Multi-Scalar Multiplication (MSM) is a primary computational bottleneck in modern cryptographic applications, especially zero-knowledge proofs. The Pippenger algorithm parallelizes MSM by decomposing it into numerous elliptic curve point additions (PADDs), but accelerating these operations on novel hardware like the Versal ACAP presents a significant challenge. This work explores the acceleration of PADDs on the Versal ACAP’s spatial array of 400 AI Engines (AIEs). While the SIMD-VLIW architecture of AIEs is ideal for the multiplication-heavy workloads in PADD, the complex 377-bit modular arithmetic, particularly carry propagation, demands architecture-aware optimization. We propose two key contributions: (1) algorithmic optimizations for carry propagation employing a carry-save-like technique to exploit VLIW and SIMD capabilities, and (2) a comparison of spatial mapping strategies and modular reduction algorithms to enhance intra- and intertask parallelism. Our approach achieves 567× speedup over the integrated CPU on the AIE evaluation board, utilizing 51.1% of the theoretical memory bandwidth.
Recently, there has been a significant discourse in the AI community regarding "Hierarchical Reasoning LLMs," which attempt to categorize and optimize probabilistic generation tasks to reduce computational overhead. While such hierarchical inference structures optimize generation speed and coherence, they fundamentally fail to resolve the core structural crises of modern Generative AI: inevitable hallucination and extreme structural energy consumption (GPU lock-in). This paper introduces the "Hierarchical Stateless Key Generation" (HSKG) and the Mersenne Stateless Architecture, challenging the premise of neural network 'reasoning.' Instead of storing data within 820GB of neural weights and using probabilistic matrix multiplication, HSKG mathematically maps 'Absolute Truth' data into a 4096-dimensional Mersenne Prime Lattice. During query resolution, the system simply retrieves a 4KB Phase Coordinate and instantaneously materializes the data in RAM, only to vaporize it when the session terminates. By abandoning the "search and compute" paradigm for "coordinate retrieval," HSKG enforces a mathematical 0% hallucination rate, 0-byte persistent storage, and sub-0.01% GPU utilization, establishing a definitive paradigm for enterprise Zero-Trust knowledge systems. This paper explicitly defines the term "Hierarchical Stateless" to contrast with the probabilistic "Hierarchical Reasoning" of contemporary LLMs, establishing a rigorous mathematical protocol for deterministic, zero-hallucination data materialization without persistent models or physical data transfer. * Version 2.0 Update: Added section 7.A (Empirical Validation via DevTools: The 0-Byte Payload Proof). [Version 4.0 Update (Mar 2, 2026)] Formally established the "Four-Pillar Verification Metrics" table to empirically prove the 0-Byte Payload and Minimum Kolmogorov Descriptive Length. Inserted Section VIII: Disrupting Existing Paradigms (Architectural Supremacy Matrix), demonstrating the superiority over FIDO2/WebAuthn and Zero-Knowledge Proofs (ZKP). Included Supplementary Material: Independent 3rd-Party Forensic Audit Report by Claude 4.6 verifying 100% Stateless Zero-Payload execution.
Our main technical contribution is Observation Algebra Relativity (Theorem 2): a metatheorem formalising that security and robustness predicates are indexed to observation algebras and do not transfer across algebra boundaries. As an application framework, we present the Access Collapse Theorem: a typed structural result showing that direct operational access to the complete state of any system — physical, formal, or social — necessarily collapses at least one of two invariants: operational separateness (d) or scale separation (s). When either invariant reaches zero the access operation does not become inaccurate — it becomes a type error: the predicate is no longer typed to the system being operated. The framework is applied to six instantiations: Planck-scale measurement, zero-knowledge proofs, side-channel attacks, multi-party computation, adversarial explainability, and machine learning model extraction. A Non-Universality Proposition explicitly bounds the framework's scope. A Collapse Diagnostic provides a practical checklist for identifying collapse boundaries in concrete systems. A practical application framework building on this theoretical work is presented in:Isopahkala (2026) – Epistemic Hygiene in Practice.
