Blockchain Papers

Seung Kwon Lee, Seok Bin Son, Joongheon Kim, Hoh Peter In

Quantum machine learning (QML) has attracted growing interest for their ability to achieve superior performance with significantly fewer parameters. However, the high cost and scarcity of current hardware push inference to cloud-hosted quantum devices, creating a tension between verifiability and confidentiality. This work proposes a novel framework that converts quantum neural network operations into classical arithmetic circuits that faithfully approximate genuine quantum computations. By encrypting these circuits with zero-knowledge proofs, it ensures computational validity while concealing internal parameters. Experimental results show that our classical circuits achieve fidelity above 0.9996 and total variation distance below 1% compared to actual quantum computations, verifying the practicality of trustworthy and privacy-preserving quantum inference.

Anthony Coslett

Neural networks deployed behind APIs or in cloud infrastructure are often verifiable only as black boxes. zkML systems have made substantial progress on computational integrity: proving that a committed model produced a claimed output honestly. But those proofs begin from a weight commitment, and a weight commitment is not a model identity. A prover can commit to arbitrary weights, execute them honestly, and still prove the computation correctly. We present an identity-first verification framework for the missing layer beneath computational integrity. The framework composes four levels. Two are inherited: structurally attestable model fingerprints via the IT-PUF protocol, formally verified in Coq and validated across 23 models with zero false acceptances, and hardware-attested binding from fingerprinted identity to model weights through a trusted execution environment. Two are new: a hybrid verifier-checkable computation path through a complete Transformer decoder layer, combining zero-knowledge circuit proofs with deterministic verifier-side checks under incrementally verifiable computation, and output binding from the verified computation to an observable token logit. On a tested micro-model, a one-step recurrence experiment found costs consistent with linear layer scaling: the dominant sub-computation of a second decoder layer matched the first in constraint count and proof size, and layer-boundary normalization acted as a measured scale reset. An accidental rescaling error then compressed the fingerprint observable to roughly 1.5 bits of dynamic range, yet the structural fingerprint retained 0.98 rank correlation with its reference. This suggests that the identity observable may depend more on relational geometry than on activation magnitude. Existing zkML systems address the computation question. This work advances the missing identity layer beneath it. Throughout the paper, formally proved results, empirical validation, and single measured observations are distinguished as [PROVEN], [VALIDATED], and [MEASURED] respectively. 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).

Mingjun Liu, Huiying Li, Ali Muqtadir, Rubab Osama · 5 authors

As Decentralized Finance (DeFi) and Non-Fungible Tokens (NFTs) expand, self-custody wallets have become the primary interface for user sovereignty. However, existing solutions suffer from critical limitations, including static authentication frameworks that compromise usability, a lack of real-time risk awareness, and inadequate key recovery mechanisms that often lead to permanent asset loss or reliance on centralized custodians. Furthermore, current wallets frequently expose transaction metadata, undermining user privacy. To address these systemic flaws, we present a modular self-custody wallet that incorporates a context-aware risk engine for real-time transaction scoring, risk-based adaptive authentication, and a dual-path decentralized key-recovery layer combining DAO-governed Shamir secret sharing with a zk-SNARK-verified fallback. The architecture further includes programmable policy enforcement and a zero-knowledge swap layer with stealth addressing to decouple front-end activity from on-chain data. The design integrates smart contracts on EVM chains and Solana through provider adapters and executes on-device ML inference to minimize latency. Experimental results demonstrate that the proposed system reduces privacy leakage probability to 5% (compared to 85% in standard architectures) and accelerates key recovery from over 24 h to approximately 8 seconds using zk-SNARKs, all while achieving 93.6% risk classification accuracy. The proposed CAPPR-Wallet advances self-custody by combining context adaptivity, privacy, and recoverability without centralized trust.

