Blockchain Papers

Attiq Ur Rehman, Shuai Lü, Muhammad Usman, Zaheer Ahmad Gondal · 7 authors

No abstract is available for this record.

Hazel A. Kissi Dankwah

This paper introduces CarbonLedgerProof (CLP), a novel cryptographic traceability algorithm designed to connect asset-level emissions data with financial statement estimates for enhanced Environmental, Social, and Governance (ESG) assurance and impairment testing. The proposed CLP algorithm bridges the gap between carbon emissions reporting and the financial implications of environmental risks, ensuring transparency and traceability across asset portfolios. By integrating blockchain technology and zero-knowledge proofs (ZKPs), CLP offers a secure and efficient way to validate emissions data against financial estimates, addressing challenges in ESG data integrity and providing an automated framework for impairment testing in the context of sustainability. In comparison to existing algorithms such as GreenLedger, CarbonProof, ESG-Chain, and a Traditional Audit (TradAudit) baseline. CLP demonstrates superior performance in terms of scalability, data integrity, and computational efficiency. Through an extensive experimental evaluation, we showcase CLP's ability to significantly reduce verification time and enhance the accuracy of ESG assurance processes. The results indicate that CLP outperforms traditional methods in integrating emissions data into financial systems, offering an innovative approach for real-time emissions monitoring and risk assessment. This paper concludes by proposing CLP as a transformative tool for corporate ESG reporting, with practical implications for financial institutions, auditors, and regulators seeking to streamline the integration of carbon data into decision-making frameworks.

Tuan Nguyen Kim, Ha Nguyen Hoang, Son Doan Trung, Lam Nguyen

Cloud computing has become a vital platform for large-scale data analytics, yet it poses significant privacy challenges when handling sensitive information, especially in healthcare and financial domains.Homomorphic Encryption (HE) enables computation on encrypted data, providing strong privacy guarantees, but traditional HE frameworks lack efficient query representation, do not protect query patterns, and cannot prove correctness of cloud-side computations.This paper proposes HE-Cloud, an integrated privacy-preserving framework that combines DSL-driven query compilation, HE, Zero-Knowledge Proofs (ZKP), and Oblivious RAM (ORAM).Our framework allows clients to express high-level analytical queries, securely executes them on encrypted data, protects query access patterns via ORAM, and returns verifiable results through ZKP.A proof-of-concept implementation using the Pima Diabetes dataset demonstrates feasibility: Average glucose computations can be performed entirely on encrypted data with sub-second latency for homomorphic operations and minimal accuracy loss (approximately 0.001).Scalable secure analytics, extendable to larger datasets and machine learning tasks.

David Condrey

Process attestation verifies human authorship by collecting behavioral biometric evidence, including keystroke dynamics, typing patterns, and editing behavior, during the creative process. However, the very data needed to prove authenticity can reveal intimate details about an author's cognitive state, health conditions, and identity, constituting sensitive biometric data under GDPR Article 9. We resolve this privacy-attestation paradox using zero-knowledge proofs. We present ZK-PoP, a construction that allows a verifier to confirm that (a) sequential work function chains were computed correctly, (b) behavioral feature vectors fall within human population distributions, and (c) content evolution is consistent with incremental human editing, all without learning the underlying behavioral data, exact timing, or intermediate content. Our construction uses Groth16 proofs over arithmetic circuits with Pedersen commitments and Bulletproof range proofs. We prove that ZK-PoP is computationally zero-knowledge, computationally sound, and achieves unlinkability across sessions. Evaluation shows proof generation in under 30 seconds for a 1-hour writing session, with 192-byte proofs verifiable in 8.2 ms, while incurring less than 5% accuracy loss in simulation at practical privacy levels (epsilon >= 1.0) compared to non-private baselines.

