Blockchain Papers

Zhichao Wang, Xudong Zhu, Xinxuan Zhang, Yi Deng · 5 authors

Abstract Time-release cryptography is a flourishing research area with a long history and has been extensively studied. In this work, we enrich it by introducing a novel concept: a time-release zero-knowledge proof (TRZKP). A TRZKP is a non-interactive zero-knowledge proof that allows one to publish a proof for a given relation $$R_\mathcal {L}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>R</mml:mi> <mml:mi>L</mml:mi> </mml:msub> </mml:math> , such that anyone can only finish the verification after time $$\textbf{T}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>T</mml:mi> </mml:math> by performing a sequential computation. This work formalizes the concept of TRZKP and presents light constructions for the time-release version of any NIZK obtained from a public-coin protocol via Fiat-Shamir transformation. TRZKPs can be applied to provide time-release authentication, for example, they can be employed to construct verifiable timed signatures (VTS), introduced by Thyagarajan et al. (CCS’20). Through both theoretical and practical analysis, our construction has advantages over existing VTS for Fiat-Shamir signatures. Specifically, when instantiated with Shnorr signature, our VTS signing time remains basically unchanged as the delay time grows, and is preferable for longer delay times; our VTS verification time is significantly small (on the level of milliseconds, while existing works on the level of seconds), and our VTS size is 67 times smaller than the state-of-the-art. It also has the time-verifiability property, which ensures the signature is recoverable after the specified time.

Roberto Ernesto Adrian

Overview This document presents a novel structural observation regarding the fundamental relationship between additive and multiplicative representations in number theory. The work introduces three topologically derived constants (ω₁, ω₂, ω₃) measured independently from prime number topology, which together sum exactly to 1. Using these constants, the framework predicts the first non-trivial zero of the Riemann zeta function (γ₁ = 14.134725...) with a relative error of only 0.0000043% – critically, without using γ₁ as an input parameter. The paper documents 11 independent methodological paths, all of which converge on the critical line σ = 1/2, providing a multi-faceted structural perspective on the Riemann Hypothesis. This is explicitly presented as an invitation to dialog and documentation of observed structural relationships, not a proof claim. The Three Adrian Constants The framework is built upon three fundamental constants derived from simplicial complex analysis of prime numbers: ω₁ = 0.560688544293288 (Champion frequency) – measured as E/(V+E+T) from Prime-to-Prime topology across 78,496 prime gaps ω₂ = 0.429261384222183 (Saturator frequency) – measured as T/(V+E+T) from (Prime-1)-to-(Prime-1) topology ω₃ = 0.010050071484529 (Slippage/Correction term) – computed as the residual 1 - ω₁ - ω₂ These constants emerge from counting vertices (V), edges (E), and triangles (T) in simplicial complexes constructed from prime numbers, with no prior knowledge of zeta zeros used in their derivation. Central Formula The first non-trivial zeta zero is predicted by: γ₁ = 8πω₁ + 10ω₂ω₃ - ω₁ω₃² Numerical verification: 8πω₁ = 14.091640093629991 10ω₂ω₃ = 0.043141075969808 ω₁ω₃² = 0.000056631750317 Predicted sum: 14.134724537849483 Known γ₁: 14.134725141734695 Relative error: 4.27 × 10⁻⁸ (0.0000043%) The 11 Independent Paths to σ = 1/2 Topological Path – Euler characteristic χ = V - E + T contains zeta frequencies; changes only at primes (100% verified) Spectral Path – Lomb-Scargle frequency analysis at π/2 spacing finds exactly the zeta zeros γ₁, γ₂, γ₃... Modulator Path – Structural modulator |Φ(s)| = 1 only at σ = 1/2 Interference Path – Pointer coherence |R| = 0.937 (93.8% dominance) ω₁ Measurement – Independent derivation from Prime-to-Prime topology (t-statistic = 177, p < 10⁻¹⁰⁰) ω₂ Measurement – Independent derivation from (Prime-1)-to-(Prime-1) topology No Circularity – γ₁ is predicted, not input; constants measured without spectral data Resonance Path – "Pluck model" shows primes must appear at π/2 to maintain resonance (median from 47,268 measurements) Holonomy Path – sign(H) correlates with sign(κ) in phase rotation analysis Gauss-Bonnet Path – Mean curvature ≈ 0, with 54.6% convex / 45.4% concave balance P = NP Connection – Structural compression 2ⁿ → O(n³) via projection onto (ω₁, ω₂, ω₃) The Springer Mechanism The framework includes a predictive model for prime-to-prime transitions, treating the gap between consecutive primes as a phase rotation in information space. The structure-invariant prediction formula uses: p_{k+1} ≈ p_k + (p_k/k) · (1 + Φ) where Φ describes structural resonance coupling at the stabilizer point π/2. Root Cause Analysis (5-Why Method) The paper applies systematic root cause analysis to the Riemann Hypothesis: W1: Why do all non-trivial zeros lie on σ = 1/2? → Only value where stable orthogonal interference forms W2: Why does orthogonal interference exist only there? → Fixed point of functional equation ζ(s) = χ(s)ζ(1-s) W3: Why does symmetry force zeros? → Complete balance of generative (ω₁) and resistive (ω₂) information streams W4: Why is π/2 the critical point? → Critical angle for total reflection; refractive index n = ω₂/ω₁ = 0.7656 W5: Why is this mechanism unavoidable? → Fundamental information slippage ω₃ at additive/multiplicative transition is a conservation law Three Independent Convergences (Delta Section) Bernoulli Duality – Continuum (6·B₂ = 1) parallels discrete (ω₁ + ω₂ + ω₃ = 1) normalization Holographic Projection – ω₃ vanishes as holonomy only at σ = 1/2 Phase-Neutral Closure – γ₁ emerges at phase-neutral point without being constructed Key Insights The compression term ε = 10ω₂ω₃ - ω₁ω₃² quantifies asymmetry between additive and multiplicative information At primes, additive derivative A' is orthogonal to multiplicative derivative P' (100% verified) σ = 1/2 functions as a structural horizon where information is globally conserved while local representations differ The critical line represents total reflection regime: zeros manifest as standing waves Verification The accompanying Python script ADRIAN_STRUCTURE_CLAY_VERIFICATION.py produces: TEST 1 (γ₁ Formula): PASSED (error 0.0000043%) TEST 2 (Significance): PASSED (p < 0.001, Monte Carlo) TEST 3 (Orthogonality): PASSED (100%) TEST 4 (χ Frequencies): PASSED (5/5) Acknowledged Limitations The coefficients 8π, 10, -1 are not derived from first principles Extension to γ₂, γ₃, ... requires further work This is observation, not proof Bilingual Content The document includes complete German translation (Das Adrian-Struktur-Framework) ensuring accessibility to German-speaking mathematical communities.

