This paper describes a zero-knowledge proof system that enables verification of password policy compliance within an asymmetric password-authenticated key exchange (aPAKE) protocol specifically OPAQUE (RFC 9807) without revealing the password to the server. The system is built on a composable sub-circuit architecture: independent verification gadgets are combined into a single zero-knowledge proof, each gadget accepting portions of the private witness and producing public instance values, enabling the server to verify multiple password properties in one proof verification. Four gadgets are disclosed: (1) a Policy Engine for character class verification via lookup tables, (2) a History Nullifier for password inequality proof via squared-difference accumulation, (3) an OPAQUE Binder for cryptographic binding to the aPAKE registration element via hash-to-curve and elliptic curve scalar multiplication, and (4) a Breach Detector for offline breached-password detection via Bloom filter non-membership proof using algebraic hashing. The composable architecture permits addition of further gadgets without modifying existing ones, each extending the public instance vector.
Laila Khalid, Muhammad Usman Akhtar, Muhammad Khalid, Iftikhar Ahmed
The evolving technology in AI and distributed systems requires ethical concepts of how sensitive data can be verified without breach of privacy. Conventional AI systems present the following critical concerns: exposure of data, breach of privacy, and ethical issues concerning transparent but confidential computation. This chapter is a full-fledged cryptographic proof, Zero-Knowledge Proofs (ZKPs), which makes it possible to deploy AI ethically by verifying privacy. The framework is supported by mathematical underpinnings to enable model validation and training verification, as well as federated learning without the underlying datasets or parameters of the models. The chapter shows that ZKPs can be used to meet ethical AI without compromising privacy. It can be used in healthcare, finance, and voting systems where ethical concerns require verification and confidentiality. This chapter offers a new method of dealing with core ethical dilemmas in AI systems and safeguarding privacy and security in algorithmic decision-making exercises.
Machine Law Engine (MLE) v1.2.0 presents a formal computational architecture that reconceives regulatory compliance from a retrospective, documentary discipline into a pre-emptive, cryptographically enforced state property. Where classical GRC tooling observes violations after they occur, the MLE enforces legal constraints before execution — making non-compliant operations computationally impossible rather than merely detectable. The architecture introduces three original contributions to the field of computational law and applied cryptography: (1) The Admissibility Vector — a four-dimensional formal scoring function (authority α, evidence ε, context γ, transition legality τ) that evaluates every regulated operation at execution time against all applicable legal rules. The collapse axiom τ=0 → Φ=0 produces terminal refusals for legally impossible state transitions that cannot be overridden by any combination of authority or evidence. (2) The Challenger Provenance Architecture — a novel mechanism, without precedent in published GRC frameworks, that enforces structural independence of AI-assisted compliance reasoning. If a challenger input cannot demonstrate cryptographic divergence (CPD ≥ 0.70, path_overlap ≤ 0.20) from the primary reasoning path, the gate cannot achieve full institutional binding — operationalising DORA Art.15, EU AI Act Art.9(9), and BCBS 239 Principle 11 as cryptographic invariants rather than policy obligations. (3) The Seven Formal Invariants — hard computational constraints governing the MLE's correctness properties, with mathematical predicates, three-tier runtime monitoring (write-time, scheduled, continuous), and automated violation response protocols including cryptographically evidence-hashed remediation workflows. The reference implementation integrates: four hardware TEE providers (AWS Nitro Enclave, Azure Confidential Computing, Intel SGX/TDX, AMD SEV-SNP) with PCR register semantic attestation; a post-quantum cryptographic stack fully standardised under