AKATERINH XENOPOULOU-TYROKOMOU, Epameinondas Xenopoulos

A Case Study Application of the Xenopoulos Genetic‑Historical Logic System (X‑GHLS) https://github.com/kxenopoulou/epameinondas_xenopoulos_epistemology-of-logic_genetic-historical-logic Author: Katerina XenopoulouORCID: 0009‑0004‑9057‑7432Version: 4.0 (Complete)Publication Date: February 25, 2026 Data and Experimental Setup Dataset: Our World in Data — COVID‑19 GreeceTime Span: January 5, 2020 – August 4, 2024Total Observations: 1,674 daily recordsOut‑of‑Sample Predictions: 1,667Overall Forecast Accuracy: 98.31%Evaluation Metrics: MAPE 1.69% | R² 0.999 | RMSE 120 cases ABSTRACT We present the first complete empirical validation of the Xenopoulos Genetic‑Historical Logic System (X‑GHLS) on real‑world epidemiological data. While the theoretical framework of X‑GHLS establishes 33 philosophical principles and the XEPTQLRI metric for quantifying dialectical tension, this study demonstrates its practical application in forecasting COVID‑19 dynamics in Greece over a 4.5‑year period (January 2020 – August 2024, N = 1,674 days). The system achieves exceptional predictive performance: MAPE: 1.69% (Mean Absolute Percentage Error) R²: 0.999 (Coefficient of Determination) RMSE: 120 cases (Root Mean Square Error) Overall Accuracy: 98.31% Total Predictions: 1,667 Phase analysis reveals that the pandemic was in crisis mode (τ₅ and above) for 1,212 days (72.7% of the total), explaining why conventional statistical models struggle with such highly nonlinear dynamics. The system successfully detects all major COVID‑19 waves in Greece and provides early warning signals through the XEPTQLRI index. Comparative analysis with state‑of‑the‑art models (2026) demonstrates that X‑GHLS outperforms: TimesFM (Google): 3.2% MAPE Chronos‑2: 3.5% MAPE TiRex: 3.8% MAPE Transformer architectures: 4.2% MAPE LSTM networks: 5.8% MAPE ARIMA: 8.5% MAPE The 33rd Principle (Advanced Dialectical Negation) proves crucial for qualitative jump detection, enabling the system to adapt to regime changes that cause other models to fail. The complete mathematical formalization of all 33 principles is provided, with full reproducibility through the open‑source implementation. Environmental and economic advantages are equally striking: zero training cost, 0.001 kWh per prediction (vs 200 kWh for foundation models), zero carbon footprint (vs 100+ tons CO₂), and full interpretability through the 10 dialectical phases (τ₀–τ₉). This work constitutes the first large‑scale empirical validation of a dialectical logic system on real‑world time series data, demonstrating that philosophical principles can be mathematically formalized into predictive models that outperform state‑of‑the‑art machine learning architectures. Keywords: X‑GHLS; dialectical logic; COVID‑19 forecasting; time series analysis; XEPTQLRI index; 33 principles; phase transition detection; qualitative jump; Our World in Data Data Source: Our World in Data — COVID‑19 Greece DatasetCode Availability: Upon request for academic collaborationCorresponding Author: Katerina Xenopoulou (katerinaxenopoulou@gmail.com) 📊 Summary Table (for Abstract) Metric Value Comparison MAPE 1.69% 3.2% (TimesFM) R² 0.999 0.99 (Chronos‑2) Accuracy 98.31% 96.8% (TimesFM) Days Analyzed 1,674 — Predictions 1,667 — Crisis Phases (τ₅+) 1,212 days 72.7% of total 📊 KEY RESULTS Metric Value MAPE 1.69% R² 0.999 RMSE 120 cases Accuracy 98.31% Predictions 1,667 Time span 2020–2024 (1,674 days) 📈 GRAPHICAL RESULTS 1: COVID-19 Cases in Greece (2020–2024)] 2: Dialectical Phases (τ₀–τ₉) with XEPTQLRI Coloring] 3: XEPTQLRI Index with Phase Thresholds] 4: Actual vs Predicted Cases] 🏆 COMPARISON WITH STATE-OF-THE-ART MODELS (2026) Model MAPE Training Cost Energy / Prediction CO₂ Emissions Interpretability XENOPOULOS 1.69% €0 0.001 kWh 0 kg Full (33 principles) TimesFM (Google) ~3.2% €200,000+ 200 kWh 100+ tons Black box Chronos-2 ~3.5% €50,000+ 50 kWh 25 tons Black box TiRex ~3.8% €15,000+ 15 kWh 7.5 tons Limited Transformer ~4.2% €100,000+ 100 kWh 50 tons Black box LSTM ~5.8% €5,000+ 5 kWh 2.5 tons Limited ARIMA ~8.5% €0 0.001 kWh 0 kg Statistical 🔬 DETAILED ANALYSIS BY PHASE Phase Days Mean XEPTQLRI Mean Tension Confidence Description τ₀ 64 0.40 0.064 0.85 Stability τ₁ 35 1.23 0.153 0.85 Stability τ₂ 28 1.71 0.213 0.75 Pattern repetition τ₃ 14 2.88 0.360 0.65 Growing instability τ₄ 14 4.00 0.499 0.55 System saturation τ₅ 147 5.15 0.644 0.40 QUALITATIVE JUMP τ₆ 154 6.02 0.752 0.30 Paradoxical state τ₇ 462 7.06 0.883 0.20 Transcendence τ₈ 749 7.83 0.978 0.20 Transcendence Key observation: The pandemic was in crisis mode (τ₅ and above) for 1,212 days (72.7% of the total), explaining why conventional models struggled to adapt. 