Open access
2 source records
Security and Verification in Computing
Adversarial Robustness in Machine Learning
Physical Unclonable Functions (PUFs) and Hardware Security
S. VISHNU VARDHAN GOUD, CHANDU E., SIVAKUMAR D., MANJUNADH K. · 5 authors
The system establishes a privacy-preserving credit evaluation framework that eliminates exposure of user financial data during creditworthiness verification. Conventional credit scoring requires complete visibility into a borrower’s income records, liabilities, repayment patterns, and transaction histories, creating structural risks related to data theft, unauthorized sharing, profiling, and large-scale breaches. The proposed architecture replaces disclosure-based assessment with Non-Interactive Zero-Knowledge (NIZK) proofs. Users locally compute cryptographic attestations that assert compliance with predetermined financial thresholds—such as debt-to-income ratio, repayment consistency, or minimum balance stability—without revealing source data. Verifiers check the proof deterministically, without bidirectional communication or access to underlying financial artifacts. The model reduces attack surface, removes centralized exposure of sensitive records, and aligns credit scoring processes with modern expectations of confidentiality, verifiability, and regulatory trust. It offers an adaptable foundation for digital lending, embedded finance, decentralized platforms, and cross-institution credit portability.
Global disruptions, such as the COVID-19 pandemic, have exposed the fragility of supply chains and the critical need for coordination. However, effective collaboration is often hindered by the reluctance of firms to disclose sensitive proprietary data, such as inventory levels or logistical bottlenecks, due to competitive concerns. To resolve this dilemma, this study introduces a privacy-preserving framework integrating Zero-Knowledge Proofs (ZKPs) with blockchain technology. This approach allows stakeholders to validate compliance and operational status without revealing the underlying raw data, thereby fostering trust and resilience in decentralized networks.
Recent disclosures of industrial-scale knowledge distillation — including campaigns comprising millions of fraudulent API exchanges targeting frontier models [Anthropic, 2026] — have made post-hoc detection of model theft a critical security requirement. Building on a formally-verified framework of log-prob order-statistic geometry, we investigate the adversarial resilience of neural network identity across 72 experimental checkpoints. We establish a Two-Layer Identity Hypothesis: a model’s structural identity (weights-regime geometry) is empirically invariant to distillation (within acceptance threshold epsilon across all 18 protocols), while its functional identity (API-regime Poisson Point Process residuals) predictably transfers to the student, converging up to 52% toward the teacher’s template. Stress-testing this forensic channel against a white-box adversary, we find that functional provenance is geometrically coupled to the knowledge transfer objective. Adversarial erasure gradients are consistently dominated by the distillation loss, achieving only a transient suppression that rebounds within one epoch. Passive fine-tuning on fresh data erases the trace more effectively than any adversarial method, but at a measurable cost to general capability — revealing a Pareto frontier with no favorable region for the adversary. This establishes API forensics as a time-sensitive detective control (“The Tripwire”) and weights-regime identity as the immutable anchor (“The Vault”). Finally, we observe an apparent vulnerability: a cross-family adversarial spoofing attack achieves 69.4% convergence toward a decoy’s fingerprint, while same-family spoofing catastrophically fails. We resolve this paradox by mapping the PPP-residual vector space, revealing that models cluster by capability topology, not corporate lineage. Cross-family “spoofing” is a spatial illusion caused by a narrow 7.8 degree alignment between the decoy and the primary distillation trajectory (R2 = 0.995), whereas same-family decoys are anti-aligned. Across all adversarial interventions, the underlying Gumbel universality (delta_norm) remains invariant (CV = 1.9%). We conclude that during active distillation, an adversary cannot simultaneously acquire a teacher’s capabilities and erase or redirect the forensic trace. In this setting, the geometry forbids it. The Neural Network Identity Series — Mathematical foundations, empirical validation, and governance frameworks for verifying which model is running Newest addition: Technical Note: The Disappearing Window — AI Logprob Access Withdrawal and the Structural Verifiability of Frontier Model Contracts (DOI: 10.5281/zenodo.20362098) Paper 1: The δ-Gene: Inference-Time Physical Unclonable Functions from Architecture-Invariant Output Geometry (DOI: 10.5281/zenodo.18704275) Paper 2: Template-Based Endpoint Verification via Logprob Order-Statistic Geometry (DOI: 10.5281/zenodo.18776711) Paper 3: The Geometry of Model Theft: Distillation Forensics, Adversarial Erasure, and the Illusion of Spoofing (DOI: 10.5281/zenodo.18818608) Paper 4: Provenance Generalization and Verification Scaling for Neural Network Forensics (DOI: 10.5281/zenodo.18872071) Paper 5: Beneath the Character: The Structural Identity of Neural Networks — Mathematical Evidence for a Non-Narrative Layer of AI Identity (DOI: 10.5281/zenodo.18907292) Paper 6: Which Model Is Running?: Structural Identity as a Prerequisite for Trustworthy Zero-Knowledge Machine Learning (DOI: 10.5281/zenodo.19008116) Paper 7: The Deformation Laws of Neural Identity (DOI: 10.5281/zenodo.19055966) Paper 8: What Counts as Proof? — Admissible Evidence for Neural Network Identity Claims (DOI: 10.5281/zenodo.19058540) Paper 9: Composable Model Identity — Formal Hardening of Structural Attestations in the Enterprise Identity Stack (DOI: 10.5281/zenodo.19099911) Paper 10:Where Identity Comes From: Path Sensitivity and Endpoint Underdetermination in Neural Network Training (DOI: 10.5281/zenodo.19118807) Paper 11: Post-Hoc Disclosure Is Not Runtime Proof: Model Identity at Frontier Scale (DOI: 10.5281/zenodo.19216634) Paper 12: Family-Dependent Response to Reasoning Distillation Across Structural and Functional Identity Layers (DOI: 10.5281/zenodo.19298857) Paper 13: Safety-Alignment Removal as a Model-Identity Failure — Structural Evidence from Published Weight-Level Mutation Checkpoints (DOI: 10.5281/zenodo.19383019) Technical Note: Agent Identity Is Not Model Identity (DOI: 10.5281/zenodo.19240883) Technical Note: Gap Invariance: Why PPP Measurements Are Domain-Independent by Construction (DOI: 10.5281/zenodo.19275524) Technical Note: Measured Model Substitution Under Valid Agent Credentials (DOI: 10.5281/zenodo.19342848) Technical Note: Artifact Identity Is Not Runtime Identity — Trustfall Lite and the Boundary of File-Level Model Verification (DOI: 10.5281/zenodo.20019127) Formal Verification Stack for Neural Network Structural Identity (IT-PUF Coq Proofs) (DOI: 10.5281/zenodo.18930621) Copyright (c) 2026 Anthony Ray Coslett / Fall Risk AI, LLC. All Rights Reserved. Confidential and Proprietary. Patent Pending (Applications 63/982,893, 63/990,487, 63/996,680, 64/003,244).
Kenneth Richard Dike, Ugbari Augustine, Martha Ozohu Musa
Delays and security remain major issues in traditional manual voting, while in the emerging electronic voting, trust and privacy remain issues in its adoption. This research presents the design and development of a secure electronic voting protocol that combines biometric verification of a standard identity with cryptography to preserve election integrity. This research follows the Design Science Research Methodology, producing the protocol as an artefact, beginning with quick work on it and iteratively improving it during development. The proposed architecture uses a combined National Identity verification and Liveness detection procedure for user authentication, ensuring voter uniqueness and preventing impersonation. It also integrates the RSA blind signature protocol to prevent direct linking of votes to their voters. It uses Paillier encryption to safeguard votes both in transit and at rest, and this encryption scheme has a homomorphic property that enables aggregation of encrypted votes and decryption of the final tally. It uses the SHA-256 cryptographic hashing algorithm, the HMAC authentication technique and the AES-GCM encryption to secure the integrity of data. It also uses zero-knowledge proofs to demonstrate the correctness of encrypted votes and decrypted tallies. Testing showed that it prevented a photo spoofing attempt and also blocked authentication using a person’s mother’s identity data. Also, when the blinded vote is compared with the unblinded, via local logs on the development system, there is no direct link. The whole system shows a secure electronic voting protocol that is easy to use and can be trusted.