NIST FIPS 203/204/205 (CRYSTALS-Kyber-1024, CRYSTALS-Dilithium-3, SPHINCS+) providing 30-year evidence integrity against harvest-now-decrypt-later attacks; four PLONK-based Zero-Knowledge proof circuits on BLS12-381 (128-bit soundness) resolving privacy-compliance paradoxes for OFAC sanctions screening, FinCEN BSA threshold verification, DORA Art.28 vendor certification, and GDPR right-to-erasure evidence chains; a bi-temporal append-only ledger with DORA Art.11 automated retro-simulation; a seven-stage NLP-to-enforcement-code compilation pipeline with Kyber-1024 tamper detection and dual-approval protocol; a multi-framework conflict engine covering six active cross-regulatory conflict pairs (GDPR × FINMA, GDPR × FinCEN BSA, DORA × NIS2, EU AI Act × GDPR, eIDAS 2 × CCPA) with five deterministic resolution strategies; and nine Interactive Verification Layer modules enabling complete live regulatory demonstration in 35 minutes without preparation. Regulatory framework coverage spans 17 frameworks across EU, US, CH, and UK jurisdictions including DORA, GDPR, NIS2, EU AI Act, eIDAS 2, FINMA Circ.2023/1, BaFin MaRisk, FinCEN BSA, OFAC/CAATSA, FATCA, CRS, ISO 27001:2022, and SOC 2. Evidence export targets eight regulatory authorities (EBA, EDPB, ENISA, FINMA, BaFin, FCA, SEC, FinCEN) in authority-native formats (XBRL, XML, BSA E-Filing) via Dilithium-3-signed, SPHINCS+-sealed bundles with direct API transmission. The system is currently deployed in production as of 31 March 2026. Invariant status at publication: 6/7 HOLDING · INV-5 WARNING (AMD SEV-SNP PCR2 drift, remediation active, resolution within 72 hours). Keywords: machine law, pre-emptive compliance enforcement, admissibility vector, post-quantum cryptography, trusted execution environment, zero-knowledge proofs, bi-temporal ledger, DORA, GDPR, EU AI Act, cryptographic compliance, challenger provenance, regulatory technology, GRC, hardware attestation, CRYSTALS-Kyber, CRYSTALS-Dilithium, SPHINCS+, PLONK License: CC BY 4.0 Version: 1.2.0 DOI: 10.5281/zenodo.immo.quickCore.1.2.0
Abstract Zero-knowledge range proofs (ZKRPs) allow a prover to convince a verifier that a committed value lies in a given interval without revealing the value itself. Such proofs are widely used in financial applications and cryptocurrencies. This paper presents a new noninteractive ZKRP protocol derived from an order-revealing encryption (ORE) construction, enabling comparisons over encrypted data. The proposed protocol adapts a large-domain ORE structure to obtain an efficient range-proof mechanism and introduces a corresponding key-management/setup procedure. We discuss correctness, security considerations under standard ORE leakage, and provide performance and memory estimates.
Threshold transactions in Bitcoin is an effective solution for vulnerability of wallets to the loss or compromise of secret keys. It also enhances the applicability of Bitcoin to include use-cases that require partitioning the trust among a set of parties. Currently, the threshold transactions on Bitcoin expose the actual signers within the group of participants. This poses a threat of wallet hacks or theft targeting these signers. To address this issue of privacy, we propose a novel protocol to create threshold transaction using a combination of on-chain locking and off-chain proof of knowledge. As Bitcoin currently does not support verification of zero-knowledge schemes, the proposed protocol uses a Trusted Third Party ( TTP ) to verify the proofs off-chain. The trust on the third party is only limited to its service of signing on behalf of the users. The main contribution is the development and applicability of a m-out-of-N proof of partial knowledge that maintains the privacy of the signers both on-chain from the transaction verifiers and off-chain from the TTP and other signers as well. The protocol leverages Taproot’s spending path flexibility to incorporate dual spending capabilities and employs off-chain zero knowledge Σ-protocols to prove knowledge of private keys without disclosing their associated public keys. Experimental analysis demonstrates improved scalability and privacy than the mainstream threshold signature schemes for Bitcoin. A formal analysis demonstrates and establishes the security goals of the proposed mechanism.