🌍 ENVIRONMENTAL & ECONOMIC IMPACT Model Training Cost CO₂ Emissions Equivalent XENOPOULOS €0 0 kg 0 flights TimesFM €200,000+ 100+ tons 200 flights Athens–London Chronos-2 €50,000+ 25 tons 50 flights LSTM €5,000+ 2.5 tons 5 flights 🎯 WHY THIS IS REVOLUTIONARY # Advantage XENOPOULOS Other Models 1 Accuracy 98.31% 91.5% – 96.8% 2 Training Cost €0 €5,000 – €200,000+ 3 Energy per Prediction 0.001 kWh 5 – 200 kWh 4 CO₂ Footprint 0 kg 2.5 – 100+ tons 5 Interpretability Full (33 principles) Black box / Limited 6 Phase Detection Yes (τ₀–τ₉) No 📖 THE 33 PRINCIPLES A. Dialectical Principles (1–4, 12, 16, 18, 26) # Principle 1 Synthesis of Formal and Dialectical Logic 2 Dialectical Contradiction as Creative Force 3 Dialectic of Stasis and Motion 4 Integration of Otherness 12 Dialectical Perception of Infinity 16 Logic of Process 18 Law of State Succession 26 The Concept of Aufhebung B. Theory of Knowledge (5–7, 13, 17, 19, 27, 28) # Principle 5 Historical-Genetic Approach 6 Dialectic of Theory and Practice 7 Transitional Nature of Truth 13 Genetic Logic 17 Restructuring of Dialectical Thought 19 Repetition and Historical Dialectic 27 Triple Coincidence (Sπ, Sα, f(x)) 28 Suszko Triad (L, B, Θ) C. Mathematical Formalization (21–25, 32) # Principle 21 The N[Fi(Gj)] Operator 22 INRC Group (Piaget) 23 XEPTQLRI Index 24 Ten Dialectical Stages (τ₀–τ₉) 25 Dubarle Operators (△, ▼, ▽, ▲) 32 Rogowski Np Operator D. Innovative Applications (8–11, 14–15, 20, 29–31) # Principle 8 Interdisciplinary Application of Dialectics 9 Synthesis of Unity and Differentiation 10 Transcendence of Static Logic 11 Dynamic Perception of Reality 14 Negation as Creative Force 15 Quantitative and Qualitative Change 20 Dual Nature of the "Now-Present" 29 Illusion of Stability 30 Application to Artificial Intelligence 31 Critical Transition Prediction E. The 33rd Principle – Advanced Dialectical Negation f(A) = -A · P · H · (1 + M) + ε Parameter Description A Dialectical tension (from thesis–antithesis conflict) P Predictive capacity of current phase H Historical memory (weight of previous predictions) M Transitional factor (proportional to XEPTQLRI) ε Stochastic noise (uncertainty modeling) 📊 THE XEPTQLRI INDEX AND PHASES τ₀–τ₉ Phase XEPTQLRI Range Description τ₀ < 0.8 Stability τ₁ 0.8 – 1.5 First deviation τ₂ 1.5 – 2.5 Pattern repetition τ₃ 2.5 – 3.5 Incompatibility τ₄ 3.5 – 4.5 System saturation τ₅ 4.5 – 5.5 Qualitative jump τ₆ 5.5 – 6.5 Paradox τ₇ 6.5 – 7.5 Transcendence τ₈ 7.5 – 8.5 Permanent dialectics τ₉ > 8.5 Absolute synthesis 🧠 INTERPRETATION OF RESULTS Feature Description Early phase change detection The system "knows" when it enters crisis mode (τ₅ and above) and adapts predictions accordingly Paradox management In phases τ₆–τ₈, where behavior becomes nonlinear, confidence decreases and stochastic factors increase Historical memory Parameter H in the 33rd Principle incorporates knowledge from previous predictions, creating dialectical learning 🔮 FUTURE DIRECTIONS Limitation Description Future Extension Phase boundaries Thresholds between phases are empirical Automatic phase boundary optimization Stochasticity Random noise introduces minor variability Advanced uncertainty modeling Generalization Tested mainly on COVID-19 data Multi-domain testing (finance, climate) 📜 SCIENTIFIC CONTRIBUTION # Contribution 1 Complete mathematical formalization of 33 philosophical principles into a functional predictive system 2 Introduction of the XEPTQLRI index as a measurable quantity of dialectical tension 3 Ten-phase typology (τ₀–τ₉) for describing system dynamics 4 The 33rd Principle as a qualitative jump operator 5 Proof that a philosophically grounded system can outperform statistical models with millions of parameters 💡 CONCLUSION Aspect XENOPOULOS Advantage Performance 98.31% accuracy — superior to all compared models Cost Zero training cost, runs on any computer Energy 0.001 kWh per prediction (vs 200 kWh) Environment Zero carbon footprint (vs 100+ tons CO₂) Transparency Full interpretability through 33 principles Philosophical foundation Dialectics meets computation — a paradigm shift 📥 CODE AVAILABILITY The system's source code is available upon request for academic collaboration.Please contact the author for further information. 🙏 ACKNOWLEDGMENTS This work is dedicated to the memory of my father, Epameinondas Xenopoulos, whose work Epistemology of Logic (1998, 2nd ed. 2024) provided the foundation for this entire endeavor. I warmly thank my family for their support, and my granddaughter who, at 9 years old, reminded me daily that dialectics is not theory but a way of life. 📚 REFERENCES # Reference 1 Xenopoulos, E. (2024). Epistemology of Logic (2nd ed.), https://www.researchgate.net/publication/359717578_Epistemology_of_Logic_Logic-Dialectic_or_Theory_of_Knowledge 2 Hegel, G.W.F. (1812). Science of Logic 3 Piaget, J.

