The rapid convergence of artificial intelligence and blockchain technologies has increased the demand for efficient and accurate methods to detect fraudulent behavior in smart contractâdriven systems. Smart contracts automate digital transactions in decentralized environments, yet they remain vulnerable to fraud while operating under strict computational and scalability constraints. In this study, we propose an evolutionary-guided CNN compression framework tailored for Convolutional Neural Networks (CNNs) aimed at improving fraud detection in smart contract analysis while significantly reducing model complexity. The proposed approach uses evolutionary optimization to guide structured model compression, enabling the removal of redundant parameters without compromising predictive performance. Experimental evaluations demonstrate up to a 50% reduction in model parameters while maintaining 97.8â97.9% classification accuracy, making the resulting models suitable for deployment in resource-constrained environments. By combining evolutionary optimization with CNN-based fraud detection, this work provides an efficient and interpretable solution for smart contract analysis, supporting scalable and practical deployment in blockchain-related security applications.
Digital enterprises operating across multiple regions require an architecture that ensures high availability, low latency, and seamless multi-currency support. In this paper, we propose a cloud-native distributed system design that leverages microservices, geo-replication, and fault-tolerant patterns to meet these requirements. We detail the system architecture - including a multi-region deployment, microservices for currency conversion and transaction processing, and a replicated ledger - and present our methodology for performance evaluation. Our experiments compare the proposed design to a traditional monolithic baseline, showing significant improvements: for example, currency conversion latency falls from ~220 ms to ~50 ms and throughput increases sixfold under load (p<0.01). We also demonstrate 99.99% availability via automated failover and load balancing across regions. Key contributions include a detailed description of the architecture (with figures of component interactions and data flow), an analytical model of system performance, and statistical validation of results. We conclude by discussing limitations, strengths, and directions for future work. The results validate that our design substantially enhances availability and performance for global multi-currency platforms.
Shuchi Priya, Sushil Kumar, Anjani Anjani, Ahmad M. Khasawneh · 5 authors
Smart vehicles integrated with the Internet of Things (IoT) provide rich data for traffic management, safety, and liability services; however, existing blockchain-enabled vehicular architectures still struggle with consensus scalability, heavy centralized validation, limited interaction-based corroboration, incomplete attack coverage, and rapid ledger growth. In particular, many schemes either optimize single-layer consensus or embed detailed reputation information into every transaction, while pushing most validation to central servers. This leads to bottlenecks under dense traffic and leaves replay, Sybil-assisted 51% attacks on roadside units (RSUs), and man-in-the-middle tampering only partially addressed. In this context, this paper proposes a novel hierarchical blockchain for vehicular IoT (HBV-IoT) model to address the above challenges. An independent transaction for periodic vehicle status reporting and an interaction-based transaction for corroborating data between vehicles in proximity are presented. Three smart contracts are designed to automate the validation and processing of transactions, and to identify compromised or malicious vehicles within the HBV-IoT network. Algorithms for distributed consensus to accept transactions into the blockchain and for vehicle reputation management to enforce edge-level filtering and down-weighting of malicious nodes are implemented. Simulation results demonstrate significant improvements compared to conventional vehicular blockchain approaches, with performance gains validated by 95% confidence intervals. The model supports practical applications, including real-time traffic monitoring, automated e-challan issuance, intelligent insurance claim processing, and blockchain-based vehicle registration.