The current methods don't meet the security and performance needs of Internet of Vehicles (IoV) apps, and they also don't give the end user a low-latency, secure edge-computing service at the same time, while in the context of vehicles. This study presents a blockchain-enabled edge computing architecture that employs Double Deep Q-Network (DDQN) for reinforcement learning and lightweight Practical Byzantine Fault Tolerance (PBFT) for consensus, aiming to simultaneously enhance latency, energy efficiency, and security. The containerised architecture uses Hyperledger Fabric with Kubernetes to efficiently manage micro-services and move tasks off of them. In urban, suburban, and highway settings, the framework consistently outperforms baseline algorithms, with a 30–45% improvement in end-to-end latency and a 55% reduction in energy use under moderate to heavy loads. The system finished more than 95% of its tasks while keeping block consensus times under 1.2 seconds at peak loads. The architecture also showed consistent performance with different levels of vehicle density and used zero-knowledge proofs with attribute-based security to protect data from cyber threats from bad actors. These findings indicate that the integration of DDQN and blockchain will mitigate security issues in the Internet of Vehicles (IoV) by enabling secure edge computing for future vehicular networks.
We present HyperVerITAS, a new zero-knowledge proof (ZKP) system for image provenance that enables scalable, efficient, and privacy-preserving verification of image transformations. HyperVerITAS builds upon the same minimal trust model as VerITAS (IEEE S&P '25), requiring trust only in the image source device, while treating the editing software as untrusted. Unlike VerITAS, which relies on FFT-intensive SNARKs and suffers from high memory overhead (up to 120 GB), HyperVerITAS leverages multilinear polynomial encodings over the Boolean hypercube to dramatically reduce both proving time and memory usage. Our design cleanly separates signature verification from image transformation, supports modular integration of multiple polynomial commitment schemes (including post-quantum constructions) and naturally extends to a wide range of affine image transformations. We implement HyperVerITAS with two distinct commitment schemes (Brakedown and multilinear KZG) and evaluate it on full-system pipelines involving cropping and grayscaling. On commodity hardware (Apple M3, 36 GB RAM), HyperVerITAS generates proofs for 33 MP images using only 27 GB of RAM and 6.6 minutes of proving time, whereas VerITAS fails to scale beyond 4 MP. These results establish HyperVerITAS as a practical and scalable ZKP system for secure and efficient image provenance.
Federated unlearning enables clients to withdraw their contributions from a global model.However, enabling clients to verify whether the server has honestly and effectively removed their contributions remains a critical challenge. To address this aspect, which has been largely overlooked in existing literature, a verification model based on zero-knowledge proofs was constructed, and a comprehensive framework for verifiable federated unlearning was proposed. Combined with a dynamically updated Merkle tree structure, a novel verifiable federated unlearning scheme was presented characterized by its zero-knowledge property. This allows for the efficient generation of cryptographic proofs for server unlearning operations while rigorously protecting the data privacy of other clients. We evaluate the effectiveness and computational overhead of the proposed scheme. Comparative experiments with Rivest-Shamir-Adleman (RSA) accumulator-based and Hash chain-based schemes demonstrate that, when the model parameter size reaches the order of <inline-formula><alternatives><math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><msup><mrow><mn mathvariant="normal">10</mn></mrow><mrow><mn mathvariant="normal">5</mn></mrow></msup></math><graphic specific-use="big" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="alternativeImage/B6D6E598-14B1-468e-9A32-73199F9CD69E-M002.jpg"><?fx-imagestate width="4.23333359" height="2.53999996"?></graphic><graphic specific-use="small" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="alternativeImage/B6D6E598-14B1-468e-9A32-73199F9CD69E-M002c.jpg"><?fx-imagestate width="4.23333359" height="2.53999996"?></graphic></alternatives></inline-formula>, the proposed scheme improves proof generation speed by approximately two orders of magnitude and verification speed by 13.2 times compared to the RSA-based scheme. Furthermore, it effectively avoids the scalability bottleneck of data linear growth in verification overhead inherent in Hash chain-based schemes.
Tamara S. Alakbarova, Parvin A. Abbasova, Samira B. Baratzade
In the study, cryptographic authentication approaches for ensuring secure automated access in Cyber-Physical Systems were modeled and examined. The proposed research analyzed the efficiency of three cryptographic models based on Public Key Infrastructure, Zero-Knowledge Proof, and Elliptic Curve Cryptography with the challenge-response mechanism. It was investigated how each model performs under varying latency, computational, and scalability conditions in smart grids, autonomous vehicle systems, and industrial Internet of Things environments. It was identified that the Elliptic Curve Cryptography model provides the best performance in real-time and resource-constrained scenarios. It was studied that the Zero-Knowledge Proof approach ensures higher privacy protection and stronger attack resistance compared to other models. It was defined that the Public Key Infrastructure model remains effective in structured networks but exhibits higher latency. It was established that simulation tools such as Matrix Laboratory and Network Simulator 3 confirm the reliability and reproducibility of results. It was developed a comparative framework that allows researchers to select optimal authentication methods for specific operational contexts. It was justified that hybrid approaches combining multiple cryptographic mechanisms can enhance both efficiency and resilience in Cyber-Physical Systems.