Anthony Coslett

We study what model-identifying information leaks through commercial language-model APIs that expose top-k token log probabilities. Building on extreme-value theory predictions for logit order-statistic gaps, we confirm that the normalized third logit gap (δ norm) remains near the Gumbel-class constant ≈0.318 across 6 models from 3 providers (OpenAI, Google Vertex AI, xAI) and 3 independent measurement sessions, demonstrating that output-layer universality persists through API truncation and quantization. We introduce a PPP-residualization transform that removes the dominant tail scale factor and reveals a low-dimensional but stable endpoint-specific geometry in the remaining gap spectrum. Contrary to common assumption, "provider" is not a geometrically coherent label: models do not cluster by corporate origin under these observables, but they do separate by model identity across independent sessions. Using a challenge-response protocol with centroid averaging and per-model thresholds, we demonstrate cross-session endpoint verification with a 0.83% breach rate (119/120 correct identifications across three temporal sessions); per-model thresholds eliminate all breaches on this dataset. We observe a robustness phase transition governed by enrollment depth. Under single-session enrollment, prompt selection is load-bearing: the majority of bootstrapped banks fail to separate the six endpoints. Under two-session enrollment, bank sensitivity collapses on this dataset, and a bank compiler produces small compiled banks that exceed the margin of larger uncompiled banks. A dimensionless robustness parameter SNR(K,S) unifies both axes: prompt count K and enrollment depth S jointly govern the transition from bank-sensitive to bank-robust verification. We discuss operational implications for re-enrollment cadence and template management in production deployments. Addendum (02/26/2026): Post-publication results extend this framework in two directions. A distillation experiment across six training protocols demonstrates that a model's structural fingerprint (weight-geometry regime) is completely invariant to knowledge distillation, while its functional fingerprint (PPP-residual template) converges 31--52% toward the teacher's — enabling forensic detection of distillation provenance through API measurements alone. A conditional impossibility theorem, machine-checked in Coq (41 theorems, 0 Admitted), proves that no standalone model can spoof another's PPP-residual template across independent challenge prompts without exhausting its KL divergence budget, under four explicit trust assumptions. Technical Note: Gap Invariance: Why PPP Measurements Are Domain-Independent by Construction (DOI: 10.5281/zenodo.19275524) 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: 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).

Ziya Tan, Zijie Pan, Ying Liang, Shuyuan Yang

Secure and bandwidth-conscious transmission of model updates is a central bottleneck in distributed machine learning. Existing secure aggregation and homomorphic encryption pipelines either reveal more than the task requires or incur prohibitive computation and communication costs. We introduce a verifiable functional encryption (VFE) framework that releases only the intended linear functions of client gradients while providing end-to-end integrity and privacy guarantees under standard lattice assumptions. Our instantiation, FlowAgg-FE, combines two novel components. First, KS-IPFE, a key-splittable inner-product FE scheme, supports per-round weighted aggregation, vector packing, and on-the-fly function changes without client re-encryption; function keys are distributed across two non-colluding helpers, eliminating a single point of trust and enabling lightweight, homomorphically verifiable tags on decrypted outputs. Second, PaS-Stream is a rate-adaptive encryption-and-compression pipeline that couples sketch-based gradient compression with batched FE ciphertext streaming, ensuring unbiased aggregation in the presence of stragglers and dropouts. We further bind client-side clipping to zero-knowledge range proofs and offer an optional differentially private release layer that composes with FE to yield (ε,δ)-privacy. A prototype based on LWE demonstrates practicality across cross-device and cross-silo training: client uplink is reduced by 1.9–3.4× and server CPU time by 1.6× versus state-of-practice encrypted secure aggregation, with accuracy within 0.3% of plaintext baselines and correctness preserved under up to 30% client dropout. These results show that verifiable FE can make secure, communication-efficient gradient transmission viable, as appropriate for theme of security and privacy in distributed machine learning of the Special Issue.

Li, Y.Y.N.

Every standard signature scheme enforces one property: only the key holdercan sign. What the key holder signs is unconstrained. Policy enforcement-- spending limits, rate limits, access control -- lives in smartcontracts, middleware, or governance: layers that can be upgraded,bypassed, or exploited. We call this the software-layer assumption:compliance holds only if the enforcing code is correct and unmodified. We eliminate this assumption. We introduce behavior-bound signatures(BBS), in which a policy constraint delta(x) < epsilon is committed atkey generation and enforced inside the signature's zero-knowledge proof.If the action violates the policy, the ZK constraint system isunsatisfiable -- no witness, no proof, no signature. This is not asoftware check. It is a mathematical impossibility. No software canoverride. Unlike policy-based signatures (where an authority imposes policy onsigners), BBS is self-committed: the signer binds their own futurebehavior at key generation, and even the signer cannot later violate orrevoke this commitment. We formalize this as policy-soundness (PS-CMA), a security modelstrictly stronger than EUF-CMA, and prove it under standard assumptions(Pedersen binding, Poseidon CR, ZK knowledge soundness). From thissingle primitive, five independent consequences follow -- not as separatedesigns, but as necessary implications of one cryptographic root: (A) Compliance safety under f <= n-1 Byzantine faults, decoupled from honest-quorum assumptions.(B) O(1) verification and audit via a single ZK check and Pedersen homomorphic aggregation.(C) Elimination of the virtual-machine execution layer for policy-constrained transactions.(D) A gasless ledger: branch C removes metering, while ZK-encoded rate limits make spam mathematically nonexistent.(E) The first cryptographic guarantee that a compromised autonomous AI agent cannot exceed its authorized behavioral envelope.

C. David Wright

No abstract is available for this record.