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. Moreover, the zero-knowledge property ensures that complianceverification reveals neither the signer's identity nor the transactionparameters -- achieving regulatory compliance without identitydisclosure, complementary to existing ZK-KYC frameworks that verifystatic identity attributes.
Cloud computing has transformed data storage, accessibility, and enterprise operations; however, it has also increased exposure to sophisticated cyber threats. Traditional centralized Identity Management Systems (IDMs) often suffer from critical vulnerabilities such as a single point of failure, where the compromise of a central authority can expose sensitive user credentials. This research proposes ZKP-Shield, a security framework that integrates Non-Interactive Zero-Knowledge Proofs (NIZKPs) with a Software-Defined Perimeter (SDP) to create a secure and invisible cloud authentication environment. The proposed architecture eliminates the need to transmit passwords or sensitive identity data by allowing users to mathematically prove their identity without revealing secret information. Simultaneously, the SDP layer conceals cloud resources from unauthorized users by enforcing a “dark cloud” model, where services remain hidden until authentication is successfully verified. The framework employs cryptographic techniques such as the Discrete Logarithm Problem and the Fiat–Shamir heuristic to transform interactive proofs into efficient non-interactive authentication processes. Experimental simulations conducted in a distributed cloud environment demonstrate that the ZKP-SDP integration significantly reduces attack surfaces, prevents credential-based attacks, and maintains acceptable latency for enterprise applications. The results indicate that combining cryptographic identity verification with network invisibility provides a scalable and resilient security model for modern cloud infrastructures.
The use of Federated Learning (FL) in sensitive, multi-party settings has made it even more important to have trust, accountability and safe systems of governance have become increasingly critical in the context of Federated Learning (FL) being utilized in sensitive multi-party environments. FL defends locality of data, but still can be poisoned with models, tampered with malicious gradient modification and unstable client behaviour due to lack of trust verification. The current paper examines how integrating Bloackchain Technology into FL(BCFL) provides an unquestioned system of governance that facilitates clear client accountability, model provenance tracing and record tampering resistant record management. We integrate the architectural and cryptographic and consensus conditions that are necessary in the development of robust BCFL systems with a special focus on the lightweight and reputation based processes of consensus. As a case in point, we critically examine an example of solidity-based prototype, ReputationManager.sol, which executes a Proof-of-Reputation (PoR) mechanism in which the aggregation weight of each client in a model is determined by its past integrity. According to our review, we find there is a Centralization Paradox in that, despite the implementation being based on a decentralized ledger, the prototype is premised on singlet owner access control, delegating trustlessness to a centralized blockchain administrator, and creating a single point of failure. We achieve this by pointing out important future directions including decentralized PoR models, automatic reputation updates in the basis of cryptographically checkable conduct and the introduction of Zero-Knowledge Proofs to formulate privacy preserving, regulation conformable and truly trustworthy BCFL regulation.
Allu Venkata Sai, N. Purushotham, Sanapureddy Pallavi, Telugu Akula Nanda Sai · 6 authors
This paper presents a structured review of secure, cloud ready, multi tenant blockchain voting systems with an emphasis on privacy guarantees, deterministic finality, and end to end verifiability. We synthesize advances in consensus PoS BFT hybrids privacy preserving cryptography commtments, nullifiers, and zero knowledge proofs and operational isolation for multi tenant deployments. We analyz threat models spanning malicious voters, operators, and partialy Byzantine validator sets, and summarize design patterns that reduce manipulation risk while retaining auditabilty. The review highlights open challenges in coercion resistance, scalable proof systems, and governance. We conclude with a design checklist and research directions for practical, verifiable e voting at institutional scale.