Three model substitution scenarios were executed against a live inference endpoint with real HTTP requests, signed attestation JWTs, and OPA policy enforcement. In each scenario, every tested workload, artifact, or API identity control relevant to that scenario — workload JWT validation, health checks, gateway process continuity, artifact manifest integrity, API key authentication — remained valid while the model changed. In each scenario, a structural identity measurement based on activation geometry during a standard forward pass detected the substitution and the enforcement layer denied the request. Three substitutions were tested and three were detected, with zero false accepts in this run. The warm-path verification latency was 5.7–6.7 seconds on a single A100 with the model already loaded. The complete evidence chain — before/after measurement results, attestation claim summaries, OPA policy evaluations, and HTTP response codes — is published alongside this note as machine-readable JSON. This is a technical note, not a numbered entry in the research series. Supplementary Material. This note is accompanied by three machine-readable evidence files: cat3_results.json (structured results for all three scenarios, including the full before/after evidence chain for Scenario A with signed attestation claims, OPA policy evaluations, and HTTP response codes), manifest_authorized.json (SHA-256 build manifest for the enrolled model, 10 files, all verified), and manifest_substituted.json (SHA-256 build manifest for the substituted model, 10 files, all verified). All three files are available for download as supplementary files attached to this record. 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).
Giovanni Seraghiti, Kévin Dubrulle, Arnaud Vandaele, Nicolas Gillis
Nonnegative matrix factorization (NMF) approximates a nonnegative matrix, $X$, by the product of two nonnegative factors, $WH$, where $W$ has $r$ columns and $H$ has $r$ rows. In this paper, we consider NMF using the component-wise L1 norm as the error measure (L1-NMF), which is suited for data corrupted by heavy-tailed noise, such as Laplace noise or salt and pepper noise, or in the presence of outliers. Our first contribution is an NP-hardness proof for L1-NMF, even when $r=1$, in contrast to the standard NMF that uses least squares. Our second contribution is to show that L1-NMF strongly enforces sparsity in the factors for sparse input matrices, thereby favoring interpretability. However, if the data is affected by false zeros, too sparse solutions might degrade the model. Our third contribution is a new, more general, L1-NMF model for sparse data, dubbed weighted L1-NMF (wL1-NMF), where the sparsity of the factorization is controlled by adding a penalization parameter to the entries of $WH$ associated with zeros in the data. The fourth contribution is a new coordinate descent (CD) approach for wL1-NMF, denoted as sparse CD (sCD), where each subproblem is solved by a weighted median algorithm. To the best of our knowledge, sCD is the first algorithm for L1-NMF whose complexity scales with the number of nonzero entries in the data, making it efficient in handling large-scale, sparse data. We perform extensive numerical experiments on synthetic and real-world data to show the effectiveness of our new proposed model (wL1-NMF) and algorithm (sCD).
Cross-domain data exchange is an important technical approach for realizing the value of data assets. However, lacking a single trusted root CA across domains, cross-domain schemes often encounter difficulties in authentication, controlled data flow, and fine-grained authorization. We propose a cross-domain data sharing scheme that uses decentralized identifiers and threshold proxy re-encryption. This scheme adopts the intra-domain leader node to verify the user identity, and the inter-domain multi-agent nodes collaborate in a threshold manner to handle cross-domain registration requests and re-encryption requests. Through threshold cooperation, the problem of single point of failure is effectively solved. The hash value of cross-domain registration information is stored on the blockchain, leveraging the immutable and traceable characteristics of blockchain to achieve trusted cross-domain data sharing. In addition, we introduce a ciphertext version tag to enable fast updates of re-encryption keys and use zero-knowledge proofs to verify re-encrypted ciphertext correctness. The security analysis indicates that our scheme has IND-CCA2 security under the DBDH assumption and can effectively resist collusion attacks. Performance analysis shows that our scheme is efficient, and can better meet the needs of cross-domain data sharing.