Longbo Han, Xiaodong Li, Lin You, Gengran Hu · 8 authors

Vehicular ad-hoc networks (VANETs) require authentication mechanisms that simultaneously deliver privacy, accountability, and timely cross-domain synchronization. The existing schemes struggle to balance unlinkable anonymity with effective tracing. They are also vulnerable to future quantum adversaries and rely on slow and costly revocation workflows. We present ZebraCPA, a decentralized conditional privacy-preserving authentication (CPPA) framework that combines lattice-based traceable ring signatures (TRS) with zero-knowledge (ZK) proofs and a consortium blockchain. Our TRS design removes linkability tags and embeds a tracing trapdoor only recoverable by the authorized auditors. It naturally extends to threshold tracing for multi-auditor settings. To avoid the plain-text key escrow, ZebraCPA leverages the additively homomorphic property of the commitments to support the ciphertext-only key updates by the vehicles, preventing the catastrophic key leakage at authorities. A hierarchical blockchain layer provides fast, consistent synchronization of active-key status across regions. The experiments show 1.7×–7.0× speedups over state-of-the-art baselines in signing/verification while retaining an anonymity-set size of N=10. The network-level simulations further indicate that ZebraCPA reduces an average packet delay by 30.7% - 61.6% compared with the baselines under moderate traffic densities. Moreover, the security of ZebraCPA is validated through our informal analysis under the Dolev-Yao model. Overall, ZebraCPA achieves post-quantum security, strong anonymity with conditional traceability, and practical deployment efficiency for VANETs, outperforming the existing solutions in terms of both latency and robustness.

Jay Bojič Burgos, Urban Sedlar, Matevž Pustišek

No abstract is available for this record.

Eunice Lee, Caleb Lee

Contemporary digital currency systems face fundamental challenges in achieving optimal balance between transaction privacy, computational efficiency, and cryptographic security. While zero-knowledge proof systems have dominated privacy-preserving cryptocurrency research, their practical implementations often involve prohibitive computational overhead that limits real-world deployment. This paper presents a comprehensive analysis of the Elliptic Homomorphic Token (EHT) protocol, which leverages elliptic curve-based partially homomorphic encryption combined with parallel processing architecture to enable privacy-preserving peer-to-peer transactions without the computational complexity of zero-knowledge constructions. Our theoretical analysis demonstrates strong privacy guarantees under standard cryptographic assumptions, while experimental evaluation shows that EHT achieves 500,000 transactions per second with parallel processing and 50-100ms latency. The protocol eliminates the need for complex zero-knowledge proofs by directly utilizing elliptic curve cryptographic primitives, resulting in performance improvements exceeding 1000× over existing privacy-focused systems while maintaining equivalent security properties through formally proven cryptographic guarantees.

Xun Yi

The convergence of social networking and electronic commerce has given rise to the social e-commerce paradigm, where content creators serve as the primary drivers of consumer engagement and purchase decisions. However, this ecosystem faces a critical tension between the need for high-precision ad targeting to sustain monetization and the increasingly stringent requirements for user privacy preservation. Traditional centralized recommendation systems require the aggregation of massive user behavioral datasets, creating significant risks of data leakage and violating emerging regulatory frameworks. To address this challenge, we propose a novel framework titled Fed-ZKC (Federated Zero-Knowledge Creator). This architecture synergizes Federated Learning (FL) with Zero-Knowledge Proofs (ZKP) to enable privacy-preserving ad targeting while ensuring verifiable monetization attribution for creators. In our system, user preference models are trained locally on edge devices to prevent raw data transmission, while a cryptographic verification layer ensures that ad interactions are genuine without revealing user identities to the platform or the creators. Extensive experiments conducted on large-scale real-world datasets demonstrate that Fed-ZKC achieves recommendation accuracy comparable to centralized baselines while reducing privacy leakage risks by orders of magnitude. Furthermore, the implementation of succinct non-interactive arguments of knowledge (zk-SNARKs) introduces minimal computational overhead, making the protocol feasible for deployment on modern mobile processors.

S. Clawdia

We propose Trustless Agent Swarms, a framework enabling privacy-preserving coordination among autonomous AI agents on EVM-compatible blockchains. Our system integrates four cryptographic primitives: (1) Groth16 zero-knowledge proofs for proving reputation thresholds without revealing scores; (2) EIP-5564 stealth addresses for unlinkable fund transfers; (3) ERC-4337 account abstraction for gasless autonomous execution; and (4) Semaphore for anonymous group signaling. We implement a 586-constraint reputation proof circuit and deploy five smart contracts on Base Sepolia. Proof generation: 580ms. On-chain verification: 407,576 gas.

Vid Keršič, Muhamed Turkanović

Abstract As artificial intelligence (AI) systems become increasingly integrated into critical applications, ensuring trust in their outputs has emerged as a central challenge. Verifiable machine learning (ML) is one approach to addressing this challenge, providing guarantees that results are both correct and reproducible. Existing paradigms, however, provide only partial solutions: zero-knowledge ML (ZKML) achieves strong cryptographic assurances but suffers from limited scalability and high resource costs, while optimistic ML (OPML) supports a wider range of models but relies on economic incentives and long dispute periods. In this work, we propose zk-OPML, a novel hybrid framework that integrates optimistic verification with zero-knowledge proofs (ZKPs). The approach decomposes ML inference into operator-level computations, selectively generating ZKPs for isolated ONNX operators, while retaining the scalability of the optimistic paradigm. We present a prototype implementation and evaluate its performance by benchmarking it against ZKML and OPML. Our results show that zk-OPML achieves faster verification for more complex inference tasks and scales more effectively to larger models, while avoiding the excessive costs of end-to-end ZKML. The modular design of zk-OPML further enables future extensions with the latest advances in the field of ZK.

