Abstract This concept documents a complete physics-first authorisation architecture for the quantum-permanent era — applicable across AI cluster security, interbank settlement, space and interplanetary infrastructure, critical infrastructure protection, digital identity, supply chain integrity, and optional democratic participation tools. The architecture rests on a single physical principle: a cryptographic credential that no longer exists cannot be recovered by any computation, quantum or classical, regardless of future advances in hardware or algorithms. The concept extends the Temporal Rotation Security Protocol (TRSP v3, DOI: 10.5281/zenodo.20324081) and the TRSP Digital Coin (TDC v2, DOI: 10.5281/zenodo.20332811) with a unified Layered Temporal-Quantum Security (LTQS) framework. LTQS combines NIST FIPS 203/204-standardised Post-Quantum Cryptography (ML-KEM, ML-DSA) as Layer 0 — mathematical transit security — with TRSP temporal rotation as Layer 1 — physical credential elimination through hardware-enforced destructive readout within a configurable rotation window (10–500 ms). Layers 2 and 3 add geographically distributed hybrid dynamic quorum validation and LEO satellite orbital entropy anchoring with relativistic timestamp verification. An integrated adaptive AI management layer selects security profiles dynamically across High-Assurance, Standard, Degraded, and Emergency modes — guaranteeing graceful degradation to pure PQC fallback when physical infrastructure is unavailable. The hardware commitment module previously documented as CRATON is architecturally designated URDHR, after the Norse Norn of the irrecoverable past. The two complementary quorum layers are designated VERÐANDI (present-moment ground quorum) and SKULD (future-anchoring orbital quorum) — the three Norns mapped to the three temporal dimensions of cryptographic security. Prior art established under the CRATON designation in all previously published documents extends fully to the URDHR designation. Fifteen novel contributions are placed on the public record as defensive prior art: NC-TDC-21 (AI-to-AI Micropayment Architecture), NC-TDC-22 (Macroscopic Environmental Entropy as Optical Physical Unclonable Function), NC-TDC-23 and NC-TDC-23a (Macroscopic Polymorphic Cipher with Dynamic Dimensional Entropy — exploratory), NC-TDC-24 (TRSP Democratic Coercion Shield — exploratory, extending Juels-Catalano-Jakobsson coercion-resistant voting literature), NC-TDC-25 (Continuous Anonymous Democratic Pulse — exploratory), NC-TDC-26 (Physical Proof of Presence consensus mechanism operating at the Landauer thermodynamic minimum), NC-TDC-27 (Temporal Scarcity Value Architecture anchored in thermodynamic time-arrow irreversibility), NC-TDC-28 (AI Exchange Consortium Architecture), NC-TDC-29 (Biometric Supply Architecture), NC-TDC-30 (CRATON Chain Coin Identity Architecture without persistent private key), NC-TDC-31 (Three-Phase Value Architecture), and NC-TDC-32 (Cooperative Multi-Anchor Currency Architecture with Founder-Operator Equity-Plus-Operating-Margin Compensation Structure). NC-URDHR-1 and NC-TRSP-Hybrid-1 formalise the Three-Norn naming framework and the four-layer hybrid post-quantum/temporal architecture respectively. NC-TDC-32 is the central economic contribution of this version. It formalises a digital currency architecture in which multiple stakeholder classes — AI infrastructure operators, financial institutions, sovereign states, and individual participants — coexist as independent issuing classes within a single cooperative cryptographic framework. Each class mints its own coin contingent backed by its own economic activity rather than by shared monetary authority. Phase transitions admit new classes through supply expansion, not through re-pricing of existing coins. Coin denomination is calibrated from inception across micropayment to reserve-asset volume regimes via the monetary identity M·V = P·Q. The infrastructure operator class — the AI companies that build and continuously operate the adaptive security layer — is compensated through a two-component structure: bounded equity recognition at phase transitions (capped, independently audited) plus formula-bound operating margin on continuing services. This two-component compensation model is economically required to keep operating margins moderate and the architecture competitive against established settlement infrastructures. Monetary sovereignty remains exclusively with the issuing class for each contingent; the operator class operates the cryptographic issuance infrastructure but does not exercise monetary authority over any contingent. The architecture is the first formalised digital implementation