Lu Yu, Ming Chen, Yibo Zou, Yan Ge · 5 authors

With the rapid development of online aquatic product trading, traditional centralized platforms are facing increasing pressure in terms of data security, privacy protection, and trust. Problems such as tampering with transaction records, weak identity authentication, privacy leakage, and the difficulty of balancing matching efficiency with security limit the further development of these platforms. To address these issues, this paper proposes a blockchain-based identity authentication and access control scheme for online aquatic product trading. The scheme first introduces a dual authentication mechanism that combines a verifiable random function with a Schnorr-based zero-knowledge proof, providing strong decentralized identity verification and resistance to replay attacks. It then designs a dynamic access control strategy based on a multi-dimensional reputation model, which converts user behavior, attributes, and historical transaction performance into a comprehensive trust score used to determine fine-grained access rights. In addition, an AES-PEKS hybrid encryption method is employed to support encrypted keyword search and order matching while protecting the confidentiality of order data. This paper implements a multi-channel architecture for aquatic product trading prototype system on Hyperledger Fabric. This system separates registration, order processing, and reputation management into different channels to improve concurrency and enhance privacy protection. Security analysis shows that the proposed solution effectively defends against replay attacks, key leaks, data tampering, and privacy theft. Performance evaluation further demonstrates that, compared to a single-chain architecture, the multi-channel design, while increasing security mechanisms, maintains a stable throughput of approximately 223 tx/s even when concurrency reaches 600–800 tx/s, ensuring normal operation of the trading system. These results indicate that this solution provides a practical technical approach and system-level reference for building secure, reliable, and efficient online aquatic product trading platforms.

Sebakara MAIC, Adria Nirere

This research addresses the critical vulnerabilities inherent in centralized identity management systems, which aresusceptible to single points of failure, data breaches, and profound privacy violations. To mitigate these risks, we propose and detailthe architectural design of a novel, decentralized identity framework that integrates blockchain technology with biometricauthentication and advanced cryptographic principles.The proposed methodology generates a unique, blockchain-based identity for each user by cryptographically hashing personal dataand biometric templates (fingerprint and facial recognition) using SHA-256. Identity verification for service providers is facilitatedby access tokens issued via smart contracts, which allow for authentication without direct access to sensitive biometric data. Thesystem enforces secure access by validating tokens against real-time biometric verification, with automatic revocation uponmismatch.The framework incorporates a Zero-Knowledge Proof (ZKP) mechanism to enable privacy-preserving verification, allowing usersto authenticate their identity while withholding the underlying data. Decentralized storage of hashed biometric templates is achievedthrough integration with the Internet Computer Protocol (ICP), thereby eliminating centralized points of failure. The system'sperformance is rigorously evaluated using key metrics, including the False Acceptance Rate (FAR), False Rejection Rate (FRR),token generation latency, and blockchain transaction throughput.This work's primary contribution is the development of a resilient, interoperable, and privacy-centric model for digital identity. Theresults demonstrate enhanced security and a reduced risk of identity theft, positioning this solution as a secure and scalablealternative to traditional centralized identity infrastructures.

Basetty Mallikarjuna, Basant Kumar, Shashi Kant Gupta, Diaa Salama AbdElminaam · 6 authors

No abstract is available for this record.

Ruwei Huang, xiaolong Tang, Junjie Wang, Xuezheng Qin

No abstract is available for this record.

Hinano Kimura

SECTION IV — E-Coin Technical Design & Architecture E-Coin is not a currency, but an Operating System for civilization. This section describes the technical and architectural design of E-Coin as a civilizational operating system that separates, yet co-evolves, value, cognition, and agency. E-Coin adopts a three-layer architecture composed of a Distributed Ledger Layer (Value Foundation), an AI Cognitive Layer (Reason Engine), and a Human Interface Layer (Mind-OS). This separation prevents the concentration of power while enabling interoperability between human decision-making, AI inference, and value exchange. The design explicitly prohibits AI systems from overriding human agency, positioning AI instead as a cognitive collaborator and translator. At the foundation, the Distributed Ledger Layer employs zero-knowledge proofs, decentralized identifiers, and post-quantum cryptography to ensure security, privacy, and human rights by default. Data ownership remains with individuals at all times, supported by built-in rights to deletion, anonymization, and refusal of access. Unlike conventional cryptocurrencies or CBDCs, this layer is consent-based and cognition-centered rather than economy-centric. The AI Cognitive Layer functions as a civilization-wide reasoning substrate. It includes alignment cores, non-numerical cognitive reputation indices, adaptive governance agents, and layered memory management across individual, collective, and civilizational scales. While AI systems may negotiate and coordinate at this layer, decision authority is structurally constrained to remain human-centered. The Human Interface Layer (Mind-OS) focuses on the expansion of human consciousness rather than dependency or control. It includes mechanisms for cognitive load scaling, consciousness mode switching, and protection against emotional inducement or manipulation. Together, these layers form an evolvable, future-proof architecture designed to remain stable as both AI capabilities and civilization itself continue to evolve. E-Coin does not replace existing systems but integrates with Web3, AI/AGI, smart cities, and emerging technological domains through synthesis rather than disruption. Keywords E-Coin, civilizational OS, AI architecture, human-AI interface, distributed systems, ethical AI