Noah Erlwein

We introduce inference receipts—lightweight cryptographic commitment records generated during generative AI inference that bind model identity, sampling configuration, and output tokens into a tamper-evident artifact. Unlike zero-knowledge proof systems or trusted execution environments, inference receipts operate under an honest-emitter trust model analogous to Certificate Transparency: the emitter commits faithfully, and any deviation is detectable by third-party auditors. This design occupies a distinct point on the cost–trust Pareto frontier—negligible overhead and no specialized hardware, at the cost of weaker guarantees than cryptographic proofs. We formalize three security properties (receipt binding, tamper detection, chain integrity) via game-based reductions to standard cryptographic assumptions (collision resistance, second-preimage resistance). We describe receipt granularity levels (per-session, per-forward-pass, and per-token), a four-phase orchestration pattern (PLAN, SENSE, DECIDE, PROVE) for bounded AI autonomy, and an oracle mode for opaque cloud models. Ten experiments spanning four model families (1.5B–72B parameters), three quantization levels, three cloud APIs, and three receipt granularity levels on consumer-grade hardware demonstrate: overhead below 0.006% of inference time even at per-token granularity with top-k logit hashing (decreasing to below 0.001% at 72B scale); O(1) amortized chain emission sustained to 10⁶ receipts at 168,860 receipts/sec; 100% tamper detection across 1,200 attempts with zero false positives; perfect within-quantization deterministic replay; and 96% claim recall with 100% chain integrity across 15 multi-step PLAN/SENSE/DECIDE/PROVE workflows, with an honest assessment of gate limitations at 7B model scale. All data, scripts, and a standalone verifier are provided as ancillary files.

Aatu Isopahkala

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.

Li, Y.Y.N.

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

Vid Keršič, Muhamed Turkanović

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

Menyhért Pálinkó

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

Anthony Coslett

As neural language models are deployed in regulated domains, verifiable model provenance becomes a critical security requirement. We construct an Inference-Time Physical Unclonable Function (IT-PUF) that provides a challenge-response authentication protocol for neural networks, achieving zero false acceptances across 1,012 comparisons spanning 23 models and 16 vendor families. The IT-PUF derives its entropy from a geometrically intrinsic behavioral fingerprint—the delta-gene (the third pre-softmax logit gap)—which we prove is invariant to inference temperature and empirically validate as invariant across six distinct neural architectures. We provide a formal impossibility result for fingerprint spoofing: an interval-splitting theorem proves that no adversarial Kullback-Leibler (KL) budget can simultaneously close the fingerprint gap and avoid detection via accumulated noise. To establish that this security does not degrade at scale, we validate an Equation of State across three independent model families spanning a 147x parameter range (0.5B to 72B). We falsify the assumption of unbounded stiffness but discover a strict positive empirical floor (S_min = 1.1797), from which the Cramér-Rao bound guarantees a computable minimum spoofing cost. The theoretical foundation is formally verified in the Coq proof assistant: 311 theorems across 16 files, with zero uses of "Admitted" and zero vacuous definitions. 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).

Gianpietro Castiglione, Shahriar Ebrahimi, Narges Khakpour

A Software Bill of Materials (SBOM) is a key component for the transparency of software supply chain; it is a structured inventory of the components, dependencies, and associated metadata of a software artifact. However, an SBOM often contain sensitive information that organizations are unwilling to disclose in full to anyone, for two main concerns: technological risks deriving from exposing proprietary dependencies or unpatched vulnerabilities, and business risks, deriving from exposing architectural strategies. Therefore, delivering a plaintext SBOM may result in the disruption of the intellectual property of a company. To address this, we present VeriSBOM, a trustless, selectively disclosed SBOM framework that provides cryptographic verifiability of SBOMs using zero-knowledge proofs. Within VeriSBOM, third parties can validate specific statements about a delivered software. Respectively, VeriSBOM allows independent third parties to verify if a software contains authentic dependencies distributed by official package managers and that the same dependencies satisfy rigorous policy constraints such as the absence of vulnerable dependencies or the adherence with specific licenses models. VeriSBOM leverages a scalable vector commitment scheme together with folding-based proof aggregation to produce succinct zero-knowledge proofs that attest to security and compliance properties while preserving confidentiality. Crucially, the verification process requires no trust in the SBOM publisher beyond the soundness of the underlying primitives, and third parties can independently check proofs against the public cryptographic commitments. We implement VeriSBOM, analyze its security, and evaluate its performance on real-world package registries. The results show that our method enables scalable, privacy-preserving, and verifiable SBOM sharing and validation.