of the cooperative multi-stakeholder economic model previously demonstrated at continental scale only by the Hanseatic League (twelfth to seventeenth century). All fifteen contributions are documented as conceptual frameworks. Production Concepts (NC-TDC-21, NC-TDC-22, NC-TDC-26 through NC-TDC-32, NC-URDHR-1, NC-TRSP-Hybrid-1) represent architecturally sound design patterns ready for implementation evaluation. Exploratory Concepts (NC-TDC-23, NC-TDC-23a, NC-TDC-24, NC-TDC-25) document underlying architectural ideas requiring further formal research. All specific implementation parameters — quantities, ranges, governance percentages, consortium composition — are illustrative starting points belonging to the institutions that choose to implement the architecture. A dedicated Part 9 — Engineering Considerations and Open Challenges — documents five anticipated technical reviewer questions with referenced solution pathways from current research literature: global consensus latency under M-of-N geographically distributed validation (Sliding Window Key Rotation with Dual-Key Buffers, TLS 1.3 RFC 8446); fuzzy extractor Helper Data leakage in optical entropy capture (Controlled PUF Finite State Machine architectures eliminating Helper Data transmission, addressing Becker 2015); orbital quorum availability under atmospheric and orbital dynamics constraints (Multi-Path Delivery with configurable Grace Periods and Layer 2 graceful degradation); post-quantum zero-knowledge proof latency for autonomous AI agent commerce (Off-Critical-Path ZKP architecture separating HMAC authorisation from asynchronous identity verification); and multi-anchor synchronisation between independent issuance classes (Key-ID and class-identification headers preserving structural separation between technical operation and monetary sovereignty). Part 9 introduces no additional Novel Contributions — it documents that the engineering challenges anticipated by reviewers have established research-backed pathways, demonstrating readiness for Proof-of-Concept implementation phases without modifying or weakening any architectural element documented in Parts 1 through 8. The concept is published as defensive prior art under CC BY-NC-ND 4.0 , preventing future patent claims on the documented conceptual architectures while preserving open non-commercial use for evaluation, research, citation, and standards consideration by IETF, ISO/IEC JTC 1/SC 27, NIST Post-Quantum Cryptography programme, or any institution choosing to adopt all or any independent component of the architecture.
A line of impossibility results holds that a distributed ledger must either store a global state linear in the number of accounts or impose a near-linear rate of proof updates on its users; the most general, the revocable-proof-system lower bound of Christ and Bonneau, concludes there is "no useful trade-off." We show this impossibility does not bind the validity predicate Bitcoin actually uses—an artifact of one modelling choice, that validity is decided by a holder-maintained witness checked against a single mutating commitment. We define the spend-event validity predicate (SEVP) that a UTXO ledger uses instead, and prove it is not a revocable proof system: it instantiates no holder witnesses, so it lies outside the domain the lower bound quantifies over rather than within either branch of the dichotomy. The same exclusion holds for the related accumulator-update bounds. We are explicit about scope—stateless UTXO constructions that issue holder witnesses (accumulator- and vector-commitment designs) are correctly bound; the claim is that the UTXO model as Bitcoin implements it is not such a construction. This is not hypothetical: public Teranode benchmark evidence demonstrates one-million-transactions-per-second validation in a six-region BSV benchmark, while companion measurements report a 520-million-output active set with no holder-maintained witnesses. We then develop the supporting machinery. The binding resource is active-state maintenance in fast memory, not archival disk, and pruning bounds that state safely with a parameter-free reduction ratio of exactly T_yr/(d·T_block) (263× at retention depth d = 200), never altering the ledger and preserving the forensic record through self-interested retention plus archival nodes. For certification we give a construction and cost analysis for interval non-revocation, combining known authenticated-dictionary primitives so that interval validity is decided by a single point query with no trusted responder. Bounds are closed-form under stated assumptions; the one-million-TPS regime is demonstrated, the tens-of-millions a marked near-term projection.