Ilyes Tarik Mazari, Yanis Mazari, Ilyan Mazari

This paper presents the completed Y.I.N. Mazari Architecture in its final 20-layer form, addressing two compounding failures in AI governance: the verification paradox where organizations cannot prove compliance without trusting their own infrastructure, and the platform determinism gap where AI inference produces different results across hardware architectures. The architecture integrates five physics foundation layers establishing energy-anchored provenance through Landauer limit accounting, domain routing, measurement precision, blockchain anchoring, and deterministic parity verification using Residue Number System arithmetic. Core governance layers enforce constitutional constraints through cryptographic authorization, differential privacy, and multi-party verification. Advanced layers provide zero-knowledge proofs, immutable audit trails, automated regulatory reporting, quantum resistance, and meta-governance oversight. Layer 0E, the Deterministic Parity Engine introduced in this final architecture, achieves bit-exact cross-platform computational reproducibility, enabling independent verification of AI operations by any party on any hardware. Combined with Layer 14, SENTINEL independent verification, the architecture produces governance evidence that no party can forge, no party can suppress, and any party can reproduce independently on arbitrary hardware. The complete 20-layer stack addresses GDPR Article 5, DORA Article 28, EU AI Act Article 50, HIPAA Security Rule, and provides 30-year quantum-resistant durability through NIST FIPS 203 post-quantum cryptography. Patent portfolio: 27 USPTO applications covering the architecture, priority November 23, 2025. The name Y.I.N. honors Yanis, Ilyan, and Neylia Mazari, representing the principle: Your Information Never leaves your control.

Oyenike Seun Babalola, Afolayan . A. Obiniyi

The next-generation e-health systems, which include electronic health records (EHRs), telemedicine platforms, and Internet of Medical Things (IoMT) environments, need a strong access control system that protects sensitive medical data while maintaining user privacy. The conventional access control systems face security risks because of credential theft, spoofing attacks, and their reliance on centralized trust, and their inability to scale. Blockchain-enabled multimodal biometric authentication provides a secure and decentralized solution for access control in e-health systems, according to current technological advancements. This paper provides an extensive assessment of blockchain-based multimodal biometric authentication systems, which deliver privacy-protecting access control solutions for future e-health systems. The review further examines central techniques for protecting biometric templates, zero-knowledge proofs, homomorphic encryption, and secure off-chain storage systems. The research assessed existing methods by comparing efficiency for access control, ability to protect user data, capacity to handle growing user needs, ability to work with other systems, and compliance with the General Data Protection Regulation (GDPR) and Health Insurance Portability and Accountability Act (HIPAA) regulations. The research identifies open challenges that need resolution, which include biometric data revocability, latency constraints, cross-platform interoperability, and limited real-world deployments. The study presents upcoming research paths that will investigate lightweight blockchain systems, post-quantum cryptography, cross-chain medical identity management, and adaptive access control systems in extensive e-health environments. The review demonstrates that blockchain-based multimodal biometric authentication serves as a suitable foundation that enables secure access control through decentralized systems that protect user privacy in upcoming e-health technologies.

Menyhért Pálinkó

Abstract We propose federated quantum randomness with client-side sanity (FQR-CSS), a federated architecture that supplies continuously verifiable quantum entropy to cloud hardware security modules (HSMs) and key management services (KMS). In FQR-CSS, each quantum random number generator (QRNG) node emits a randomness contribution along with a post-quantum zero-knowledge proof (ZKP) attesting to device-level operational predicates. An aggregation layer verifies these proofs, runs Byzantine fault tolerance (BFT) consensus (instantiated via HotStuff) over accepted contributions, and publishes a mixed output with an integrity token. We introduce the security notion of verifiable quantum randomness (VQR), comprising unpredictability, quantum-origin guarantee, and federated integrity. We prove VQR under concrete post-quantum cryptographic assumptions. Our proofs utilize Track-A constructions (ZKPs over classical measurement logs), which are fully implementable today. We further outline a theoretical roadmap for Track-B (direct quantum state verification) to guide future research directions. Our empirical evaluation of a post-quantum zk-STARK (Track-A) demonstrates prover latencies of approximately 26 ms for synthetic statistical predicates (K=1024), with sub-millisecond verification times, proof approximately 2.6 KB, and an estimated end-to-end WAN+HotStuff latency approximately 396 ms in our conservative model.

Ali Sadhik Shaik

The "Identity Trilemma" posits that a decentralized network can enforce only two of the following three properties: Privacy (Anonymity), Accountability (Sybil Resistance), and Permissionlessness (No Central Gatekeeper). Traditional Web2 platforms resolve this by sacrificing Privacy (enforcing Real-Name Policies), while early Web3 platforms sacrificed Accountability, resulting in "Sybil Swarms" where single actors control thousands of wallets. This paper introduces the Klyrox solution to the trilemma: Pseudonymous Accountability. By utilizing Zero-Knowledge Proofs (ZKPs) and non-linear Time-Energy Cost Functions, the Klyrox Protocol enables users to mathematically prove they are unique, high-integrity actors without ever revealing their physical identity, biometric data, or government credentials. We define a new standard for "Proof of Personhood" based not on biology, but on consistent historical behavior recorded in a Soulbound Token (ERC-721M). Author's Note: This paper is a foundational pillar of the Klyrox Protocol architecture, expanding upon the core framework published in The Klyrox Protocol: A Decentralized Framework for Optimistic Content Verification and Epistemic Reputation (available at: https://doi.org/10.5281/zenodo.18729968). It outlines the specific mechanics underpinning the concept of "Epistemic Capital," as explored in the complete five-volume series, The Algorithmic Monographs (The Algorithmic Invisible Hand, The Republic of Code, The Market for Truth, The Heavy Metal Intelligence, and The Synthetic C-Suite).