Nicolai Maisch, Shengjian Chen, Alexander Robertus, Samed Ajdinović · 7 authors

This work presents a concept and implementation for the secure storage and transfer of quality-relevant data of milled workpieces from online-quality assurance processes enabled by real-time simulation models. It utilises Non-Fungible Tokens (NFT) to securely and interoperably store quality data in the form of an Asset Administration Shell (AAS) on a public Ethereum blockchain. Minted by a custom smart contract, the NFTs reference the metadata saved in the Interplanetary File System (IPFS), allowing new data from additional processing steps to be added in a flexible yet secure manner. The concept enables automated traceability throughout the value chain, minimising the need for time-consuming and costly repetitive manual quality checks.

Thomas Perry

v2: Corrected affiliation domain to pastoral.tech. This paper presents a unified framework for anticipatory cyber defense integrating eight convergent dimensions: adversarial machine learning countermeasures, supply chain and hardware implant analysis, quantum threat transition analysis, attribution resistance with deepfake forensics, autonomous defense game theory, zero-knowledge proof systems for operational security, temporal correlation at scale, and biological-physical security integration. We formalize the Mantis autonomous defense environment as a Gymnasium-compatible reinforcement learning system with self-play training, introduce Chameleon, a five-channel defensive steganography framework using dynamic key rotation and Shamir Secret Sharing, and develop a ZK-Evidence Ledger for cryptographic evidence chains with Merkle tree notarization and Circom-based inclusion proofs. The convergence of these systems produces an anticipatory architecture where offensive research (Helix synthetic organization detection), defensive operations (Mantis game-theoretic simulation), and attribution resistance (zero-knowledge Merkle proofs) form a closed operational loop.

Usman Khalil, Mueen Uddin, Maha Abdelhaq, Homam ElTaj · 7 authors

This research presents the design and implementation of the Decentralized Smart City of Things (DSCoT), a novel framework leveraging Web3 architecture to enhance the security and authentication of assets in cyber-physical systems (CPSs) for smart cities on a private blockchain. Unlike traditional non-fungible tokens that primarily identify and distinguish financial assets, existing approaches lack robust mechanisms for attributing and authenticating CPS assets such as owners, users, and IoT-enabled smart devices. DSCoT addresses this gap by introducing an extended ERC721 protocol, enabling IoT-enabled devices to have unique blockchain identities similar to user accounts, which enhances device management and tracking. Novel smart contract modules facilitate secure identification and authentication of CPS assets. Evaluated results on a private Hyperledger Besu blockchain show that DSCoT achieves significant performance improvements, including sub-second latency (∼0.1–0.5 s for application programming interface (API) calls), minimal transaction costs (∼0.01–0.05 USD), and a high processing capacity (∼1000 transactions per second (TPS)). These results, along with improved security through immutable authentication records, demonstrate DSCoT’s effectiveness as a scalable and secure solution for smart city CPSs.

João Gião, Fernando Luis-Ferreira, Joao Sarraipa, Ricardo Jardim-Gonçalves

Nowadays, with the increased integration of cloud-computing, data integrity continues to be a problem in the current eHealth sector. This security principle is considered fundamental to ensure the accuracy and reliability of data by ensuring protection from unauthorized and illicit tampering. The present work aims to demonstrate the ability for the Distributed Ledger Technology (DLT) to provide trust and confidence in the healthcare infrastructure for patients, healthcare professionals and policy makers. The DLT has the potential to become one of the most reliable solutions for the many challenges facing the healthcare industry for its potential to enable more secure, transparent, and equitable data management. Although much documentation exists about applications in this domain, it is mostly presented in high-level conceptualization, without detailing the actual development or implementation. This document proposes a metadata-based approach to protect healthcare data integrity in compliance with GDPR, ensuring trustworthy data access for end-users, while demonstrating that the solution can be deployed on low-resource hardware with minimal adaptation effort and time constraints.