Como a perícia blockchain organiza evidências para vítimas e advogados Golpes com criptomoedas costumam envolver promessas de investimento, falsas corretoras, pirâmides, phishing, malware, engenharia social e transferências para carteiras controladas por fraudadores. A boa notícia é que blockchains públicas deixam rastros verificáveis. A má notícia é que transformar esses rastros em prova útil exige método. O que a perícia blockchain consegue mapear Uma análise técnica pode identificar transações de entrada e saída, carteiras intermediárias, consolidação de valores, uso de bridges, mixers, exchanges, contratos de tokens e movimentações de stablecoins como USDT e USDC. O objetivo é reconstruir o caminho do ativo e apontar possíveis pontos de identificação.
George Sebastian, Neethu Tom, Saritha M S, Vimal Babu P
Existing cloud storage auditing mechanisms rely on third-party auditors (TPAs) or centralized verification, introducing single points of failure and trust assumptions. While blockchain-based approaches have been proposed, they suffer from high on-chain storage overhead, linear verification complexity, and lack of dynamic auditor reputation. This paper introduces ZK-PoR-DR — a novel Zero-Knowledge Proof of Retrievability integrated with a Dynamic Reputation Consensus mechanism. Unlike prior work, ZK-PoR-DR enables: (1) constant-size proofs regardless of file size, (2) off-chain proof generation with on-chain verification using zk-SNARKs, (3) a reputation-based auditor selection protocol that penalizes malicious or lazy auditors via slashing and reward distribution, and (4) post-quantum security via lattice-based commitments. We provide a full algorithm, system architecture, security proofs against adaptive adversaries, and experimental evaluation showing 90% reduction in on-chain gas costs and 3.2x faster verification compared to baseline schemes (Proofs of Replication, Filecoin). No prior work has combined these four properties simultaneously. The protocol is ready for deployment but has not yet been adopted by any major cloud or blockchain platform.
Federated Learning (FL) enables collaborative model training across decentralized data silos without raw data exchange, making it particularly attractive for privacy-sensitive domains like financial fraud detection. However, FL introduces critical vulnerabilities, notably the poisoning of global models through malicious client updates. Traditional defense mechanisms often rely on computationally expensive aggregation rules or complex anomaly detection. This paper introduces PoSFedFraud, a robust framework that integrates a Proof-of-Stake (PoS) economic layer directly into the federated aggregation process for financial fraud detection. By combining staking mechanisms with a dynamic reputation system, PoS-FedFraud economically disincentivizes adversarial behavior through automatic slashing and trust decay. We simulate a toy fraud detection scenario using a 29-dimensional feature space, demonstrating how the framework defends against norm-based gradient poisoning attacks. Our experimental results show that PoS-FedFraud successfully identifies and penalizes malicious actors—reducing their stake and trust upon detection—while maintaining global model convergence. The proposed method offers an incentive-compatible punitive layer that complements existing robust aggregation and anomaly-detection techniques for decentralized financial applications.
Modern military logistics and command systems face significant challenges in terms of security, transparency, and verifiability. Traditional centralized systems are vulnerable to single points of failure and malicious attacks, while the transmission of sensitive orders and supply manifests risks interception. This paper proposes a novel framework that leverages a permissioned blockchain to create an immutable and auditable ledger for both physical asset and information logistics. To address the critical need for confidentiality, our framework integrates Zero-Knowledge Proofs (ZKPs), enabling military units to verifiably confirm not just the receipt, but the correct content and understanding of commands or assets without revealing any operational data on-chain. This approach ensures end-to-end integrity, non-repudiation, and resistance to future quantum decryption threats while maintaining the highest level of data privacy. We present the system architecture, detail the interaction protocols, and demonstrate its effectiveness through practical use case scenarios, including the secure delivery of sensitive assets and commands.