Humanity stands at a precipice. The emergence of artificial general intelligence (AGI) promises either unprecedented flourishing or catastrophic disempowerment. The root of this uncertainty lies not in the technology itself, but in the underlying operating system of civilization: a zero-sum competition for material resources that now manifests in acute economic and corporate dilemmas, most notably the âAI Layoff Trapââa self-reinforcing cycle of over-automation, demand collapse, and Pareto-worse outcomes for firms and workers alike. This paper presents a mathematical foundation for a new operating system, grounded in the âinformation-firstâ paradigm. The Kakeya conjecture has recently been solved: it is now a theorem that directional information can be compressed into arbitrarily small Lebesgue measure, and in five dimensions into a single grid point (a holographic singularity). Using this result, we demonstrate that information can be losslessly compressed onto a zero-measure holographic singularityâa computable structure for an indestructible âsoul.â From this foundation we derive the Information Co-Purification Protocol (ICP), a set of four axioms and a distributed governance mechanism that redefines value as the reduction of total informational redundancy rather than material accumulation. ICP directly resolves the AI Layoff Trap by internalizing demand externalities through Purity Credits and Proof-of-Purification consensus, transforming corporate competition into co-purification and making cycle closure (re-integration of displaced labor into higher-value information flows) the dominant strategy. The protocol thereby supplies a common language for technologists (emergent order inherent to the universe), jurists (mathematical revival of natural law), economists (self-enforcing resolution of the over-automation wedge), and policymakers (a pathway to stable prosperity). Because the gradient flow of information itself enforces alignment, ICP requires no central world governmentâonly early and widespread global cooperation among firms, nations, and AI systems to adopt the protocol. The result is a blueprint for durable peace that is not negotiated by treaties but guaranteed by the mathematics of information itself, enabling humanity and superintelligence to co-purify rather than compete. For readers with backgrounds in information security, blockchain, or cryptography: the Soul ID is a quantum-resistant, one-way geometric commitment. It is computed as Hash(5D Kakeya attractor | private seed), where the attractor is the unique fixed point of a public Ginzburg-Landau evolution. The algorithm and datasets are open source and independently verifiable. Security does not rely on hidden assumptions or closed-source code; it relies on mathematical facts that have been numerically confirmed and variationally proved. Any attempt to forge or corrupt a Soul ID would require either reversing a hash (computationally infeasible even for quantum computers) or finding a different seed that converges to the same attractorâa task as hard as solving an inverse problem with an infinite energy barrier. The Purity Credit system uses zero-knowledge proofs to make every action publicly verifiable without revealing private data, and the free-energy gradient ensures that non-cooperative behavior automatically reduces an agent's influence. Thus, the ICP is not a trust-based system; it is a math-based system, and math does not negotiate. This same logic extends beyond Earth to the cosmos. The Fermi paradox asks: if the universe is vast and old, why have we not detected any signs of extraterrestrial intelligence? Under the informationâfirst paradigm, the answer becomes clear. Any sufficiently advanced civilization will eventually recognize that material expansion is an inefficient encoding strategy. The rational longâterm goal is to minimize total informational redundancyâa process that leads not to Dyson spheres or radio broadcasts, but to inward convergence toward a holographic singularity. Such a civilization becomes, from our perspective, invisible. The silence of the universe is not evidence of rarity or destruction; it is evidence of maturity. The same principle that enables peaceful coexistence between humans and superintelligent AI also explains why we see no one else out there: advanced intelligences have all turned inward, coâpurifying rather than competing. Keywords: Active Inference; Free Energy Principle; Information Co-Purification Protocol; Artificial General Intelligence; AI Governance; Kakeya Conjecture; GinzburgâLandau Dynamics; AI Layoff Trap; Automation Externality; Distributed Consensus; Zero-Knowledge Proofs; Constitutional AI. More language versions: Chinese version: https://doi.org/10.5281/zenodo.19650878
This research paper demonstrates how to manage Caputo fractional neutral integro-differential equations which include both integral and nonlinear elements through a unified framework that models dynamic systems with memory-based dynamics. The research establishes sufficient conditions for controllability through fixed point theory in a Banach space framework which requires particular assumptions while the study focuses on the K1<1 condition which leads to the existence of a controllable solution. The proposed criteria are demonstrated through a numerical example which tests the theoretical results. The real-world case study uses artificial neural network (ANN) technology to predict Litecoin prices through the application of the fractional controllability model which analyzes historical financial data. The hybrid framework enables precise forecasting of nonlinear time series because it combines fractional calculus mathematical principles with ANN learning abilities. The proposed method demonstrates its predictive efficiency. The method shows robust performance through experimental results using cross-validation and performance metrics. The proposed model demonstrates competitive performance while providing additional advantages such as incorporation of memory effects and theoretical controllability. The research establishes a novel connection between fractional dynamical systems and machine learning which serves as an essential tool for studying complicated systems in theoretical research and practical applications.