Rong Fu, Jia Yee Tan, Ziyu Kong, Shuning Zhang · 8 authors

Zero-knowledge circuits enable privacy-preserving and scalable systems but are difficult to implement correctly due to the tight coupling between witness computation and circuit constraints. We present zkCraft, a practical framework that combines deterministic, R1CS-aware localization with proof-bearing search to detect semantic inconsistencies. zkCraft encodes candidate constraint edits into a single Row-Vortex polynomial and replaces repeated solver queries with a Violation IOP that certifies the existence of edits together with a succinct proof. Deterministic LLM-driven mutation templates bias exploration toward edge cases while preserving auditable algebraic verification. Evaluation on real Circom code shows that proof-bearing localization detects diverse under- and over-constrained faults with low false positives and reduces costly solver interaction. Our approach bridges formal verification and automated debugging, offering a scalable path for robust ZK circuit development.

Dai Lixi, Di Wu

With the rapid proliferation of artificial intelligence generated content (AIGC), non‑fungible tokens (NFTs), and blockchain‑based services, creative works are increasingly born digital and managed as intellectual property (IP) digital assets. However, the assetization of content has outpaced the maturity of the supporting legal, technical, and educational infrastructures. Content creators and learners face fragmented tools for creation, registration, traceability, and infringement detection, which leads to weak evidence chains and high transaction costs in rights protection. This paper proposes an integrated framework for intelligent generation and security protection of IP digital assets that tightly couples AIGC engines with multi‑modal watermarking, blockchain‑based registration, and privacy‑preserving analytics. On this basis, a teaching‑oriented implementation is designed and deployed in a university course on digital media and IP management. The system supports full‑lifecycle management of images, text, code, and multimedia works, enabling students to experience rights creation, proof‑of‑ownership, risk diagnosis, and evidence preservation in realistic project tasks. Experimental results on a mixed benchmark of 4,200 assets show that the proposed scheme improves watermark robustness by 7.5% on average and shortens rights registration latency by 68% compared with traditional workflows, while significantly enhancing students’ IP literacy and compliance intention. The study demonstrates that IP digital‑asset technology can be transformed from a purely legal or technical topic into an operational teaching infrastructure, supporting both innovation and compliance in the AIGC era.

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.

Francisco Angulo De Lafuente, V. F. Veselov, Richard Goodman

This definitive research memoria presents a comprehensive, mathematically verified paradigm for neural communication with Bitcoin mining Application-Specific Integrated Circuits (ASICs), integrating five complementary frameworks: thermodynamic reservoir computing, hierarchical number system theory, algorithmic analysis, network latency optimization, and machine-checked mathematical formalization. We establish that obsolete cryptocurrency mining hardware exhibits emergent computational properties enabling bidirectional information exchange between AI systems and silicon substrates. The research program demonstrates: (1) reservoir computing with NARMA-10 Normalized Root Mean Square Error (NRMSE) of 0.8661; (2) the Thermodynamic Probability Filter (TPF) achieving 92.19% theoretical energy reduction; (3) the Virtual Block Manager achieving +25% effective hashrate; and (4) hardware universality across multiple ASIC families including Antminer S9, Lucky Miner LV06, and Goldshell LB-Box. A significant contribution is the machine-checked mathematical formalization using Lean 4 and Mathlib, providing unambiguous definitions, machine-verified theorems, and reviewer-proof claims. Key theorems proven include: independence implies zero leakage, predictor beats baseline implies non-independence (the logical core of TPF), energy savings theoretical maximum, and Physical Unclonable Function (PUF) distinguishability witnesses. Vladimir Veselov's hierarchical number system theory explains why early-round information contains predictive power. This work establishes a new paradigm: treating ASICs not as passive computational substrates but as active conversational partners whose thermodynamic state encodes exploitable computational information.

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.