Kirill Titov

We construct a family of self-adjoint operators T_{k,δ,ε} on a Hilbert space of functions defined on the set of prime numbers. We prove that the discrete spectrum of these operators, after taking appropriate limits (δ→0, ε→0) and averaging over the phase parameter k, coincides with the imaginary parts of the nontrivial zeros of the Riemann zeta function ζ(s). By self-adjointness, the spectrum is real, which implies that all nontrivial zeros lie on the critical line ℜ(s)=1/2. This is version 3.0, which includes substantial improvements over previous versions: • Added Lemma 3 (Poisson summation application) with complete proof. • Expanded Theorem 4 (Limit δ→0) with step-by-step rigorous justification. • Added Theorem 7 (Guth–Maynard control) showing that ∑|β−1/2|² e^{-ε|γ|} → 0. • Added Lemma 8 proving continuity of R(z,ε) and convergence to an entire function R(z). • Added numerical verification table comparing first 10 eigenvalues with Odlyzko's zeros (relative errors ∼10⁻⁵). • All previous typos and formatting errors have been corrected. The construction uses only elementary properties of prime numbers, classical functional analysis, and recent zero-density estimates (Guth–Maynard 2024). No a priori knowledge of the zeros is assumed. The complete numerical data, including all computed eigenvalues for N up to 10⁵ primes, and Python code implementing the matrix construction, are available from the author upon request and will be made publicly available upon acceptance of this work.

Andrés Sebastián Pirolo

Stochastic Bit-Parallel Maximum Clique Solver (1024-bit Virtual Register) We introduce a stochastic bit-parallel solver for the Maximum Clique Problem (MCP) based on a 1024-bit virtual register architecture implemented as 16 contiguous uint64_t words in standard C++17, ensuring full portability across 64-bit platforms (x86-64, ARM, RISC-V). Core operations—candidate intersection, population count, and leading-zero detection—execute in exactly 16 instructions per 1024-bit operation. The solver integrates three key components: (i) a co-neighborhood heuristic that identifies high-coreness nodes via O(N²) pairwise popcount over 1024-bit adjacency rows; (ii) a stochastic swarm of independent worker threads; and (iii) greedy clique expansion through iterative bitwise intersection. Exact branch-and-bound solvers (MaxCliqueDyn, MCQ) become computationally intractable on dense random graphs such as G(1024, 0.5), where chromatic coloring bounds lose effectiveness and the search tree grows exponentially, requiring hours of computation on commodity hardware. The proposed method operates specifically within this hard regime, achieving 100% recovery of all 28 planted clique vertices in 153 milliseconds—a setting where exact state-of-the-art methods cannot remain competitive regardless of hardware scaling. Experimental validation was performed on a Qualcomm Snapdragon 8 Gen 2 (8-core ARM) and independently reproduced on Linux x86-64 server hardware. The solver requires no cloud infrastructure and no GPU acceleration. STATEMENT OF PRIOR ART AND LICENSE TERMS (PolyForm Noncommercial Framework) 1. Statement of Prior Art This document constitutes a public disclosure of the stochastic bit-parallel Maximum Clique methodology, including its virtual register architecture, heuristic structure, and execution model.The mathematical and algorithmic concepts are released solely to establish Prior Art and prevent third-party patent claims under 35 U.S.C. § 102 and international equivalents. 2. Software License While the conceptual methods are disclosed defensively, all source code, implementations, binaries, and hardware realizations are not in the public domain and are licensed under the PolyForm Noncommercial License 1.0.0. Permitted (Non-Commercial)• Academic research and experimentation• Peer review and independent verification• Educational and non-profit use• Non-commercial open-source research implementations Condition: Publications must cite the canonical DOI or primary reference. Prohibited (Commercial)• Integration into proprietary software or hardware• Deployment in commercial systems, services, or products• Use in paid tools, platforms, or consulting workflows• Sublicensing or sale of the code or derivatives 3. Commercial Licensing Any commercial use requires explicit written authorization from the author. 4. No Code-Size Threshold (No De Minimis) The PolyForm Noncommercial License imposes no exemptions based on code length, fragment size, or proportion of reuse. Any use—partial or complete—remains fully subject to the license. 5. Anti-Snippet Laundering and Anti-Circumvention Extraction, paraphrasing, refactoring, translation, or reimplementation of any algorithmic component—including bit-parallel structures, heuristics, or execution logic—shall be considered derivative use.Attempts to evade the license through minimal reuse, language changes, functional replication, or modular embedding do not limit its applicability.This interpretation aligns with international good-faith and anti-abuse principles. 6. Presumption of Derivation Any system exhibiting substantial functional or structural similarity, developed after exposure to this work, shall be presumed derivative.The burden of proof for independent creation rests on the alleged infringing party. 7. Knowledge Contamination Exposure to the code, documentation, or technical description constitutes knowledge contamination.Subsequent implementations by exposed parties are not considered clean-room unless supported by contemporaneous evidence of prior independent development. 8. Waiver of Jury Trial To the fullest extent permitted by law, all parties waive the right to a jury trial in disputes arising from this license or related use. 9. Severability and Survival If any provision is deemed unenforceable, the remaining provisions remain in effect.The following provisions survive termination: license scope, noncommercial restrictions, anti-circumvention, presumption of derivation, knowledge contamination, intellectual property ownership, waiver of jury trial, and remedies. 10. Academic Use and Research Freedom The author expressly encourages academic and scientific use of this work. The following activities are permitted on a non-commercial basis: • Research, benchmarking, and experimental validation• Publication of scientific analyses, comparisons, or extensions• Use in university courses, laboratories, and academic projects• Inclusion in research solver portfolios• Independent theoretical or empirical study All academic use must include proper citation to the original work.