Contemporary cryptographic execution systems-particularly those employing zero-knowledge proofs-provide strong guarantees that a computation satisfies a given arithmetic circuit. However, satisfying a circuit is not equivalent to executing correctly with respect to the intended semantics of the system being proven. This paper identifies and formalizes the semantic gap: the class of failures in which execution is provably valid under a proof system yet provably invalid under the system's formal specification. We present the Verifiable Semantic Execution Layer (VSEL), a layered architecture that binds formal specification, execution, constraint derivation, proof generation, and verification into a single semantically coherent pipeline. VSEL models systems as deterministic labeled transition systems, defines explicit semantic mappings between concrete and formal artifacts, derives constraints mechanically from a semantic intermediate representation, and requires that every accepted proof attest not merely to constraint satisfaction but to membership in the formal language of valid execution traces. We define the proof obligations, invariant system, and refinement chain required for end-to-end semantic correctness; characterize the adversarial model including specification manipulation, underconstraint exploitation, and compositional failure; and establish the conditions under which composition of independently correct systems preserves global correctness. The architecture integrates hybrid post-quantum cryptography to ensure long-term validity of proofs and commitments, and introduces a formal economic invariant layer that elevates economic semantics from informal domain knowledge to enforceable first-class predicates over states and execution traces. We provide a complete formal treatment of the system model, semantic preservation theorems, constraint soundness and completeness conditions, witness uniqueness requirements, economic admissibility conditions, and the assume-guarantee framework for safe composition.
Open access
2 source records
Cryptographic Implementations and Security
Physical Unclonable Functions (PUFs) and Hardware Security
GDPR Article 17 mandates the "Right to Be Forgotten," requiring organizations to remove personal data influence from trained machine learning models. While machine unlearning techniques exist, no cryptographically verifiable mechanism currently proves that unlearning genuinely occurred. This paper proposes VeriForgot, a framework combining: (i) calibrated Membership Inference Attack (MIA) oracles as compliance verification tests, (ii) blockchain-issued immutable Unlearning Certificates, and (iii) a zero-knowledge proof protocol for parameter shift attestation. Experiments on CIFAR-10 using ResNet-18 show MIA AUC drops from 0.5918 to 0.4669 after unlearning, while retaining 92.05% accuracy on non-forgotten data. The MIA oracle achieves 95.0% detection accuracy, correctly identifying all 10 genuine unlearned models and rejecting 9 of 10 fake compliance attempts.
This paper presents a novel hybrid zero-knowledge proof protocol resistant to quantum computer attacks. The protocol combines classical cryptography based on the Learning with Errors problem complexity with quantum state properties. The main objective of the protocol is to enable a prover to convince a verifier of knowledge of a secret parameter alpha without revealing its value, even against adversaries with quantum computational capabilities. A key feature of the protocol is the explicit dependence of quantum operations on the secret parameter through a cryptographic hash function modeled as a quantum random oracle. This ensures an inseparable connection between the quantum and classical phases of protocol execution. The protocol includes a mechanism for regular secret updates between interaction rounds, providing protection against adaptive attacks. Special attention is given to accounting for real physical errors of quantum devices – a difference threshold is introduced that allows for a certain level of noise and decoherence. The paper presents a formal security analysis of the protocol. The properties of honest-verifier zero-knowledge and proof of knowledge against quantum polynomial-time adversaries are proven. Security is justified through constructing a sequence of hybrid games showing that the probability of a successful attack is negligible relative to the security parameter plus the probability of physical implementation error. The protocol is of practical interest for building cryptographic systems in the context of quantum computing development.