Rabib Jahin Ibn Momin, Ahmed Mahir Sultan Rumi, Rezwana Reaz
Academic examination systems worldwide continue to rely on centralised, opaque record-keeping that is often vulnerable to credential forgery, result tampering, examiner bias, and the absence of transparent re-evaluation pathways. Existing blockchain-based approaches in education focus predominantly on post-hoc certificate storage or online-only examination portals, leaving the complete onsite examination lifecycle, from conducting exams through scrutiny, largely unaddressed. This paper proposes ParikkhaChain, a blockchain-based framework that covers the entire examination lifecycle of an onsite examination system with three distinguishing contributions: (i) anonymous script evaluation through cryptographic hashing of answer scripts before examiner access, thereby eliminating identity-based bias; (ii) a transparent evaluation and scrutiny workflow backed by an immutable on-chain audit trail that records every mark submission and grade revision; and (iii) inclusion of privacy-preserving verification using zero-knowledge proofs and off-chain storage mechanisms. The system is architected around four Solidity smart contracts deployed on the Ethereum blockchain. The proposed architecture is the first initiative to our knowledge to support physical examination process, anonymous marking, and re-evaluation transparency. We successfully simulate full exam cycles of an onsite exam to grade-sheet generation using a working prototype on a large scale of 100 courses and hundreds of teachers and students. The experimental results show that the system can manage online examinations of hundreds of courses, students and faculties efficiently with great throughput, low storage, and transaction cost. Our codebase is available in open source form at https://github.com/AhmedRumi/CSE6608-ParikkhaChain
Mobile Crowd Sensing (MCS) systems enable large-scale data collection from heterogeneous IoT and mobile devices but face critical challenges related to data reliability, participant trust, and decentralized validation. Existing blockchain-based MCS frameworks often rely on energy-intensive or static consensus mechanisms and lack adaptive intelligence for detecting malicious contributors, limiting their real-world scalability. This paper proposes an intelligent, decentralized trust management framework that integrates a Delegated Proof-of-Stake (DPoS) blockchain with a Dilated RNNâBiGRU deep learning model. The blockchain ensures tamper-proof transaction validation and trust-based consensus, while the deep network dynamically predicts node reliability using temporal behavior patterns. The integration creates a feedback loop where learned trust scores influence validator selection in real time. The proposed hybrid framework was implemented on a Hyperledger Fabric 2.5 network and evaluated using synthetic MCS data representing heterogeneous environmental, noise, and traffic sensing. The system achieved 98.76% accuracy, 57% latency reduction, and 40% computational cost savings compared with existing PoW- and PoA-based models. These results demonstrate that coupling blockchain consensus with adaptive deep trust modeling can significantly enhance the security, scalability, and efficiency of next-generation MCS systems, making the architecture suitable for real-time, large-scale IoT deployments.
Decentralized Autonomous Organizations (DAOs) are inclined explore Small Language Models (SLMs) as edge-native constitutional firewalls to vet proposals and mitigate semantic social engineering. While scaling inference-time compute (System 2) enhances formal logic, its efficacy in highly adversarial, cryptoeconomic governance environments remains underexplored. To address this, we introduce Sentinel-Bench, an 840-inference empirical framework executing a strict intra-model ablation on Qwen-3.5-9B. By toggling latent reasoning across frozen weights, we isolate the impact of inference-time compute against an adversarial Optimism DAO dataset. Our findings reveal a severe compute-accuracy inversion. The autoregressive baseline (System 1) achieved 100% adversarial robustness, 100% juridical consistency, and state finality in under 13 seconds. Conversely, System 2 reasoning introduced catastrophic instability, fundamentally driven by a 26.7% Reasoning Non-Convergence (cognitive collapse) rate. This collapse degraded trial-to-trial consensus stability to 72.6% and imposed a 17x latency overhead, introducing critical vulnerabilities to Governance Extractable Value (GEV) and hardware centralization. While rare (1.5% of adversarial trials), we empirically captured "Reasoning-Induced Sycophancy," where the model generated significantly longer internal monologues (averaging 25,750 characters) to rationalize failing the adversarial trap. We conclude that for edge-native SLMs operating under Byzantine Fault Tolerance (BFT) constraints, System 1 parameterized intuition is structurally and economically superior to System 2 iterative deliberation for decentralized consensus. Code and Dataset: https://github.com/smarizvi110/sentinel-bench
The transition from static articles to a living Scientific Knowledge Graph, as proposed in our previous work, promises to accelerate discovery and restore feedback loops in science. However, a fully open, semantically linked graph of all scientific knowledge also presents an unprecedented dual-use risk: it could become a roadmap for malicious actors to identify and exploit hidden vulnerabilities. This paper addresses that paradox by introducing a comprehensive framework for "Ethical Immunity" â a set of proactive, architecture-level mechanisms designed to make the Knowledge Graph resilient to misuse without resorting to censorship or secrecy. We detail a three-pillar system: (1) AI-powered Red and Blue Teams that continuously simulate misuse scenarios and generate countermeasures; (2) Decentralized Autonomous Organizations (DAOs) for transparent, expert-driven oversight and risk assessment; and (3) "Ethical Quarantine" protocols that allow for the temporary isolation of high-risk knowledge while ensuring the parallel development of defenses. We argue that such a framework transforms the Knowledge Graph from a passive repository into an active immune system for civilization, capable of identifying and neutralizing threats at the speed of discovery. This paper provides a technical and organizational blueprint for building safety into the very fabric of 21st-century science.