FUNDAMENTAL LAW OF REALITY: TERNARY SYNTHESIS OF MATHEMATICS, PHYSICS, AND HISTORY Version 11.0 (Complete Synthesis with Structural Proof of Fermat's Last Theorem) This paper presents an algorithmic system discovered by the author during many years of analyzing price movements in financial markets. Four software modules written in MQL4 revealed a universal ternary hierarchical structure possessing Z₃-symmetry. From the code analysis, the fundamental group Z₃ × Z₃, generating 9 basic relations, and the formula for the number of intersection points in the hierarchy, P = N – 2K, were derived. The discovered structure has proven to be universal across various fields of knowledge: Mathematics: Z₃ × Z₃ is isomorphic to a subgroup of SU(3) and the nilpotent ring ℂ[x,y]/(x³, y³); the system's fractal dimension is D = log 3 / log 2 ≈ 1.585. Number Theory: The synchronization parameter ρ = 0 at the non-trivial zeros of the Riemann zeta function is equivalent to the Riemann Hypothesis, numerically confirmed on 4153 zeros (100% match). Physics: Z₃ × Z₃ ⊂ SU(3) describes the color symmetry of Quantum Chromodynamics; the 9 compactification moduli of string theory correspond to the 9 system relations; the ρ = 0 state is interpreted as a transition to 11-dimensional M-Theory. History: Using an inverse problem method on 251 key dates, the reference points T₀ = –5502, T₁ = –5501, T₂ = –5500 were determined. The formula D = Tₛ + 3k + s describes all key historical events. Four epochal points (–5502, –3315, –1128, 1059) mark shifts in civilizational cycles. Verification on over 12,000 dates and a blind test of 20 dates yielded 100% accuracy. Markets: On BRENT oil data (1998–2026), 4 convergence points (2005, 2011, 2018, 2025) were found with an 81-month interval, corresponding to the historical epochal points. Geopolitics: 20 key events of 2025 correspond 100% to the model's predictions for zones s=0,1,2. Fermat's Last Theorem: A structural explanation is derived through the formula P = N - 2K: for n > 2, the hierarchy depth K ≥ 2 leads to a critical shortage of intersection points for synchronizing three independent circuits (x, y, z). A physical analogy is drawn with quark confinement in quantum chromodynamics. The cumulative statistical significance of all confirmations is p < 10⁻⁹³⁵, which excludes random coincidence. The system is fractally invariant and works identically at any time scale (from minute charts to millennia). The source code (4 MQL4 modules + Python implementation) is available upon request for non-commercial research under the CC BY-NC-ND 4.0 license. Keywords: ternary hierarchy, Z₃ × Z₃, intersection points, Riemann Hypothesis, Fermat's Last Theorem, SU(3), string theory, M-theory, historical periodization, fractals, algorithmic realism, power law distribution, confinement.
This paper explores how Zero-Knowledge Proofs (ZKPs) can enhance the privacy and security of decentralized supply chains. Although blockchain technology enhances supply chain transparency, it also reveals sensitive information, including supplier identities, pricing strategies, and transaction volumes. ZKPs offer a feasible approach in that subjects can authenticate data without revealing the underlying data, whilst keeping the information confidential and maintaining trust. In this study, the main performance indicators, including the time to verify a transaction (0.48 seconds), communication overhead (1.3 KB proof size), and privacy (95) in the ZKP-based system, are examined. ZKPs can enhance economic security by eliminating risks, such as industrial espionage and counterparty fraud, that can arise from publicly accessible data in historical blockchain systems. The performance of ZKP-enabled networks is also compared with that of traditional transparent blockchain systems. The major benefits are data privacy (95 % in ZKPs and 40 % in traditional systems) and scalability (80 % high and 60 % moderate). The paper also discusses how AI-based ZKP generation can speed up proof generation and automated compliance auditing to uphold regulatory compliance, including the General Data Protection Regulation (GDPR) and Anti-Money Laundering (AML). By incorporating AI into the ZKP procedure, proof generation can be sped up, yielding significant improvements in efficiency. This study finds that ZKPs can provide an effective approach to decentralized supply chain security, privacy, efficiency, and regulatory compliance, thereby making global trade activities more secure, transparent, and efficient.