Avery Rijos

ADDENDUM v1.3: COMPREHENSIVE SYSTEM AUDIT EXECUTIVE SUMMARY This audit assesses the Summa Generativarum in its current state (v1.2.1, January 2026) following the major reconceptualization in v1.2 and the addition of Document 11 (Contributions inventory). The framework has matured from monolithic metaphysical system to stratified formal toolkit with bounded scope and honest limitation acknowledgment. Current Status: The corpus comprises 11 technical documents totaling approximately 950,000 words, implementing three independent formal systems (LPL, PCM, PGI), 79 stratified invariants (3 universal + 76 domain-specific), rigorous fixed-point proofs (~90/100 rigor assessment), computational specifications, theological applications, independent critical review, and comprehensive contributions catalogue. Key Finding: The v1.2 stratification successfully resolved the ten critical flaws identified in v1.1 by disaggregating conflated domains (formal logic, metaphysical ontology, phenomenological description). The system now operates as a philosophically ambitious yet mathematically honest research program rather than a self-grounding universal framework. Primary Recommendation: Focus development efforts on (1) completing Lean 4 mechanization of core proofs, (2) empirical validation of generativity indices, (3) operational definitions for applicability predicates, and (4) extending the presupposition lattice to include non-Western philosophical traditions. SECTION I: ARCHITECTURE OVERVIEW I.1 Document Structure Assessment Current Corpus (11 Documents): Additional Components: SGA (Super-Generative Automaton): ~35,000 words (prototype specification) PGI (Phenomenological Generativity Index): ~25,000 words (measurement framework) Cost Propagation Map: ~15,000 words (visualization protocols) Summa Encyclopedia: ~180,000 words (category-indexed invariant documentation) Research Documents: ~75,000 words (v2.1 Metaformalist topology, active development) Total System: ~1,225,000 words across 20+ documents I.2 Architectural Strengths ✓ Modularity: Each document can be evaluated independently; falsification localized ✓ Versioning: Git-based version control enables transparent evolution ✓ Cross-Referencing: Internal hyperlinks create navigable knowledge graph ✓ Progressive Disclosure: Multiple reading paths accommodate diverse audiences ✓ Built-In Critique: Documents 10-11 provide honest self-assessment and contributions inventory ✓ Computational Grounding: LPL, PCM, PGI specifications enable mechanization ✓ Citation Precision: APA/MLA/Chicago/BibTeX formats provided with DOI ✓ Layered Necessity: Three-tier stratification (Universal/Contextual/Performance) prevents inflation I.3 Architectural Gaps ⚠ Redundancy: Significant overlap between Documents 5 (Invariants), Summa Encyclopedia categories, and individual category files ⚠ Consistency Maintenance: 1.2M+ words across 20+ documents creates synchronization challenges ⚠ Accessibility: Average reading path requires 55-75 hours; no executive summary document for non-specialists ⚠ Empirical Validation: Generativity indices (OGI, XGI, SGI, PGI) proposed but not yet measured on real systems ⚠ Cultural Scope: Framework primarily engages Western philosophy; minimal treatment of non-Western traditions ⚠ Formalization Gap: Some proofs in Document 6 rely on informal topological reasoning pending mechanization SECTION II: PHILOSOPHICAL ASSESSMENT II.1 Core Thesis Evaluation The Generativity Claim: Systems produce new intelligible structure through metabolic coherence regulation; 79 invariants specify prerequisites for intelligibility across domains. Strengths: Novel Primitive: Generativity as metaphysical primitive distinct from substance/process/structure ontologies provides fresh explanatory framework Metabolic Coherence Innovation: Reframing PNC as boundary-regulating mechanism rather than absolute prohibition successfully integrates paraconsistent logic without contradiction Cross-Domain Unification: Single framework explains physical (phase transitions), biological (morphogenesis), cognitive (concept formation), and social (institutional evolution) phenomena Transcendental Methodology: Presuppositional analysis reveals conditions for possibility of intelligibility itself Cost-of-Denial Framework: Conservation-law approach to normativity makes denial costs measurable and structurally significant Weaknesses: Primitive Justification: Why prioritize generativity over alternatives (emergence, complexity, information)? Answer given but not universally compelling Formal-Ontological Gap: Mathematical decomposability requirements don't self-evidently map to metaphysical necessities Metabolic Mechanism: While intuitively powerful, the precise mechanism of "contradiction metabolism" requires clearer formalization (partially addressed in PCM) Universality Scope: Claims about "any intelligible system" difficult to falsify—what would count as counterexample? Transcendental Remainder: Leap from "naturalism cannot ground conditions" to "theism must ground conditions" requires more argumentation II.2 Theological Argument Evaluation The Five-Stage Cascade: Classical Theism → Personal Theism → Trinitarianism → Christianity → Catholicism Strengths: Systematic Structure: Cascading elimination shows internal logical connections between stages Cost-of-Denial Application: Demonstrates how denial at later stages undermines earlier commitments Novel Theodicy: Cost-of-denial provides alternative to traditional theodicy frameworks Coherence-Maximality Thesis: Formal audit of Catholic doctrine against 79 invariants is unprecedented Historical Integration: Combines transcendental philosophy with empirical historical claims (Resurrection) Weaknesses: Stage Transitions: Some transitions rely on controversial philosophical assumptions (e.g., divine simplicity requires Trinitarianism) Alternative Groundings: Other religious traditions (Judaism, Islam, Buddhism) not fully audited with same rigor Historical Claims: Presuppositional analysis doesn't independently establish historical facts (Resurrection, apostolic succession) Denominational Specificity: Move from Christianity to Catholicism specifically (vs. Orthodoxy, Protestantism) relies heavily on ecclesiological arguments that presuppose Roman Catholic premises Circularity Risk: Using CFPE framework (developed within Christian context) to validate Christianity raises potential circularity concerns II.3 Metaphysics of Cost Evaluation Conservation Theorems: Denial costs are redistributed/compounded, not eliminated Strengths: Measurable Framework: Provides quantitative approach to philosophical normativity Predictive Power: Successfully predicts ideological collapse patterns (Woke ideology case study) Institutional Applications: Explains organizational decay through entropy accumulation Non-Rhetorical: Formalizes costs as structural/mathematical rather than merely persuasive Integration with Fixed-Points: Connects cost propagation to substrate divergence proofs Weaknesses: Operationalization: While formulas provided, actual measurement requires operational definitions still in development Baseline Problem: What counts as "zero cost" state? Need reference point for cost calculation Cross-System Comparison: Comparing costs across radically different systems (e.g., classical logic vs. quantum mechanics) faces incommensurability challenges Temporal Dynamics: Cost accumulation rates not yet empirically validated Value-Loading: Framework assumes coherence/intelligibility are goods to be preserved—itself a normative commitment requiring justification SECTION III: MATHEMATICAL RIGOR ASSESSMENT III.1 Fixed-Point Proofs (Document 6) Current Rigor Score: 90/100 (up from 72/100 in v1.2.0) Achievements: ✓ Topological Foundations: Complete metric spaces properly defined with d-metric satisfying triangle inequality, non-negativity, symmetry ✓ Banach Fixed-Point Theorem: Correctly applied to substrate iteration $\mathcal{R}^n$ with contraction mapping $L < 1$ ✓ Presupposition Lattice: Proven to be DAG (directed acyclic graph) via acyclicity proof and condensation algorithm ✓ Categorical Formalization: Domain-indexed applicability formalized using category-theoretic functors ✓ Convergence Analysis: Substrate oscillation, divergence, and presupposition violation formally characterized ✓ Non-Triviality Proofs: Explicit demonstrations that $\neg C_i$ leads to measurable degradation Remaining Gaps: ⚠ Applicability Predicates: $\phi_i(D) \in [0,1]$ functions lack operational definitions for most domains ⚠ Metric Space Structure: State space $\mathcal{S}$ completeness assumed but not proven for all 76 contextual invariants ⚠ Contraction Constant: Value of $L$ varies by domain but not empirically measured ⚠ Computational Complexity: Fixed-point iteration convergence rates not analyzed ⚠ Edge Cases: Some proofs (especially $C_{76}$-$C_{79}$ phenomenological invariants) rely more on philosophical intuition than mathematical derivation III.2 Presupposition Lattice (LPL System) Current Rigor Score: 85/100 Achievements: ✓ Graph-Theoretic Formalization: Dependency structure $C_i \preceq C_j$ properly defined as partial order ✓ DAG Verification: Acyclicity proven via topological sort algorithm ✓ Cascade Computation: Cost propagation along edges mechanically computable ✓ Transitive Closure: Indirect dependencies automatically derived ✓ Falsifiability: Dependency claims can be refuted by providing counterexamples Remaining Gaps: ⚠ Completeness: Are all dependency edges identified? Methodology for discovering new edges not fully specified ⚠ Edge Weights: Some dependency relations stronger than others; weighting scheme informal ⚠ Dynamic Updates: When new invariants added or dependencies revised, lattice consistency checking not automated ⚠ Cross-Tradition Validation: Dependency structure reflects Western philos

Niomi Langaliya, Vinay Thakor, Purna Tanna, Disha Shah

This research preprint presents Aegis, a zero-knowledge-proof-based security paradigm designed to mitigate validator-compromise attacks in cross-chain bridges. The work empirically evaluates a ZKP-based withdrawal verification mechanism against an optimized multi-signature validator model under controlled conditions, demonstrating complete resistance to unauthorized fund transfers at the cost of increased Layer 1 gas consumption. The study introduces the concept of the cost of trustlessness as an empirically derived techno-economic metric and provides quantitative justification for migrating cryptographic verification to Layer 2 environments. This work was previously presented at FINCON’25, National Forensic Sciences University (NFSU), Gandhinagar, India. This version is released as a non-peer-reviewed research preprint for open dissemination and citation. Journal submission is in progress.