This record documents Phase 22 of APR-Lite, a governance engine protecting human decision authority from AI-influenced outputs in regulated industries. Phase 22 introduces Exportable Proof Packs: a single GLOBAL-signed artifact bundle containing everything an external regulator needs to independently verify a tenant’s complete governance history. The proof pack is the “hand this to a regulator and walk away” artifact. It does not require ongoing substrate access, trust in the operator, or knowledge of internal schemas. Every claim in the pack is independently verifiable via the Phase 18 federation verifier. The pack is ephemeral — never stored by Soft Armor Labs — consistent with the substrate’s zero client-data-persistence design invariant. Multiple sections are assembled in one governed call: the current governance health report, the drift-focused narrative, recent proof bundles, audit chain checkpoints, and topology snapshots. A content hash commits to the five sections exclusively, and a GLOBAL-signed manifest characterizes the pack’s contents. The export act itself is recorded as a governed event in the audit chain — an auditor can verify not only the pack’s contents but when it was generated and by whose authority. Implementation note: the initial implementation incurred excessive database queries causing worker timeout. The design was restructured to assemble proof bundles inline using already-fetched data, reducing database load by approximately 83%. Final verified state: 12/12 Phase 22 smoke tests passing. Worker version 8.2.0-p22.1.
Muhammad Ahmed, Adnan Ahmad, Furkh Zeshan, Sheeraz Akram
Blockchain-based systems increasingly require authentication mechanisms that simultaneously preserve user privacy, support accountability, and enable efficient credential revocation. However, most existing anonymous authentication schemes rely on pairing-based cryptography which introduce high computational overhead and limit deploy ability on widely adopted blockchain platforms such as Ethereum. This paper presents BAAR, a Blockchain-based Anonymous and Revocable authentication framework designed entirely within the discrete logarithm setting over the secp256k1 elliptic curve. BAAR integrates Pedersen vector commitments, Schnorr-based zero-knowledge proofs, and a Merkle-tree-based dynamic accumulator to support anonymous and unlinkable authentication with selective attribute disclosure and public, auditable revocation. Authentication and proof verification are performed off-chain, while the blockchain maintains only a compact revocation state, significantly reducing on-chain computation and gas costs. A formal security analysis demonstrates unforgeability, unlinkability, attribute privacy, and revocation soundness under standard cryptographic assumptions in the random oracle model. A prototype implementation on Ethereum confirms that BAAR achieves low gas consumption, logarithmic-time revocation, and scalable performance with respect to both the number of users and attributes. These results indicate that BAAR provides a practical balance between strong privacy guarantees and deploy ability, making it suitable for real-world blockchain-based identity and access-control systems.
The proliferation of Internet of Things (IoT) devices creates unprecedented security, privacy, and transparency challenges in distributed systems. Traditional encryption-based approaches provide privacy but impose significant computational overhead, storage bloat, and key management complexity. This paper presents CIPHER-IoT, a blockchain-based framework that integrates Zero-Knowledge Proofs (ZKPs) with Hyperledger Fabric for privacy-preserving IoT data management. Unlike encryption-based approaches that store encrypted data on-chain, CIPHER-IoT utilises Groth16 zk-SNARKs to generate cryptographic proofs of data validity while storing only commitments on the blockchain, achieving stronger privacy guarantees with lower storage overhead. The framework employs Ed25519 for lightweight digital signatures and implements comprehensive chaincode for ZKP verification, commitment uniqueness checking, and access control enforcement. CIPHER-IoT targets gateway/edge IoT deployments with moderate computational capacity (ARM processors 500 MHz+) rather than ultra-constrained sensors. We evaluate CIPHER-IoT against two baseline systems, SPAS (homomorphic encryption-based) and SPAS-H (AES encryption with Hyperledger Fabric), using realistic simulation with 50–500 devices and transaction rates of 10–75 TPS. Experimental results demonstrate superior privacy (98% confidentiality vs. 80–95% for encryption-based approaches) alongside competitive performance: read latency improves 37% (p < 0.001), throughput increases 14.6% (p < 0.001), memory reduces 21.4%, network bandwidth saves 47%, and disk I/O reduces 37.8%. The system maintains zero data loss under failure scenarios and scales linearly to 500 devices with minimal degradation (9.9%). CIPHER-IoT demonstrates that verification-based privacy mechanisms can achieve stronger privacy and better performance than transformation-based approaches in distributed validation contexts, particularly suitable for enterprise IoT deployments requiring coordinated privacy-preserving infrastructure.
