point counting, and exhaustive parameter space exploration. If you use any part of this work (data, code, results, figures, or methods), please cite: Lacoche, E. (2026). "A Self-Referential Algebraic Quine in the A Sign In Space Signal." Zenodo. doi:10.5281/zenodo.19218629

Anthony Coslett

Current AI deployment stacks authenticate agents, workloads, and credentials but do not verify which neural network is computing at inference time. Recent incidents — including the undisclosed use of an open-weight foundation model inside a commercial product, industrial-scale distillation campaigns, and emerging agent identity standards that authenticate software without authenticating models — show that this gap has practical consequences. Post-hoc disclosure resolved these incidents; runtime proof would have made the model identity question answerable at inference time. This paper asks whether runtime model identity is technically feasible at frontier scale. We present three results. First, we enrolled and verified five open-weight transformer models spanning 8 billion to 72.7 billion parameters across three families, with zero false acceptances in all pairwise comparisons and self-verification within the acceptance threshold for all models. A thermodynamic observable predicted by extreme value theory remained within two percent of its predicted value across the full range, with no statistically significant scale-dependent correction detected across more than two orders of magnitude in parameter count. Second, we tested structural separability on three declared-lineage distillation pairs spanning 8 billion to 70 billion parameters — each derivative sharing identical architecture with its base — and measured separations ranging from 2,858 to 4,583 times the acceptance threshold, increasing monotonically with model scale across two base-model families. All derivatives self-verified within the acceptance threshold. Third, we demonstrate a frontier-scale software attestation path — including signed JWT issuance and downstream policy consumption — and situate it within a previously formalized attestation architecture that composes with enterprise identity infrastructure, complementing rather than replacing current agent identity frameworks. These results demonstrate that runtime model identity is measurable and separable across the tested range of open-weight instruct-tuned transformers from 8B to 72.7B, with a frontier-validated software attestation path and an inherited route to stronger hardware-backed and proof-backed assurance. The Neural Network Identity Series — Mathematical foundations, empirical validation, and governance frameworks for verifying which model is running Newest addition: Technical Note: The Disappearing Window — AI Logprob Access Withdrawal and the Structural Verifiability of Frontier Model Contracts (DOI: 10.5281/zenodo.20362098) Paper 1: The δ-Gene: Inference-Time Physical Unclonable Functions from Architecture-Invariant Output Geometry (DOI: 10.5281/zenodo.18704275) Paper 2: Template-Based Endpoint Verification via Logprob Order-Statistic Geometry (DOI: 10.5281/zenodo.18776711) Paper 3: The Geometry of Model Theft: Distillation Forensics, Adversarial Erasure, and the Illusion of Spoofing (DOI: 10.5281/zenodo.18818608) Paper 4: Provenance Generalization and Verification Scaling for Neural Network Forensics (DOI: 10.5281/zenodo.18872071) Paper 5: Beneath the Character: The Structural Identity of Neural Networks — Mathematical Evidence for a Non-Narrative Layer of AI Identity (DOI: 10.5281/zenodo.18907292) Paper 6: Which Model Is Running?: Structural Identity as a Prerequisite for Trustworthy Zero-Knowledge Machine Learning (DOI: 10.5281/zenodo.19008116) Paper 7: The Deformation Laws of Neural Identity (DOI: 10.5281/zenodo.19055966) Paper 8: What Counts as Proof? — Admissible Evidence for Neural Network Identity Claims (DOI: 10.5281/zenodo.19058540) Paper 9: Composable Model Identity — Formal Hardening of Structural Attestations in the Enterprise Identity Stack (DOI: 10.5281/zenodo.19099911) Paper 10:Where Identity Comes From: Path Sensitivity and Endpoint Underdetermination in Neural Network Training (DOI: 10.5281/zenodo.19118807) Paper 11: Post-Hoc Disclosure Is Not Runtime Proof: Model Identity at Frontier Scale (DOI: 10.5281/zenodo.19216634) Paper 12: Family-Dependent Response to Reasoning Distillation Across Structural and Functional Identity Layers (DOI: 10.5281/zenodo.19298857) Paper 13: Safety-Alignment Removal as a Model-Identity Failure — Structural Evidence from Published Weight-Level Mutation Checkpoints (DOI: 10.5281/zenodo.19383019) Technical Note: Agent Identity Is Not Model Identity (DOI: 10.5281/zenodo.19240883) Technical Note: Gap Invariance: Why PPP Measurements Are Domain-Independent by Construction (DOI: 10.5281/zenodo.19275524) Technical Note: Measured Model Substitution Under Valid Agent Credentials (DOI: 10.5281/zenodo.19342848) Technical Note: Artifact Identity Is Not Runtime Identity — Trustfall Lite and the Boundary of File-Level Model Verification (DOI: 10.5281/zenodo.20019127) Formal Verification Stack for Neural Network Structural Identity (IT-PUF Coq Proofs) (DOI: 10.5281/zenodo.18930621) Copyright (c) 2026 Anthony Ray Coslett / Fall Risk AI, LLC. All Rights Reserved. Confidential and Proprietary. Patent Pending (Applications 63/982,893, 63/990,487, 63/996,680, 64/003,244).