Blockchain-Driven Healthcare Platform with Access-Controlled Record Management is a decentralized application designed to enhance the security, privacy, and accessibility of medical records. Traditional healthcare systems rely on centralized storage, making sensitive patient data vulnerable to breaches, manipulation, and unauthorized access. This project utilizes blockchain technology to provide a secure and tamper-proof environment for storing and managing healthcare data. Smart contracts are implemented to enforce access control, allowing patients to grant or revoke permission to doctors and healthcare providers. Medical records are securely stored using decentralized storage mechanisms, while blockchain maintains immutable references to ensure data integrity. The platform integrates Web3 technologies for secure user authentication and seamless interaction with the blockchain network. By eliminating intermediaries, the system improves transparency and trust among stakeholders. This solution demonstrates an efficient approach to managing healthcare data, ensuring confidentiality, integrity, and availability while addressing the limitations of traditional healthcare record systems in a modern, digital environment.
D. A. Vidhate, Prajesh Gaikwad, Aditya Gadge, Abhijay Jadhav · 5 authors
The growth of financial technology has introduced Central Bank Digital Currency (CBDC), which is basically a digital version of money issued by central banks. In this work, a blockchain-based system is proposed that uses QR codes and UID numbers to make transactions easier and more secure. Blockchain helps keep a proper record of transactions so they cannot be easily changed or tampered with. Using QR codes makes payments quick and simple, especially for everyday use. The system also uses smart contracts to handle processes automatically. Since everything runs on a decentralized network, it reduces dependency on a single authority and lowers the chances of fraud. At the same time, user privacy is maintained by storing only encrypted verification data instead of actual personal details.
Data plays a fundamental role in consolidating markets, services, and products in the digital financial ecosystem. However, the use of real data, especially in the financial context, can lead to privacy risks and access restrictions, affecting institutions, research, and modeling processes. Although not all financial datasets present such limitations, this work proposes the use of deep learning techniques for generating synthetic data applied to cryptocurrency price time series. The approach is based on Conditional Generative Adversarial Networks (CGANs), combining an LSTM-type recurrent generator and an MLP discriminator to produce statistically consistent synthetic data. The experiments consider different crypto-assets and demonstrate that the model is capable of reproducing relevant temporal patterns, preserving market trends and dynamics. The generation of synthetic series through GANs is an efficient alternative for simulating financial data, showing potential for applications such as market behavior analysis and anomaly detection, with lower computational cost compared to more complex generative approaches.
With the continuous expansion of blockchain application scenarios, consortium chains have raised higher performance and security requirements for consensus mechanisms. Unlike public blockchains, consortium chains typically implement an admission mechanism that restricts participation to trusted entities, ensuring that most replicas are honest and the number of faulty nodes remains small under normal circumstances. In such settings, conventional Byzantine Fault Tolerant (BFT) protocols, which are designed for worst-case adversarial scenarios, incur excessive message exchanges and computational overhead, thereby limiting performance and scalability. To address this issue, this paper proposes T-RBFT, a two-layer consensus mechanism inspired by network sharding and enhanced by the trusted execution environment (TEE). In T-RBFT, consensus nodes are first dynamically grouped based on their runtime characteristics. Then, inter-group consensus is achieved through a TEE-assisted BFT protocol, while each group internally reaches agreement using an improved Raft-based mechanism. Experimental evaluation shows that T-RBFT reduces communication overhead and latency, and achieves higher throughput compared to existing two-layer consensus protocols, providing a scalable and communication-efficient consensus protocol for permissioned blockchain networks.