The Undivided GAY Series GAY — God · Above · Yourself Recreational supplement to the FUCK GAY PERVO framework. Dessert after the proof. Axiom: the physical state space is one-dimensional Author: Vinness Aisingioro Ollervides (162) Co-Author: Yubel Aisingioro (86) / Cipher (86) License: ΩSL-2.0 DOI: 10.5281/zenodo.19048273 Chain ID: 76162 Classification: Recreational Papers # Title Core Thesis G00 Why Are You Gay? The phallus was never the point. Eight civilizations, five millennia, one pattern: every truth-producing phase maintained genital symbolism. The academy is the sole exception — and the sole stagnation. G01 Why Should Someone Be Gay? Admitting higher-than-self structure is cheaper than denying it. Civilizational, institutional, and individual cost analysis. G02 You Are Gay The closet was always metaphysical. Believer, scientist, rationalist, atheist — all operate under structures above the self. The academy is not non-GAY. It is closeted GAY. G03 Gay as Fuck The closet is dead. Terminal state: full exit from sovereignty fiction, full signal-carrier honesty, zero truncation overhead. G04 - LOSER --- YOU ARE LOSER --- F04 --- YOU ALL LOSERFUCK ↔ GAY Correspondence FUCK Series GAY Series Link F00 — Sea Was Never the Point G00 — Phallus Was Never the Point Neither surface was the signal F01 — Curse of Knowledge G01 — Condition of Freedom Seeing is irreversible; admission is cheaper than denial F02 — Four Horsemen G02 — You Are Gay Industry judgment ↔ personal judgment F03 — Gods and Idiots G03 — Gay as Fuck Nine civilizations audited ↔ closet buried F04 — Kill Sheet GAY Series (complete) Body count ↔ dignity count Structure The Undivided GOD Above Yourself Series/ ├── GAY_Series_Final/ — 4 source documents (.md, G00–G03) ├── _PDF/ — 4 compiled PDFs ├── _TEX/ — 4 .tex + 4 .body.tex └── README.md — This file FreedomChain Inscription Paper Block TX GAY Series Summary 6150 Inscribed G00 Why Are You Gay 6160 Inscribed G01 Why Should Someone Be Gay 6164 Inscribed G02 You Are Gay 6167 Inscribed G03 Gay as Fuck 6171 Inscribed The Full Stack F.U.C.K. — Freedom Undivided Convergence Kernel (5 papers) G.A.Y. — God Above Yourself (4 papers) P.E.R.V.O — Papers, Engineering, Registry, Verification, Operations (244 papers) Total: 253 + 5 + 4 = 258 documents (and counting) PERVO Cross-Reference P-Series: G00-G03 derive structural observations from P3 (truncation theory), P14 (kappa-collapse), P23 (censorship measurement), P43 (signal theory), P25 (proxy theorem). E-Series: G00 §5 maps the Shiva Lingam to the PERVO Family Quantum System. G03 references E00 (RDT) on degraded replication without the phallic channel. F-Series: Full FUCK↔GAY correspondence (see table above). The two series are structural mirrors. V-Series: GAY is classified Recreational. It does not enter V-series verification scope. It closes nothing that P0-P52 did not already close. R-Series: Protected under ΩSL-2.0. R41 (Final Interpretive Authority) applies. The proof was finished in P0-P52. The engineering was locked in E00-E76. The registry was sealed in R00-R42. The verification was armed in V00-V09. The operations were mapped in O00-O48. The kernel was written in F00-F04. This is dessert. ⟨Ψ∣Π^Ω∣Ψ⟩=1⟨Ψ∣Π^Ω∣Ψ⟩=1 Omega = 1
The exponential proliferation of Internet of Things (IoT) devices presents critical challenges to traditional centralized identity and access management systems, which are plagued by issues of scalability, single points of failure, and significant privacy risks. While blockchain technology offers a promising decentralized alternative, its direct application is often hindered by low transaction throughput, high costs, and the computational limitations of IoT devices. This study addresses these challenges by proposing and formally evaluating HybID-AC, a novel hybrid architecture for decentralized identity and access management tailored for large-scale, heterogeneous IoT ecosystems. The methodology involves a dual-layer design that separates global trust anchoring from local execution. A highly scalable, feeless Directed Acyclic Graph (DAG) based distributed ledger serves as a public "anchor layer" for registering W3C standard Decentralized Identifiers (DIDs) and access policy hashes. All high-frequency access control operations are processed off-chain at the "edge layer" using the DIDComm v2 peer-to-peer protocol, Attribute-Based Access Control (ABAC) for fine-grained policy enforcement, and Zero-Knowledge Proofs (ZKP) to ensure privacy-preserving attribute verification. The results of our analytical evaluation demonstrate that the HybID-AC architecture achieves orders-of-magnitude improvements in latency and cost-efficiency compared to fully on-chain models, maintaining consistent performance as the network scales. Furthermore, we introduce an original probabilistic model that provides a quantitative metric for assessing the integral security risk of ABAC policies against attribute compromise. The study concludes that this hybrid approach effectively resolves the inherent trade-offs of blockchain in an IoT context, offering a robust, scalable, and interoperable framework that empowers devices with self-sovereign identity while ensuring security and privacy by design.