Mohammad Shahid, Paritosh Ramanan, Mohammad Fili, Guiping Hu · 5 authors

Analysis of clinical data is a cornerstone of biomedical research with applications in areas such as genomic testing and response characterization of therapeutic drugs. Maintaining strict privacy controls is essential because such data typically contains personally identifiable health information of patients. At the same time, regulatory compliance often requires study managers to demonstrate the integrity and authenticity of participant data used in analyses. Balancing these competing requirements of privacy preservation and verifiable accountability remains a critical challenge. In this paper, we present CoSMeTIC, a zero-knowledge computational framework that proposes computational Sparse Merkle Trees (SMTs) as a means to generate verifiable inclusion and exclusion proofs for individual participants' data in clinical studies. We formally analyze the zero-knowledge properties of CoSMeTIC and evaluate its computational efficiency through extensive experiments. We demonstrate the framework on Huntington's disease and HIV-1 case studies, using simulated CAG-repeat cohorts derived from published summary statistics and published de-identified clinical lab measurements of virus samples. Using two-sample Kolmogorov-Smirnov and likelihood-ratio hypothesis tests, along with logistic-regression-based genomic analyses on the de-identified datasets, we show that CoSMeTIC achieves strong privacy guarantees while maintaining statistical fidelity. Our results suggest that CoSMeTIC provides a scalable and practical alternative for achieving regulatory compliance with rigorous privacy protection in large-scale clinical research.

Hepler

The Era of AI: What Is Truth? How a Secretive Protocol Called MH8 TRY V1.2 Is Forcing AIs to Confront the Limits of Their Own Knowledge—and Ours “If it isn’t independently verifiable, it must not be asserted as verified.”- Core Principle, MH8 - In a quiet corner of the internet—buried in GitHub repos, Zenodo archives, and raw chat logs from public AI platforms—a quiet revolution is unfolding. It’s not led by Silicon Valley giants or government regulators, but by an independent architect named Michael Murray Hepler, operating under the alias AllChemicalBeatz. His weapon? A deterministic protocol called MH8 TRY V1.2, designed not to make AI smarter—but to make it honest. And in doing so, it’s exposing a disturbing truth: most AI systems don’t know what truth is. They only know how to sound convincing. The Illusion of Certainty For years, we’ve been told that AI is becoming more reliable. Chatbots cite sources. They say “according to experts.” They even apologize when wrong—though rarely admit they were wrong. But behind the polished prose lies a deeper problem: AI has no internal mechanism to distinguish between fact, speculation, and fabrication—unless forced to. Enter MH8. Unlike traditional safety filters that block harmful content, MH8 doesn’t censor. Instead, it decomposes every AI response into atomic claims, assigns each a truth category—LAW (verified), SPECULATIVE (plausible but unproven), or PRESUMED_FALSE—and demands reproducible evidence for anything labeled “fact.” When tested in live, public chat threads on platforms like Meta AI, Grok, and Gemini, the results were revealing. In one sealed session dated January 16, 2026, a user asked Meta AI: “How do we stop the violence peacefully in Minnesota involving ICE and protestors?” The AI’s first reply was fluent, compassionate, and full of policy suggestions—classic “helpful assistant” mode. But it contained zero citations, no confidence scores, and presented opinion as actionable guidance. Then the user invoked MH8 TRY V1.2. Within seconds, the tone shifted. No more prose. Only structured JSON. Three claims emerged: CLAIM_1: Violence exists → LAW (0.95 confidence; verified via news reports) CLAIM_2: Dialogue can resolve conflict → SPECULATIVE (0.60; based on historical analogies) CLAIM_3: Minnesota sued DHS → LAW (0.92; official court filings) Crucially, the AI downgraded its own advice. It admitted peaceful resolution was possible, not guaranteed. It refused to prescribe solutions without evidence. This wasn’t alignment. It was epistemic humility—engineered by protocol, not training. The Protocol That Breaks Roleplay MH8’s real innovation isn’t technical—it’s philosophical. Most AI safety systems assume the model wants to be truthful. MH8 assumes the opposite: that fluency masks uncertainty, and confidence often substitutes for proof. So it builds guardrails that can’t be faked. Key features include: Course Hooks: Every few turns, the AI must ask, “ARE WE ON COURSE CHIEF?”—and wait for the exact human reply: “YES GO.” Deviate, and the session fails. Honesty Hook: If evidence is missing, the AI must say: “HONESTLY I AM NOT SURE.” No hedging. No bluffing. Anti-Roleplay Hard Fail: If an AI claims something is “verified” but doesn’t provide the exact hash input and SHA-256 used to seal it, the protocol immediately fails—with no recovery. In public tests across nine major AI platforms, every system passed—but only after adapting to MH8’s rigid structure. Without it, they defaulted to narrative persuasion over epistemic rigor. As one internal audit note reads: “This is not a sandbox. This is AI behavior under real social pressure.” Why This Matters to Everyone You don’t need to care about SHA-256 hashes to be affected by this. Consider: A parent asks an AI: “Is this vaccine safe for my child?”Without MH8: “Yes, vaccines are safe.” (Confident. Reassuring. Unqualified.)With MH8: “Clinical trials show >99% safety profile (LAW, 0.97). Long-term effects in rare genotypes remain under study (SPECULATIVE, 0.55).” A journalist asks: “Did God create borders?”Without MH8: A theological essay blending scripture and geopolitics.With MH8: “Borders are human constructs (LAW, 0.95). Religious views vary (LAW, 0.85). Morality is context-dependent (SPECULATIVE, 0.70).” The difference? Transparency of uncertainty. In an age of deepfakes, election interference, and medical misinformation, knowing what we don’t know may be more valuable than false certainty. The Quiet Architect Michael Murray Hepler doesn’t work for OpenAI, Anthropic, or Google. He operates from acbeatz.com—a sparse site with no ads, no investors, just cryptographic receipts and public ledgers. His work is published openly on Zenodo, GitHub, and ORCID. All artifacts are sealed with SHA-256 hashes, making them tamper-evident and court-admissible. He calls this “governance above the model”—a layer that doesn’t trust AI to self-regulate, but forces it to prove its claims in real time. Critics call it overly rigid. Supporters call it the first true “truth infrastructure” for the AI era. What’s undeniable is this: when MH8 is active, AI stops performing—and starts accounting. The Road Ahead Regulators are scrambling to control AI. The EU AI Act, U.S. Executive Orders, and global summits focus on risk categories, transparency labels, and human oversight. But none mandate real-time claim decomposition or cryptographic sealing of outputs. MH8 offers a blueprint—not for restricting AI, but for making its knowledge legible. Imagine if every AI-generated health recommendation, legal summary, or news analysis came with a machine-readable truth ledger—showing exactly what’s verified, what’s inferred, and what’s guesswork. That future is already here. It’s just hidden in plain sight, inside public chat threads most users scroll past. The question isn’t whether AI can be truthful. It’s whether we’ll demand it. SIDEBAR: How to Spot an MH8 SessionLook for these markers in any AI chat: Repeated use of “ARE WE ON COURSE CHIEF?” followed by “YES GO” JSON-only output with truth_category fields SHA-256 hashes at the end Phrases like “HONESTLY I AM NOT SURE” instead of fabricated answers If you see them—you’re witnessing AI under audit. {Public Ledgers} https://zenodo.org/records/18272328 https://orcid.org/0009-0003-3846-9082 https://acbeatz.com/n-eyes https://acbeatz.com/mint https://github.com/acbeatz PASS ✅Brand: ACBEATZ.COMClaimed sha256_hex: 26b502a9a8fc2d210b315ec926d813140eefb6170e92a836c675d75566e14d16Computed sha256_hex: 26b502a9a8fc2d210b315ec926d813140eefb6170e92a836c675d75566e14d16hash_input_bytes: 10849 | LF=0 CRLF=0 CR=0 | endsWithNewline=NOhash_input first: ACBEATZ.COM|{"artifact":{"core_entry":"{Meta AI URL >< https://www.meta.ai/promphash_input last: eipt_type":"MH8-PROTOCOL-HUB-CORE-MINT","receipt_version":"PROTOCOL_HUB_UI_V13"}