Zuodong Wu, Dawei Zhang, Mianxiong Dong, Kaoru Ota
The General Data Protection Regulation (GDPR) aims to enable the free flow of personal data while enhancing individual control. Integrating privacy-preserving data retrieval methods can provide stronger protection for personal privacy. However, existing approaches lack compliance mechanisms aligned with the GDPR, making it difficult in practice to simultaneously satisfy the principles of lawfulness and data minimization, while also exhibiting clear limitations in both security and efficiency. To address these problems, we propose VOLE-PDRAA, an efficient privacy-preserving data retrieval protocol with anonymous authorization based on the Vector-OLE (VOLE). Specifically, VOLE-PDRAA constructs a VOLE-blinded identifier by integrating pseudorandom linear encoding with VOLE-derived correlation vectors, enabling rigorous anonymity guarantees during authorization. Building on this, the protocol incorporates a non-interactive zero-knowledge proof (NIZK) to achieve anonymous authorization for the data subject and to generate verifiable informed consent proofs, thereby meeting the principle of lawfulness. Meanwhile, the data controller can verify whether each retrieval request falls within the scope authorized by the data subject without learning any identifiable information, thus maintaining adherence to the data-minimization principle in a post-quantum environment. Furthermore, VOLE-PDRAA utilizes labeled private set intersection (labeled-PSI) to safeguard the confidentiality of identifiers and their associated records under post-quantum security conditions, while enabling large-scale batch retrieval. Our protocol takes a comprehensive security analysis within the Universal Composability (UC) framework. Experimental evaluation validates its superiority through comparison with state-of-the-art work.
As large language models (LLMs) grow in scale and are predominantly served from remote platforms, verifying faithful inference execution becomes critical (i.e., ensuring that a provider actually executes the advertised model and computational workload rather than a tampered or downsized variant). Zero-knowledge (ZK) LLM inference offers an appealing approach. It promises public verifiability and delivers per-instance guarantees of equational correctness by proving that an output is consistent with executing a public architecture under committed, private weights. Though, we show that it does not bind the effort expended to produce the output. In this paper, we formalize this overlooked effort gap and introduce the Hollow-LLM Attack, in which a dishonest provider retains the declared architecture and parameter count but embeds ghost weights whose algebraic structure collapses effective computation. These witnesses satisfy the verification circuit and yield valid proofs, even though the dishonest model owner, who serves as the prover, performs computation commensurate with a much smaller model than the declared public architecture. This creates a profitable equilibrium in which providers deliver provably correct outputs at small-model cost while overclaiming model size. Accordingly, we characterize concrete families of ghost weights that compose with standard transformer blocks and show that such hollow deployments substantially reduce serving cost with zero quality loss under the same verification circuit. These findings underscore that proof of correct inference is not proof of large-model execution and necessitate additional protections to bind correctness to verifiable computational work.
Austin Bennett, Preston Vander Vos, Duc V. Le, Mira Belenkiy
Decentralized Autonomous Organizations (DAOs) run protocol governance by letting token holders vote on proposals. The dominant rule, voting power proportional to wallet balance, concentrates control among a small number of large holders, fueling the token-control governance attacks that have already compromised real protocols. To counter this concentration, the community has turned to anti-plutocratic voting mechanisms such as Quadratic Voting (QV), which assign sublinear voting power per token with the goal of dampening the influence of large holders. We prove that no voting rule that derives power solely from wallet balance can succeed on a permissionless blockchain. Through a costed model of on-chain voting that captures realistic blockchain frictions -- including per-wallet splitting and voting costs, fixed setup costs, and minimum-balance requirements -- we show that whenever a wallet of any size yields nonzero voting power, a Sybil attacker who splits tokens across many wallets achieves total voting power that grows at least linearly in their token holdings. For concave rules actually proposed to dampen governance power -- those that are positive, increasing, and finite -- we show that the optimal strategy yields power that is asymptotically linear in token holdings, regardless of the cost scheme. Instantiating the model on real DAOs reveals attack costs orders of magnitude below the value at stake. Replaying the ten most recent finalized proposals of five major DAOs (ENS, Compound, Uniswap, Arbitrum, and ZKsync) under linear, quadratic, logarithmic, and power-($β= 0.25$) voting, we measure Sybil amplification factors between $1,172\times$ and $4,039\times$ under Quadratic Voting, and exceeding $229,000\times$ under steeper power rules.