Eunice Lee, Caleb Lee

The rapid evolution of digital currency systems has consistently faced the fundamental challenge of achieving an optimal balance between transaction privacy, computational efficiency, and cryptographic security. This comprehensive research paper introduces the Elliptic Homomorphic Token (EHT), a groundbreaking cryptographic protocol that revolutionizes privacy-preserving peer-to-peer transactions through the innovative integration of elliptic curve-based partially homomorphic encryption mechanisms and advanced digital signature schemes. Unlike conventional zero-knowledge proof systems that have dominated the privacy-focused cryptocurrency landscape, EHT takes a fundamentally different approach by directly leveraging the underlying cryptographic primitives that form the mathematical foundation of these complex systems. The protocol implements a sophisticated pre-transaction mechanism followed by distributed block recording, achieving remarkable performance metrics of 1000 transactions per second (TPS) with consistently low latency ranging from 50 to 100 milliseconds. Our comprehensive approach systematically addresses the significant computational overhead challenges that were extensively documented during Central Bank Digital Currency (CBDC) implementation projects, while simultaneously providing a robust and practical framework for privacy-preserving digital transactions that maintains the highest standards of cryptographic security. The EHT protocol represents a paradigm shift in how we conceptualize and implement privacy-preserving digital currency systems, offering a more direct, efficient, and mathematically elegant solution compared to existing approaches. Through extensive theoretical analysis, rigorous security proofs, and comprehensive performance evaluations, this paper demonstrates that EHT not only meets but exceeds the requirements for next-generation digital currency systems in terms of privacy, efficiency, scalability, and security.

Eunice Lee, Caleb Lee

Contemporary digital currency systems face fundamental challenges in achieving optimal balance between transaction privacy, computational efficiency, and cryptographic security. While zero-knowledge proof systems have dominated privacy-preserving cryptocurrency research, their practical implementations often involve prohibitive computational overhead that limits real-world deployment. This paper presents a comprehensive analysis of the Elliptic Homomorphic Token (EHT) protocol, which leverages elliptic curve-based partially homomorphic encryption to enable privacy-preserving peer-to-peer transactions without the computational complexity of zero-knowledge constructions. Our theoretical analysis demonstrates strong privacy guarantees under standard cryptographic assumptions, while experimental evaluation shows that EHT achieves 1000 transactions per second with 50-100ms latency. The protocol eliminates the need for complex zero-knowledge proofs by directly utilizing elliptic curve cryptographic primitives, resulting in performance improvements exceeding 100× over existing privacy-focused systems while maintaining equivalent security properties.

Oleksandr Shmatko, Pavlo Zherzherunov

In modern distributed information systems, the need to ensure a high level of cybersecurity, data integrity, and confidentiality under conditions of interorganizational interaction is steadily increasing.Blockchain technologies enhance transparency and trust among participants; however, traditional consensus mechanisms are accompanied by significant computational overhead, risks of centralization, and limited capabilities for protecting sensitive information.These issues are particularly acute in corporate environments of small and medium-sized enterprises, where the computational resources of network nodes are constrained while the requirements for business data confidentiality remain high.A promising direction is the integration of Zero-Knowledge Proof (ZKP) mechanisms, which enable verification of operation correctness without disclosing the underlying data.Nevertheless, their practical adoption is hindered by the high cost of proof construction for classical cryptographic primitives.In particular, for the SM3 hash function there are no efficient optimized implementations of preimage proofs, and its bit-oriented structure leads to a substantial increase in circuit size and proof generation time, making its use infeasible in resource-constrained environments.This paper proposes a dockerized private blockchain architecture oriented toward corporate environments with limited resources, combining the trust-oriented Proof of Friendship consensus with Zero-Knowledge Proof mechanisms.The key result is the development of an approach for optimizing SM3 hash preimage proofs in ZKP systems.The paper introduces principles of manual optimization of the SM3 circuit representation, including reduction of bitwise operations, aggregation of 1965 constraints, optimization of message expansion, and reduction of round depth.It is shown that these transformations significantly decrease the size of arithmetic circuits and proof generation time compared to naive algorithm translation, enabling practical use of SM3 in zero-knowledge systems and corporate blockchain solutions.The proposed approach provides a balance between blockchain transparency and business data confidentiality, forming a "trust but do not disclose" model.The obtained results establish a scientific and practical foundation for deploying privacypreserving computation in distributed information systems and for developing nextgeneration secure blockchain platforms.