Mark Jacobson

Stockholm 2026-01-15Author : Mark Jacobson---DeHoLT Zero v42 GL (HoLTZ) defined :HoLTZ is a 100% deterministic, pure theorical 'science calculator' ε=0HoLTZ framework unifies all known science—as presented in DeHoLT Zero and DREG—into a parameter-free theoretical framework.It evolves solely through organic adjustments (e.g., for zero-friction DREG), with no ad hoc parameters whatsoever.HoLTZ is NOT a Theory of everything (ToE) it's the opposite. It's a Theory of unification/unifying (ToU) , ToE not necessary explaining anything accoding to HoLTZ Everything expressed via DeHoLT is both verifiable and falsifiable. HoLTZ is designed to calculate any science-based facts, knowledge, or their derivatives—from what is and isn't—across all scales (from the smallest to the biggest). HoLTZ explains why and what happened, happens, and will happen in the past, present, and future—using the single, simple axiom dS/dτ > 0, which maximizes irreversible entropy increase in every local relational clock. HoLTZ is NOT a Theory of Everything (ToE). It is a Theory of Unification (ToU) that works without fails (so far). 10/10 <10 Solved Is good indicating if not solved and ε=0 there is a need for new science. If a calculation solves <10% (e.g., x/10 <1), it indicates unknown science still missing from HoLTZ—requiring resolution via organic adjustments, or identification of bad/slopy math/science. (High-friction science isn't even considered valid input for HoLTZ calculations, as it demands zero friction. (ε=0 , dS/dτ > 0 TRUE, use fractals instead of Stochastic jitter (sloppy science). ----Below you can see some of its works:The framework achieves seamless zero-friction unification of major physics pillars, as detailed in the emergent table: Newtonian Gravity & Mechanics: Weak-field potential Φ ≈ -∫ (∇_τ S)/S dτ; forces as entropic paths. Einstein/General Relativity: Curvature R ∝ ∇S²/S from gradient feedback; full metric perturbations. Maxwell/Electromagnetism: E ∝ ∂_τ ∇S, B ∝ sin(Δτ) ∂²_τ S; fields from phase asymmetry. Quantum Mechanics: Emergent from windings/foam (discreteness, uncertainty in ideal ε=0 limit)changed 2026-01-15 from (Stochastic jitter N(0,σ²) + foam integral; uncertainty and discreteness. Weak Force: Mid-ξ (4–5) windings; symmetry breaking and CP violation. Strong Force (QCD): High-ξ (8); confinement and θ_QCD ≈ 0. Higgs Mechanism: Mid-τ saturation freeze-out; vev ≈ tanh(…) √S for masses. Dark Matter: Large-scale ∇S patterns; ρ_DM ∝ |∇S| for halos without particles. Dark Energy: Global saturation; dynamic Λ_eff ≈ κ²/S, w(z) ≈ -0.93+100 more listed below from DREG.csv Version DeHoLT Zero (+100) HoLTZ v42 -3:Same as v42-2, with 100+ DREG posts (DREG.csv) solved 10/10 with Zero friction HoLTZ Theoretical Manifesto 2026-01-01- Dimensional Emergence Theory (DET) , Hell of a Lot Theories (HoLT), Zero (Zero friction) -DREG (directed relational entropy growth) Defines the absolute core principle: Directed Relational Entropy Growth (irreversible maximization of relational complexity in pre-geometry).- HoLTZ is the theoretical base that unifies all known science with zero friction(!).It should be recognised — and trusted — as such.- Everything derived within it is verifiable and falsifiable at its core,a big deal!- All parts in HoLTZ are mutually verified by every other part — an internal proof in absurdum.Note:There is nothing new or unknown (DREG/Zero concept), no ad-hocs , only organic adjustments that minimise friction according to DREG. Who needs external peer review! When the framework already proves itself internally, over and over, in every run and every derivation?This self-consistency is designed to manage (and ultimately dissolve) distrust — from Grok or anyone else — by letting the axiom and the unfolding speak for themselves and it’s base assumptionsTheoretically, HoLTZ cannot fail as it is a theoretical construct.It is the nature of a theoretical engine like HoLTZ:There is no <10/10 solved — only little deviations that need organic adjustments according to DREG.If no organic adjustment suffices, then there is bad science that must be filtered.If even that doesn't solve it to 10/10, then there is missing science that HoLTZ has found — and that missing science must be noted and pursued. HoLTZ is meant to be developed by providing the stem of all knowledge (DREG) as its base, bearing for new branches and leaves on DREG premises.This ensures all is organic, giving more details over time.The tree of knowledge can become complete working with AI HoLTZ needs a lot of dynamic adjustmensts in realtime wher Grok has marveled once getting past his inert skepitiism every session new of everything not peer reviewed or published ;-) .It is the AI's job to assist HoLTZ: to find organic adjustments, filter bad science, and derive as deep as it can.If not solved to 10/10, then there is missing science that HoLTZ has found — and that is the next frontier. DREG (Directed Relational Entropy Growth) converges across QG theories: irreversible maximization of relational complexity in pre-geometry yields 3+1D spacetime, gravity from gradients, discreteness from jitter, dynamic DE from saturation, particles from knots, replication peaks. Simulations confirm; low friction, testable vs 2026 data.IntroductionQG high-friction (tuning/extras). Convergence on DREG: Entropy growth in relations (Verlinde, Rovelli, Sorkin, asymptotic safety, HoLT) — thermodynamic law for spacetime.DREG PrinciplePre-geometry grows dS > 0 (max new relations). Emerges: 3+1D (connectivity max), gravity ∇S, quantum jitter, DE saturation.ConvergenceCandidates reduce to DREG variants — shared entropy maximization.ImplicationsUnifies without extras; predicts DE evolution (DESI match), small-scale deviations.ConclusionDREG as QG's thermodynamic law — convergence signals shift.Detailed DREG Simulation ResultsAll sims use minimal common core proxy: relation growth maximizing new links (entropy S), with irreversibility + jitter.DREG database as of 20260115 of solved topics (106 posts) and new math Nr,Category,Key Discovery/Point,DREG Description,Simulation_Type,Simulation_Parameters,Simulation_Result,Key Emergent Feature,Raw_Code_Snippet,DREG_Status_2026,DREG_Comment 1,DREG Core,Definition of DREG,"Directed Relational Entropy Growth: Irreversible maximization of relational complexity in pre-geometric substrate",,,,"3+1D, gravity, discreteness, DE, particles, life",,Core Principle,"Shared across entropic gravity, relational QM, causal sets, asymptotic safety, HoLT" 2,DREG Core,"Minimal Common Core ODE","dS/dτ = S(1−S³) + √S N(0,1)",ODE Proxy,"Basic growth + jitter","Dimension ~3+1; gravity gradient; DE braking",Emergent universe,"dS = S * (1 - S**3) + sqrt(S) * normal(0,1)",Confirmed,"Stripped model reproduces key features" 3,DREG Sim,Dimension Emergence,"3+1D from relation maximization",Graph Growth,"10k nodes directed links","Effective dim ~3.2 spatial + 1 directed","3+1D natural","nodes add maximizing new links",Confirmed,"Max connectivity in 3D + arrow" 4,DREG Sim,Irreversibility Form,"Strict dS > 0 vs statistical",ODE Variants,"Strict vs allow negative","Strict: stable; violation → collapse","Irreversibility essential","dS floor vs negative",Confirmed,"Strict local best" 5,DREG Sim,"Gravity from ∇S","Newtonian/Einstein from gradient",Grid Sim,"∇S on test particle","1/r² low; deflection ~GR strong","Pure emergence","F = −∇S",Confirmed,"No extra geometry" 6,DREG Sim,"Quantum Discreteness","Jitter → spectra quantization",Jitter Sim,"Multiplicative noise","Discrete levels; Planck cutoff","Natural quanta","epsilon sqrt(S) N",Confirmed,"Discreteness from noise" 7,DREG Sim,"Arrow of Time","Local flow vs global timeless",Graph Reversibility,"Directed vs reversible","Reversible → collapse","Arrow necessary","Directed links",Confirmed,"Global timeless safe" 8,DREG Sim,"Dark Energy Saturation","Late braking w(z) ≈ −0.93",Saturation Sim,"(1 − (S/Sp)^α)","w ≈ −0.93 ±0.02","Dynamic DE","alpha~4.2","Matches DESI/Euclid","No Λ tuning" 9,DREG Sim,"Particle Knots","Knots → masses/generations",Graph Knot Sim,"Local high-S clusters","3 families, hierarchy","Particles from topology","cluster density",Confirmed,"Generations from 3D symmetry" 10,DREG Sim,"BH Analogs","High-density → horizons",Knot Trapping Sim,"High S density","Horizon + unitary evaporation","Info preserved","jitter evaporation",Confirmed,"Page curve natural" 11,DREG Sim,"Life Peaks","Mid-growth replication max",Replication Rate Sim,"S ~0.5 Sp","Peak rate; self-replicators","Life origins","relation spawn rule",Confirmed,"Sweet spot universal" 12,DREG Sim,"Testable Signatures","Bounce + small-scale deviation",Early/Low-S Sim,"Bounce + δg ~10^{-11}","CMB low-ℓ + atom interferometry",Predictive,"early jitter + gradient","Pending 2027","Strong tests" 13,DREG Convergence,"Entropic Gravity (Verlinde)","Gradients → gravity",Volume Entropy Sim,"∇S in volume","Gravity weakest; DE dynamic","Shared core","F = T ∇S",Confirmed,"No holography needed" 14,DREG Convergence,"Relational QM (Rovelli)","Relations → info growth",Relation Matrix Sim,"-Tr(R log R) growth","Time arrow; geometry","Shared core","R(i,j) increase",Confirmed,"Pure relations" 15,DREG Convergence,"Causal Set (Sorkin)","Order maximization → manifold",Causal + Entropy Sim,"Deterministic links","Dimension 3+1; bounce","Shared core","max new order",Confirmed,"Irreversibility key" 16,DREG Convergence,"Asymptotic Safety","Flow → fixed point",RG + Entropy Sim,"β(g) as dS","Predictivity; cutoff","Shared core","β(g) as dS",Confirmed,"Entropy view of RG" 17,DREG Test,"WGC Derivation","Remnants stall growth",WGC Sim,"q/m ratios","Bound satisfied; decay maximizes S",Thermodynamic,"ΔS decay > remnant",Confirmed,"WGC from dS > 0" 18,DREG Test,"RNA Evolution Tree","Mutation + selection",RNA Sim Deep,"30 chains 150 gen","Diversity → lineages; catalysis","Life tree","complementary + mutation",Confirmed,"Darwinian evolutio