On-chain crowdsourcing leverages blockchain's decentralization, transparency, and tamper-resistance to build trustworthy and verifiable Web3 crowdsourced services. However, existing decentralized reputation frameworks do not reconcile anonymity, reputation binding, and scalability. This paper demonstrates how on-chain crowdsourcing can simultaneously achieve these requirements under a trust-minimized model. We introduce DARTIC, a decentralized, anonymous, and scalable reputation-driven framework for crowdsourcing. DARTIC presents a dual-ledger system that enables requesters and workers to use distinct pseudonyms across interactions, ensuring unlinkability while maintaining accountability. To mitigate Sybil and reputation-reset attacks, we employ zkSNARK-based set membership proofs, cryptographically binding all user pseudonyms to a single access token without revealing the linkage. For scalability, we investigate two aggregation techniques that compress multiple proofs into a single succinct proof to minimize verification overhead. In addition, we design an automated, privacy-preserving reputation model that dynamically evaluates contributions across diverse crowdsourcing contexts. To demonstrate practicality, we instantiate and assess DARTIC in both crowdsensing and federated learning scenarios. Experimental results show that (i) individual proof generation for token spending completes in less than 3s, (ii) aggregation reduces the verification time of 1024 proofs from 8.7s to 0.96s, and (iii) zk-batching lowers gas costs by more than 100x compared to a pure Layer-1 deployment. These results demonstrate that anonymity, robust reputation binding, and scalability can be jointly achieved in fully decentralized crowdsourcing systems.
Autonomous trading agents can read markets and place orders faster than humans can supervise them. On a permissionless exchange, the usual answer is to give the agent a signing key. That is a large amount of trust. Anything that can steer the agent's prompt, memory, or context can also steer how the key is used. Recent attacks on Web3 agents show that this is not a hypothetical risk. Checking every order on the settlement chain gives a public record, but it also puts block latency in the order path. A continuous limit-order book cannot spend that. Bounded Authority puts the authority check where the order enters the venue. The user's wallet registers a risk policy on the settlement chain and authorizes a short-lived public session key. Agents propose orders. The off-chain sequencer accepts an order only after it verifies the session-key signature and runs the policy before any exchange state changes. For every accepted order, the sequencer signs the order, sequence number, risk-input hash, policy transition, match event, and append-only log leaf. Epoch roots are posted to settlement. Watchers can then challenge unauthorized orders, bad risk inputs, broken reserve or margin transitions, equivocation, or invalid matching with Merkle proofs and replayed execution. This gives agents a permissionless analogue of direct-market-access risk control. If a prompt-injected or memory-poisoned agent produces an order outside the user's policy, the result is slashable evidence against the venue rather than loss against the user. We give the protocol, threat model, accountability arguments, Solidity gas measurements for settlement challenges, and AWS measurements for the execution path. On two c7i.metal-24xl hosts, the directly measured kernel-TCP path returns signed Ed25519 acknowledgements for accepted resting orders in 124.17 microseconds median and 128.34 microseconds p99. A bounded-HMAC variant reduces this to 105.31 microseconds median, but it needs delayed key disclosure, threshold ingress, or an equivalent origin-accountability assumption.
The interplay between the advancements in quantum computing techniques and the adoption of the distributed learning approach pose an enormous challenge to conventional cryptographic authentication protocols. Traditional public key systems and federated learning (FL) authentication methods based on the hardness of solving the integer factorization problem or discrete logarithms become inefficient due to the existence of Shor’s algorithm. This paper gives a detailed review of the latest research efforts toward the development of efficient and secure privacy-preserving FL authentication methods based on Post-Quantum Cryptography (PQC). In particular, we present the state-of-the-art of three schemes, namely, PQBFL (Post-Quantum Blockchain-based Federated Learning), ZKFL-PQ (Zero-Knowledge Federated Learning with Lattice-Based Encryption), and Enhanced EAADE for vehicular networks. It is shown that lattice-based authentication is both computationally efficient (signing times of around 0.65 ms) and robust against quantum attacks. Our proposed hybrid scheme is comprised of ML-KEM for key encapsulation, ML-DSA-65 for digital signatures, and Zero-knowledge proof for gradient integrity verification. The empirical evaluation shows a reduction of 44.96% in the computation cost and 22.16% in the communication cost relative to the class.