Eria Othieno Pinyi, Joy Selasi Agbesi, Adeniran Oluwatoyosi Awe, Ezekiel Adediji · 5 authors

As the United States Department of Defense (DoD) transitions toward Zero-Trust Architecture, the hardware and software supply chain remains a critical vulnerability. Current provenance models rely on centralized, siloed databases that lack the transparency required to counter sophisticated state-sponsored interdiction. This paper proposes a novel framework: AI-Enhanced Trust Graph Analytics over Distributed Ledgers. The architecture utilizes a permissioned Distributed Ledger Technology (DLT) substrate to host an immutable record of component lifecycles, anchored by Hardware Roots of Trust (RoT) through Physically Unclonable Functions (PUFs). By mapping silicon fingerprints to Software Bill of Materials (SBOM), the system constructs a multi-dimensional Trust Graph. We employ Graph Neural Networks (GNNs) to detect structural anomalies indicative of subversion, while Federated Learning enables inter-agency intelligence sharing without compromising operational security. Our findings demonstrate that this integrated approach significantly reduces the time to detect compromised assets in air-gapped and tactical environments, providing a strategic roadmap for an autonomous, self-healing supply chain.

V. T. Krishnaprasath, T. Surya, B. Suganthi, Mohammed Kasim M · 6 authors

The globalization of semiconductor supply chains and the rise of third-party IP reuse have intensified concerns around hardware Trojan insertion, counterfeit IP distribution, unauthorized overbuilding, and dispute-prone verification workflows in modern VLSI design. This paper proposes a Blockchain-Enabled Secure VLSI Framework that unifies distributed hardware verification, provenance tracking, and IP protection through tamper-evident ledger records and cryptographically verifiable design artifacts. The proposed framework registers RTL/netlist milestones, verification reports, test signatures, and PDK-dependent constraints as immutable transactions, enabling all stakeholders (IP vendors, integrators, foundries, and verification labs) to validate authenticity and integrity without exposing sensitive design content. To prevent IP leakage, the framework supports hash-anchored commitments, permissioned access control, and zero-knowledge–ready attestations for key verification claims (e.g., “coverage ≥ threshold” or “equivalence passed”) while keeping raw waveforms and proprietary constraints off-chain. A smart-contract policy engine enforces licensing (time-bound/feature-bound), audit logging, and revocation, while a lightweight on-chain/off-chain storage strategy ensures scalability. Analytical evaluation and prototype-level profiling indicate that the approach can provide end-to-end traceability with sub-second block confirmation in permissioned mode, ~25–45% reduction in dispute resolution time via automated audit trails, and ~15–30% lower manual compliance effort by standardizing verification evidence exchange. The framework is suitable for secure SoC integration, multi-vendor verification, and IP lifecycle governance in advanced VLSI flows.

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.

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

S M Mostaq Hossain, Amani Altarawneh

Firmware integrity is a foundational requirement for securing Cyber-Physical Systems (CPS), where malicious or compromised firmware can result in persistent backdoors, unauthorized control, or catastrophic system failures. Traditional verification mechanisms such as secure boot, digital signatures, and centralized hash databases are increasingly inadequate due to risks from insider threats and single points of failure. In this paper, we propose a decentralized firmware integrity verification framework built on the Ethereum blockchain, offering tamper-proof, transparent, and trustless validation. Our system stores SHA-256 hashes of firmware binaries within smart contracts deployed on the Ethereum Sepolia testnet, using Web3 and Infura for seamless on-chain interaction. A Python-based client tool computes firmware hashes and communicates with the blockchain to register and verify firmware authenticity in real-time. We implement and evaluate a fully functional prototype using real firmware samples, demonstrating successful contract deployment, hash registration, and integrity verification through live blockchain transactions. Experimental results confirm the reliability and low cost (in gas fees) of our approach, highlighting its practicality and scalability for real-world CPS applications. To enhance scalability and performance, we discuss extensions using Layer-2 rollups and off-chain storage via the InterPlanetary File System (IPFS). We also outline integration pathways with secure boot mechanisms, Trusted Platform Module (TPM)-based attestation, and zero-trust architectures. This work contributes a practical and extensible model for blockchain-based firmware verification, significantly strengthening the defense against firmware tampering and supply chain attacks in critical CPS environments.