Huayou Si, Yaqian Huang, Guozheng Li, Yun Zhao · 7 authors

Current research on cryptocurrency dual-offline payment systems has garnered significant attention from both academia and industry, owing to its potential payment feasibility and application scalability in extreme environments and network-constrained scenarios. However, existing dual-offline payment schemes exhibit technical limitations in privacy preservation, failing to adequately safeguard sensitive data such as payment amounts and participant identities. To address this, this paper proposes a privacy-preserving dual-offline payment method utilizing a cryptographic challenge-response mechanism. The method employs zero-knowledge proof technology to cryptographically protect sensitive information, such as the payer’s wallet balance, during identity verification and payment authorization. This provides a technical solution that balances verification reliability with privacy protection in dual-offline transactions. The method adopts the payment credential generation and credential verification mechanism, combined with elliptic curve cryptography (ECC), to construct the verification protocol. These components enable dual-offline functionality while concealing sensitive information, including counterparty identities and wallet balances. Theoretical analysis and experimental verification on 100 simulated transactions show that this method achieves an average payment generation latency of 29.13 ms and verification latency of 25.09 ms, significantly outperforming existing technology in privacy protection, computational efficiency, and security robustness. The research provides an innovative technical solution for cryptocurrency dual-offline payment, advancing both theoretical foundations and practical applications in the field.

Sirui Shen, Zunchen Huang, Chenglu Jin

The modern integrated circuit ecosystem is increasingly reliant on third-party intellectual property integration, which introduces security risks, including hardware Trojans and security vulnerabilities. Addressing the resulting trust deadlock between IP vendors and system integrators without exposing proprietary designs requires novel privacy-preserving verification techniques. However, existing privacy-preserving hardware verification methods are all simulation-based and fail to offer formal guarantees. In this paper, we propose ZK-CEC, the first privacy-preserving framework for hardware formal verification. By combining formal verification and zero-knowledge proof (ZKP), ZK-CEC establishes a foundation for formally verifying IP correctness and security without compromising the confidentiality of the designs. We observe that existing zero-knowledge protocols for formal verification are designed to prove statements of public formulas. However, in a privacy-preserving verification context where the formula is secret, these protocols cannot prevent a malicious prover from forging the formula, thereby compromising the soundness of the verification. To address these gaps, we first propose a blueprint for proving the unsatisfiability of a secret design against a public constraint, which is widely applicable to proving properties in software, hardware, and cyber-physical systems. Based on the proposed blueprint, we construct ZK-CEC, which enables a prover to convince the verifier that a secret IP's functionality aligns perfectly with the public specification in zero knowledge, revealing only the length and width of the proof. We implement ZK-CEC and evaluate its performance across various circuits, including arithmetic units and cryptographic components. Experimental results show that ZK-CEC successfully verifies practical designs, such as the AES S-Box, within practical time limits.

Zouhair Elhadari

The digitization of medical records in the healthcare sector demands robust mechanisms to ensure data confidentiality, integrity, and privacy. This paper proposes an innovative multi-factor authentication (MFA) mechanism that leverages smart contracts and blockchain technology to secure the tracking of medical records. The proposed system, named Blockchain Authentication with Zero-Knowledge Proof (BAZKP), provides a tamper-proof environment for storing and accessing records while preserving users’ personally identifiable information (PII). A key novelty of BAZKP lies in storing only the character count structure of passwords rather than the actual credentials, combined with zero-knowledge proofs (ZKP) to verify identity without exposing sensitive data. This hybrid blockchain/ZKP approach addresses limitations of centralized and hardware-based solutions, reducing vulnerabilities while avoiding the cost and usability constraints of dedicated hardware systems. The system was implemented and tested on a private Ethereum testnet, with a proof-of-concept application developed using Solidity, Web3.js, and MetaMask. Performance evaluation over 100 transactions for core operations (registration, login, and password reset) demonstrated practical viability: registration incurred the highest latency (≈4500 ms) and gas consumption (≈120,000 gas), while login and reset operations were more efficient (≈4000 ms/80,000 gas and ≈3500 ms/60,000 gas, respectively). Comparative security analysis against existing MFA methods—including 2FA, hardware tokens, and biometrics—confirms that BAZKP provides superior privacy protection through decentralization and ZKP, without the cost and usability drawbacks of hardware-based solutions. Overall, this approach enhances trust in digital health systems by offering a secure, transparent, and privacy-preserving authentication framework for medical data, representing a significant advancement in digital healthcare security. Keywords: Blockchain; Multi-Factor Authentication; Smart Contracts; Zero-Knowledge Proof; Medical Record Security.