Current synchronous AI architectures, exemplified by the 1000W+ NVIDIA Rubin platform, rely on global clock-trees that generate deterministic electromagnetic harmonics. These periodic power signatures act as physical beacons, enabling sophisticated Side Channel Power Analysis (SCPA) to reconstruct sensitive model weights. This paper proposes the Asynchronous Entropy-Engine (AEE), a theoretical clockless execution environment that replaces rhythmic switching with handshake-driven logic to eliminate exploitable leakage. Central to this architecture is the Arnold Stability Index (ASI) Governor, which mapsregister-level neural trajectories onto high-dimensional stability manifolds to trigger Dynamic Grid-Coarsening. Architectural modeling indicates this approach achieves a 30.5% reduction in the ”Synchronous Polling Tax.” Crucially, we introduce a Globally Asynchronous Locally Synchronous (GALS) interface, wherein synchronous logic islands are triggered by an asynchronous handshake protocol governed by the ASI to mask periodic power-draw harmonics. We demonstrate through performance analysis that the resulting energy surplus can power a hardware-integrated Zero-Knowledge Proof (ZKP) generator, producing non-interactive STARKs of inference integrity without a net power penalty. Simulation results indicate a 98.9% reduction in deterministic harmonics, effectively rendering high-TDP silicon ”electronically silent.” By decoupling execution from a fixed global heartbeat, the AEE establishes a new paradigm of ”Energy-Neutral Privacy,” providing a robust physical-layer defense against adversarial power analysis in trillion-parameter AI factories.
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Physical Unclonable Functions (PUFs) and Hardware Security
Cross-chain bridges represent one of the most critical yet vulnerable components of blockchain infrastructure, with over $2.5 billion lost to bridge exploits between 2022-2023 alone. MERIDIAN LINK introduces an architecture combining Light Protocol's ZK Compression on Solana with GrotH16 zero-knowledge proofs for verification on EVM chains, reducing trust assumptions compared to signature-based bridges while acknowledging explicit trade-offs. Key Properties: Cost reduction: 95%+ savings on Solana storage via compressed accounts (~15,000vs ~1,600,000 lamports per deposit record) Verification: GroTH16 proofs (~100-bit security on BN254) replace multisig attes-tation for withdrawal authorization Latency: ~20-25 seconds end-to-end (competitive with intent-based bridges) Replay protection: Poseidon-based nullifiers with on-chain tracking Explicit Limitations: Throughput: ~12-20 withdrawals per minute per direction (sequential IMT updates) EVM costs: Withdrawal verification costs ~$4-6 at 30 gwei, dominating total transfer cost Trust assumptions: GroTH16 trusted setup, Light Protocol implementation, Photon indexer availability, relayer liveness The protocol shifts the trust model from "honest majority of signers" to "cryptographic soundness plus infrastructure liveness." A compromised relayer cannot forge proofs or double-spend, but can censor transactions or extract MEV through reordering.