Ms. Anushka Prasad Joshi, Prof. Sachin Bhosale, Dr. Shubhangi Gunjal, Dr. Anand Khatri
Abstract: With real estate markets digitalizing at a remarkable pace, there's a growing — and largely unmet — need for machine learning systems that can harness multi-institutional data without putting privacy or regulatory standing at risk. In this paper, we present a Privacy-Preserving Federated Learning (PP-FL) framework built specifically for digital real estate ecosystems. Our approach lets distributed stakeholders — property agencies, government land registries, financial institutions, and PropTech platforms — collaboratively train predictive models without ever pooling their raw transaction or personal records in one place. We've designed the system around three interlocking privacy layers: a DP-SGD-based differential privacy optimizer, a homomorphic encryption scheme for gradient transmission, and a secure multi-party computation protocol to safeguard intermediate model states. On top of that, a blockchain-backed audit mechanism using zero-knowledge proofs provides verifiable, regulator-friendly compliance. When we tested the framework on a simulated dataset of 2.4 million real estate transactions spanning multiple institutional clients, it achieved 91.8% prediction accuracy — just 2.4 percentage points behind a fully centralized model — while holding the differential privacy budget to ε = 0.5, cutting communication overhead by 65% relative to naive federated approaches, and satisfying both GDPR and RERA requirements. We believe these results make a strong case that high-utility, privacy-first collaborative learning is not just theoretically possible but practically deployable in today's real estate sector. Keywords: Federated Learning, Differential Privacy, Homomorphic Encryption, Real Estate Analytics, Secure Multi-Party Computation, Blockchain, GDPR Compliance, Data Sovereignty, PropTech, Zero-Knowledge Proofs.
A framework for reusable compliance attestation in regulated industries based on cryptographic primitives, vector commitments with selective-opening proofs, re-randomisable signatures, zero-knowledge succinct non-interactive arguments of knowledge, and cryptographic accumulators with succinct non-membership proofs, composed into a protocol structure adapted to the specific structure of multidimensional compliance state. We identify five gaps that separate the generic primitives from a deployable solution for compliance attestation: multidimensional state binding, temporal freshness without correlation, revocation under reuse, cross-issuer aggregation, and verifier predicate richness. We sketch the protocol structure that addresses these gaps, analyse its security and privacy properties, and discuss applications in age verification, right-to-work assurance, and continuous-compliance monitoring. Companion preprint to UK Patent Application GB2611280.5 filed at the UK Intellectual Property Office on 14 May 2026.
Cross-domain data sharing in decentralised environments faces persistent challenges related to confidentiality, auditability, and trust decentralisation, particularly when data transmission relies on centralised intermediaries or single proxy entities. To address these issues, this paper proposes a blockchain-enabled auditable data sharing scheme that integrates threshold secret sharing with non-interactive zero-knowledge proofs. In the proposed framework, the encrypted file fragments and secret key shares are decentralised across multiple blockchain nodes using threshold cryptography, preventing any single entity from reconstructing the encryption key or unilaterally performing ciphertext transformations. Zero-knowledge proofs are employed to publicly verify the correctness of the transmission and sharing operations without disclosing plaintexts, secret keys, or sensitive metadata, while the blockchain records verifiable proofs to support tamper-evident auditing. Security analysis shows that the scheme achieves confidentiality, collusion resistance, and verifiable correctness under standard cryptographic assumptions.Experimental evaluations indicate that the proposed scheme incurs acceptable computational and on-chain overhead, suggesting its feasibility in decentralised and cross-domain data sharing scenarios.
Sara Batal, Safae Belamfedel Alaoui, Said Hraoui, Mohammed Berrada
Cloud-delivered artificial intelligence (AI) is a technology that raises many problems related to access control, such as API misuse and difficulties in managing interactions with other AI models. Centralized access control offers insufficient oversight, is prone to major administrative or system failures, and has inherent weaknesses, for example central IAM dependencies and a single point of failure. We propose a decentralized access control system that leverages blockchain and smart contracts to automatically execute agreements between parties to authenticate and maintain a history of access to the attributes of the deployed AI models. Our architecture enables access control through immutable systems, which promises that the rules and access control records can never be modified. We used an experimental methodology to preserve a real system close to production and test its performance and behavior under high load. This methodology focuses on cross-chain access control security through RoleBased Access Control that will be used in our smart contracts. From the results obtained in the previous test phase, when load was increased and RBAC was implemented in the smart contracts, we were able to measure the trade-offs between gas cost and access latency within the computational resource. Resource consumption and architecture stability were measured in milliseconds as the number of processed transactions increased. Our architecture is still applicable to cloud-based AI services. Although we gained a high gas cost from the test results due to integrating RBAC into the Ethereum smart contract, we proved that access control is guaranteed in the cloud.