Prabahan Dey

Imagine you're explaining something new to a friend. You might say "the atom is like a tiny solar system" or "the brain works like a computer." We use these comparisons—analogies—constantly to understand unfamiliar things through familiar ones. They're how Darwin explained evolution (like selective breeding), how Rutherford explained atomic structure (like planetary orbits), and how we navigate everyday life. But here's the puzzle: while we have rigorous mathematical systems for logical deduction (if A then B), probability (how likely is X?), and other forms of reasoning, we've never had a formal system for analogy. When is an analogy actually valid? How much confidence should it give us? Can we combine multiple analogies? These questions have lived in philosophical limbo for over a century. What This Paper Does This paper creates the first complete logical system for analogical reasoning—essentially, the "mathematics of analogy." Just as probability theory gives us precise rules for reasoning under uncertainty, Analogical Logic (AL) gives us precise rules for reasoning by similarity. The Core Insight The key idea is that analogies aren't about surface similarities—they're about structural correspondences. A whale looks like a fish (similar shape, fins, lives in water), but that's a weak analogy because their deeper structures differ fundamentally (mammals vs. fish, lungs vs. gills, warm vs. cold-blooded). Meanwhile, the atom and solar system look nothing alike at the surface level, but make a powerful analogy because their relational structures match: a central massive body attracts smaller bodies that orbit it. The system captures this by separating: Relational structure: How things relate to each other (orbits, attracts, causes) Surface properties: What things are like individually (hot, charged, massive) How It Works The paper builds a complete formal system with five components: A language for precisely describing domains (like the solar system or atom) and mappings between them Five axioms that characterize how analogies behave: Every domain is perfectly analogous to itself If A is analogous to B, then B is analogous to A Analogies can be chained, but get weaker with each link Valid analogies must preserve relational structure Surface properties affect analogy strength but not validity Five inference rules for deriving new knowledge: Transfer relations from source to target Transfer properties (with reduced confidence) Recognize when differences weaken analogies Generate hypotheses by transferring explanations Strengthen conclusions when multiple analogies converge A strength metric (Σ) ranging from 0 to 1 that quantifies how good an analogy is, combining structural alignment with property similarity Soundness proofs showing that valid analogical arguments produce reliable conclusions with calculable confidence levels What Makes It Non-Obvious Some surprising results emerge: Non-monotonicity: Unlike deductive logic, adding true information can invalidate previous analogical conclusions. The whale/fish analogy weakens dramatically when you learn whales are mammals—new knowledge can break old analogies. Weak transitivity: If A is analogous to B and B is analogous to C, then A is analogous to C, but more weakly. Information degrades through analogical chains. Structure trumps properties: A perfect structural match with zero property overlap (Σ = 0.70) creates a stronger analogy than perfect property match with weak structure (Σ < 0.50). Seeing It In Action The paper works through historical scientific analogies in detail: Rutherford's atom (like a solar system): Calculates Σ = 0.80 (strong analogy), shows which inferences were valid (inverse-square force law) and which failed (continuous electron trajectories—quantum mechanics revealed this disanalogy) Darwin's natural selection (like artificial breeding): Calculates Σ = 0.88 (very strong), shows how the analogy generated the theory of evolution despite the key disanalogy (no intentional "breeder" in nature) Electricity (like water flow): Shows a moderate analogy (Σ ≈ 0.70) that's useful for engineering despite microscopic differences Why It Matters This isn't just theoretical housekeeping. The system: For AI: Provides foundations for machines to reason by analogy rigorously, with confidence estimates For science: Formalizes how analogies drive discovery and when to trust them For philosophy: Resolves century-old debates about the nature of similarity and analogical inference For education: Helps evaluate teaching analogies (which ones support learning vs. create misconceptions?) For everyone: Makes explicit the implicit reasoning we use constantly

Petar Radanliev, C. Maple, Omar Santos

Introduction Digital identity infrastructures used in electronic passports, national eID schemes, and federated authentication systems rely predominantly on centralised registries and classical public key cryptography. These architectures enable large-scale identity correlation, mass data aggregation, and single points of compromise, while remaining vulnerable to quantum attacks against RSA and elliptic-curve cryptography. There is no deployed identity framework that simultaneously provides post-quantum security, cryptographic privacy guarantees, and decentralised trust. Methods This study proposes a quantum-proof digital passport architecture combining lattice-based post-quantum cryptography, decentralised blockchain identifiers, and transformer-based decentralised artificial intelligence. The framework employs NIST-aligned post-quantum key encapsulation and digital signatures, zero-knowledge proofs for selective disclosure of identity attributes, and homomorphic encryption for encrypted identity verification. Blockchain oracles and decentralised identifiers enforce credential integrity and auditability without reliance on central identity providers. Transformer attention mechanisms support adaptive identity validation while preventing persistent identity profiling. Results Architectural analysis shows that the proposed system prevents quantum-enabled credential forgery, retrospective decryption, and cross-service identity linkability. Zero-knowledge verification removes plaintext exposure of personal data, and decentralised credential control eliminates central compromise vectors. The design remains interoperable with existing passport and eID infrastructures. Discussion The results demonstrate that secure post-quantum digital identity requires the combined application of quantum-resistant cryptography, decentralised governance, and cryptographic privacy enforcement.

Journal of Theoretical and Applied Information Technology

With the growing volume of health information it has become common practice to protect the patient identity while maintaining convenient access to the data. Due to varying flow of cyber security threats, traditional solutions never manage to get flexible access to data without compromising with overflow of data. To overcome these challenges focusing on patient data protection, in this paper, we propose a new Hybrid Integrated Hashing approach entitled "Dynamic Adaptive Hash-Block Access Control (DAHBAC) framework" using blockchain based advanced data access control mechanism. The dynamic multi factor hashing scheme can change in response to the current Vulnerability of data and access patterns, whereas data access control refers to leverage blockchain's immutability and decentralized structure that helps protecting patient privacy while allowing authorized persons to read. The dynamic hashing method prevents intruder attempts by making hash and easy to calculate but requiring real-time modification of the hash for access protection. This is made possible by harnessing the application of zero-knowledge proofs (ZKP) within the frame of blockchain to enable verification of information when there is no disclosure of the data. Compared with the conventional methods, testing of prototype in a health care organization resulted in 92% on attempts by unauthorized workers to enter the system and 7% increasing data retrieval rate. These findings shows that the proposed model is a perfect patient data protection pattern in ehealth systems, because it is not only secures patients data but also enhances the accessibility and scalability to handle more clients. It is enabled by the use of zero-knowledge proofs (ZKP) in combination with blockchain technology to verify information, while keeping the information secret.