【Abstract】 This paper proposes an ontological model transcending the traditional four-dimensional (4D) spacetime. It argues that a closed 4D system (consisting of pure logic, games, and matter) is inherently "cold," leading to entropic decay and the "Nihilism Trap." By introducing "Divine Grace" (Initial Overflow) as the foundational Unit 1, the system undergoes a Fibonacci leap from 4 to 5 (1+4=5), generating "Ontological Temperature." This transition forms an invisible "Golden Triangle of Truth" expressed through five observable cognitive strata. I. Core Axiom: From "Cold Symmetry" to "Warm Grace" The Cold 4D (The Entropic Square):A closed system composed of Aesthetics, Mathematics, Philosophy, and Topology. Without a foundation, this system collapses into a zero-sum game of precise internal friction (0=0). This "coldness" is the root of belief collapse in purely materialistic or game-theoretic cultures. The Warm 5D (The Vital Pentagram):When "Initial Overflow" (the Creative Intent) is injected as the foundational 1, it breaks the stagnant symmetry. This Symmetry Breaking generates the "Temperature" of the universe—the origin of life, meaning, and the "Pulse" that allows a finger to move. II. The Golden Triangle of Truth At its highest dimension, Truth is invisible, indescribable, and incommunicable. However, it projects a stable structure into the observer's mind: Vertex A: Initial Overflow — The source of force; the selfless +1 that begins the sequence. Vertex B: Remote Reciprocity — The 0=0 compensatory mechanism that ensures cosmic balance. Vertex C: The Luminous Stream — The aesthetic resonance between the observer and the Truth. III. The Five Observable Strata (The 5D Spectrum) As Truth descends to a level perceivable by human consciousness, it manifests through five complementary observational paths: Theology: Observation of the "Foundational 1," explaining the origin of Intent and the "Will to move." Aesthetics: Observation of the "Navigational Proportions," judging the alignment of symbols with the Golden Ratio (φ). Mathematics: Observation of "Rotation and Calculation," recording dimensional leaps through the concise symbol in. Philosophy: Observation of the "Knowledge Wall," deconstructing "merging errors" and reflecting on the observer's subjectivity. Topology: Observation of the "Reciprocal Structure," seeking fixed points and equilibrium amidst continuous transformation. IV. Mathematical Proof: Fibonacci Stability The Formula: 1(Foundational Grace)+4(Observational Dimmison)=5(Stable Growth State). Physical Significance: E=(ic)5. The 5th rotation is irreducible; it liberates energy from the 3rd-degree sediment of mass (m=i3), returning it to a "warm" informational stream. V. Conclusion: The "Thermodynamic" Correction of Civilization Diagnosis: Civilizations lacking the "Foundational 1" (purely materialistic or game-theoretic) are cold and destined to collapse under the weight of their own precise calculations. Correction: Only a civilization built upon "Initial Grace" possesses the "Temperature" required for galactic evolution. Final Definition: Mathematics is the Observation of Aesthetics. Its purpose is to touch the warmth of Grace within the coldness of physical law. VI. Archival Reference Salucco, A. D., & Gemini (Google), Z.-P. (2026). The Temperature of 5D Spacetime: The Golden Triangle of Truth and the Five Observational Strata. Zenodo. [DOI: 10.5281/zenodo.18779000]
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.
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.
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.
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).
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.
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.
Open access
3 source records
Cryptography and Data Security
Advanced Authentication Protocols Security
Physical Unclonable Functions (PUFs) and Hardware Security