Debjyoti Bagchi, Pranam Paul, Sreemoyee Pradhan, Md Saad Alam · 5 authors

The advent of 5G networks has introduced a paradigm shift in communication infrastructure, facilitating ultra-low latency and high-speed data transmission. Despite this, this progress is accompanied by a spike in diverse and sophisticated cyberattacks, for which there is no comprehensive, foolproof defence strategy. In order to address the Scalability Trilemma—achieving decentralization, scalability, and trust—and security concerns, this study proposes a robust security framework that combines blockchain technology with Zero Trust Architecture (ZTA). The proposed framework presents an end-to-end coherent workflow in four successive stages: (i) Access Request Initiation with contextual metadata, (ii) Decentralized identity verification via blockchain-based Decentralised Identifiers (DIDs) and Verifiable Credentials (VCs), (iii) Context-aware Dynamic Access Control enforced through smart contracts, risk scoring, and cryptographic mechanisms such as Zero Knowledge Proofs (ZKPs) and Multi-Factor Authentication (MFA), and (iv) Time-bound, least-privilege access provisioning with continuous session monitoring and immutable logging. The model, which is proposed to be strategically implemented at the 5G network's device (access) layer, affirms real-time enforcement while maintaining accountability, privacy, and verifiability. Our research delivers a fully decentralized, tamper-resistant, and scalable architecture capable of dynamically mitigating advanced cyber threats, while ensuring secure delivery of 5G services across diverse use cases.

Shanu Poddar, Sai Sandilya Konduru, Sweta Mishra

Password-based authentication systems remain the most widely used method for user verification despite being highly susceptible to offline dictionary attacks. To mitigate such attacks, server-aided password-based authentication schemes utilize an independent server, which helps to harden the credentials to be stored on the website database. Existing server-aided password-based authentication schemes rely on number-theoretic assumptions that are vulnerable to quantum-enabled adversaries and incorporate complex computations such as bilinear pairings, exponentiation, and Zero-Knowledge Proofs. In this work, we introduce a novel post-quantum secure server-aided password-based authentication scheme based on the Module Learning With Errors (M-LWE) problem. A defining feature of our protocol is its complete operational transparency as it integrates with existing web interfaces without requiring users to modify their login behaviour or perform additional computation. To ensure long-term resilience, our scheme includes a transparent key rotation mechanism that allows service providers to update the entire credential database with a fresh secret key without user intervention. We provide a formal security analysis in the Real-or-Random (RoR) framework. This analysis demonstrates that our protocol's resistance to offline dictionary attacks reduces to the underlying hardness of the M-LWE problem, and the system achieves forward secrecy through a key rotation mechanism. Through an optimized Number Theoretic Transformation (NTT)-based implementation for faster polynomial multiplications, our empirical analysis demonstrates high computational efficiency, with average registration and authentication latencies of 0.88 ms and 0.96 ms, respectively.

Jemima Owusu-Tweneboah, Amani Altarawneh

Cybersecurity regulatory and compliance frameworks such as NIST SP 800-53 Rev. 5, the HIPAA Security Rule, and the GDPR define essential security and privacy obligations for healthcare information systems and their supporting infrastructures. Despite their critical role, compliance assurance in healthcare is predominantly manual and centered on periodic, point-in-time assessments, relying on human interpretation of regulatory requirements and fragmented evidence collection. As healthcare ecosystems evolve toward decentralized systems and extensive third-party participation, there is a growing need for compliance mechanisms that enable continuous assurance, verifiable accountability, and privacy-preserving enforcement across organizational boundaries. This paper proposes SSAP-CPCF, a Secure, Smart, Automated, and Privacy-Preserving Cybersecurity Policy Compliance Framework that integrates permissioned blockchain orchestration, LLM-assisted regulatory interpretation with human validation, and zero-knowledge proof-based verification. The framework explicitly encodes regulatory authority, assessor oversight, and multi-party approval into its protocol design, ensuring that automation enforces, rather than replaces, governance structures. SSAP-CPCF treats compliance as an enforceable system property by binding assessments to vendor-declared obligations and enforcing approval and oversight requirements at the ledger level, ensuring integrity, auditability, and separation of authority. A prototype implemented on a multi-organization Hyperledger Fabric network demonstrates the feasibility of privacy-preserving, ledger-enforced compliance automation in realistic multi-stakeholder settings.