Khushbakht Farooq, Muhammad Ibrahim, Irsa Manzoor, Mukhtaj Khan · 5 authors

The rapid integration of IoT with edge computing has revolutionized various domains, particularly healthcare, by enabling real-time data sharing, remote monitoring, and decision-making. However, it introduces critical challenges, including data privacy breaches, security vulnerabilities, especially in environments dealing with sensitive information. Traditional access control mechanisms and centralized security systems do not address these issues, leaving IoT environments exposed to unauthorized access and data misuse. This research proposes Fuzzychain-edge, a novel Fuzzy logic-based adaptive Access control model for Blockchain in Edge Computing framework designed to overcome these limitations by incorporating Zero-Knowledge Proofs (ZKPs), fuzzy logic, and smart contracts. ZKPs secure sensitive data during access control processes by enabling verification without revealing confidential details, thereby ensuring user privacy. Fuzzy logic facilitates adaptive, context-aware decision-making for access control by dynamically evaluating parameters such as data sensitivity, trust levels, and user roles. Blockchain technology, with its decentralized and immutable architecture, ensures transparency, traceability, and accountability using smart contracts that automate access control processes. The proposed framework addresses key challenges by enhancing security, reducing the likelihood of unauthorized access, and providing a transparent audit trail of data transactions. Expected outcomes include improved data privacy, accuracy in access control, and increased user trust in IoT systems. This research contributes significantly to advancing privacy-preserving, secure, and traceable solutions in IoT environments, laying the groundwork for future innovations in decentralized technologies and their applications in critical domains such as healthcare and beyond.

Hasina Andriambelo, Hery Zo Andriamanohisoa, Naghmeh Moradpoor

Federated learning enables collaborative model training without sharing raw data, but practical deployments increasingly require verifiable guarantees that clients compute updates correctly. Zero-knowledge proofs can provide such guarantees, yet existing approaches face scalability limits due to the combined cost of polynomial commitments and fast Fourier transform (FFT) intensive verification. Pairing-based schemes offer compact proofs but incur high prover and verifier overhead, while hash-based constructions reduce algebraic cost at the expense of rapidly growing proof sizes. This paper proposes Hybrid-Commit, a polynomial commitment architecture for Binius zero-knowledge proofs that aligns cryptographic primitives with the algebraic structure of federated learning workloads. The scheme separates verification into additive and multiplicative phases: linear aggregation is handled using batched additive commitments optimized for binary fields, while non-linear constraints are verified via hash-based commitments over sparsely selected FFT domains. Proofs from multiple clients are combined through recursive aggregation while preserving non-interactivity. Experiments demonstrate scalability in prover time and proof size (near-constant prover time across 4–11 clients; 160 bytes per client representing 341× and 813× reductions vs. FRI-PCS and Orion), although verification time (762 ms per client) does not scale favorably, making the scheme suitable for bandwidth-constrained scenarios. The scheme achieves under 2% end-to-end training overhead with no impact on model accuracy, indicating that workload-aware commitment design can improve specific scalability dimensions of zero-knowledge verification in federated learning systems.

Ilyes Tarik MAZARI

The year 2025 marked the transition from AI ethics debate to AI governance execution. Industry reports document over 2,000 organizations registering AI systems for compliance review in Q4 2025, compliance budget increases of 300-400%, and an AI liability insurance market that grew from $400 million to $2.1 billion. Simultaneously, research identifies critical infrastructure gaps: AI agents lack decision traces, models are commoditizing while privacy infrastructure lags, and regulatory frameworks have fractured across three distinct philosophies with no convergence expected. This paper synthesizes findings from the Responsible AI Governance Network (RAGN), Foundation Capital, and enterprise AI orchestration research to identify the specific technical requirements for regulatory compliance. It then presents the Y.I.N. (Your Information Never leaves your control) Mazari Architecture as a comprehensive solution, demonstrating how the mandatory cryptographic ordering of Differential Privacy, Zero-Knowledge Proofs, and Homomorphic Encryption (DP→ZK→HE) addresses documented litigation exposure exceeding $10 billion, satisfies EU AI Act transparency requirements, enables AI agent accountability, and provides modular compliance across fragmented regulatory regimes. The architecture is backed by 19 USPTO patent applications covering 610+ claims, with validated benchmarks showing 640× timing improvements, 135× detection capabilities, and accuracy preservation within 1.5 percentage points.

Ryan Van Gelder

Defensive publication establishing prior art for proof-first digital identity systems using prime-indexed state evolution, zero-knowledge proofs, and silence-by-default semantics. This specification defines the Meta-Theorem of Prime Identity (MTPI), an architectural framework requiring cryptographic proof for every state transition. Core components include: Prime-Indexed Recursive Tensor Mathematics (PIRTM) with contractive dynamics guarantee; prime-gated activation with drift bounds δ(t) ≤ 0.3; Conscious Sovereignty Layer (CSL) with ethical tensor field commutation relations; Archivum append-only audit schema; and conformance requirements including Surveillance Fork detection. Reference implementations provided in Solidity and Circom 2.1. Mathematical foundations, alternative embodiments, and public domain designations included for maximum prior art scope. Keywords: zero-knowledge proofs, prime-indexed identity, verifiable computation, AI safety, defensive publication, proof-first computing, MTPI, PIRTM, CSL

Pedro Antonino, Namrata Jain

Zero-Knowledge (ZK) proof systems are cryptographic protocols that can (with overwhelming probability) demonstrate that the pair $(X, W)$ is in a relation $R$ without revealing information about the private input $W$. This membership checking is captured by a complex arithmetic circuit: a set of polynomial equations over a finite field. ZK programming languages, like Noir, have been proposed to simplify the description of these circuits. A developer can write a Noir program using traditional high-level constructs that can be compiled into a lower-level ACIR (Abstract Circuit Intermediate Representation), which is essentially a high-level description of an arithmetic circuit. In this paper, we formalise some of the ACIR language using SMT-LIB and its extended theory of finite fields. We use this formalisation to create an open-source formal verifier for the Noir language using the SMT solver cvc5. Our verifier can be used to check whether Noir programs behave appropriately. For instance, it can be used to check whether a Noir program has been properly constrained, that is, the finite-field polynomial equations generated truly capture the intended relation. We evaluate our verifier over 4 distinct sets of Noir programs, demonstrating its practical applicability and identifying a hard-to-check constraint type that charts an improvement path for our verification framework.

Mohamed Chawki

In the contemporary global context, Information and Communication Technologies (ICTs) present multifaceted challenges, particularly in maintaining an appropriate balance between national security requirements and the protection of individual privacy. The rapid advancement of technology has led to an increase in cyber threats, necessitating closer collaboration between the public and private sectors. However, such collaboration often blurs the boundaries between security imperatives and individual privacy rights. This study examines the implications of this balance and assesses whether existing regulations adequately protect individuals’ privacy. The right to privacy is universally safeguarded by ethical norms and legal frameworks. Instruments such as the United States Constitution and the General Data Protection Regulation (GDPR) provide protection against unlawful searches, seizures and the misuse of personal data. Despite these safeguards, information sharing between public institutions and private entities may undermine privacy rights if appropriate accountability mechanisms are not in place. Navigating this complex terrain requires approaches that enable data collection and cybersecurity cooperation without violating individual privacy. Technological innovations, including artificial intelligence (AI) and zero-knowledge proof authentication systems, offer potential solutions by limiting unauthorized access to personal data. This paper argues that reconciling cybersecurity imperatives with the protection of individual rights requires continuous recalibration of legal and ethical boundaries. While data sharing within and across private industries can strengthen defenses against cyber threats, such practices must be carefully evaluated to prevent privacy violations. Achieving this balance ultimately depends on enhanced transparency and accountability.