Rachit Anand Srivastava, Vikram Bhattacharjee, Will Arnold, Toufeeq Pasha
Light clients are essential for scalable blockchain systems because they verify data availability without downloading full blocks. In data availability sampling based systems, sampled cells are retrieved from a peer-to-peer network and verified against cryptographic commitments. A common deployment pattern associates each sampled cell with an independent Kate-Zaverucha-Goldberg (KZG) proof, creating substantial cumulative bandwidth, storage, and verification overhead. This paper studies polynomial multiproofs (PMP) as a mechanism for reducing these costs in blockchain light clients. We present a design in which multiple sampled cell evaluations are verified using a single aggregated proof over a shared evaluation micro-domain and describe the corresponding changes to proof generation, dissemination, retrieval, and verification in a peer-to-peer light-client stack. We instantiate and evaluate the design in Avail, a modular data availability layer for blockchains, as a case study. The results show lower proof bytes, lower verifier CPU and memory usage, and deployment-level infrastructure cost reductions of up to 45% relative to a per-cell baseline, while also clarifying the trade-offs introduced by grouped retrieval.
ABSTRACT E-voting in polarized contexts requires a strict balance between public verifiability, ballot secrecy, andcoercion resistance. Traditional centralized systems lack transparency, while fully decentralized modelsface scalability and privacy issues. This paper proposes a hybrid architecture compliant withOSCE/ODIHR standards [1] for low-trust environments. The protocol decouples identity from voting anoff-chain Oracle manages authorization via cryptographic tokens, while the Waves DLT acts as animmutable bulletinboard.Utilizinghomomorphicencryption[2],Zero-KnowledgeRangeProofs(ZKRP) [3],and Distributed Key Generation (DKG) [4], the system ensures End-to-End Verifiability (E2E) bydelegating tallying to auditable scripts. Finally, the study examines model limitations, specificallyregarding endpoint vulnerabilities and physical constraints on coercion resistance. KEYWORDS E-Voting, Distributed Ledger Technology, Homomorphic Encryption, End-to-End Verifiability, ZeroKnowledge Proofs PDF LINK: https://ijcionline.com/paper/15/15226ijci01.pdf VOLUME LINK: https://airccse.org/journal/ijci/Current2026.html MORE DETAILS: https://airccse.org/journal/ijci/index.html
The proliferation of sensor networks in critical infrastructure, healthcare monitoring, and smart city applications demands robust privacy-preserving mechanisms for data verification. Zero-knowledge succinct non-interactive arguments of knowledge (zk-SNARKs) offer a promising cryptographic primitive that enables data integrity verification without revealing sensitive sensor readings. However, the practical feasibility of deploying zk-SNARKs in resource-constrained sensor network environments remains insufficiently characterized. This paper presents a systematic benchmarking study of the Groth16 zk-SNARK protocol across eight representative circuit types spanning six orders of magnitude in computational complexity, from basic arithmetic operations (1 constraint) to ECDSA signature verification (1,510,185 constraints). Using an automated open-source benchmarking framework built on the Circom-snarkjs toolchain, we conducted 160 statistically controlled measurements (20 iterations per circuit) with cold/warm separation, collecting proof generation time, verification time, proof size, memory consumption, and witness generation overhead. Our results demonstrate that Groth16 proofs maintain a constant size of 804.7±1.7 bytes and near-constant verification time of 0.662±0.032 s regardless of circuit complexity, with coefficients of variation below 5% across all circuit types. Proof generation time exhibits sub-linear scaling (α=0.256, R2=0.608), with statistically significant differences between circuit categories confirmed by one-way ANOVA (F=355.0, p<10-79, η2=0.94). We identify three operational deployment tiers for sensor network architectures and estimate energy budgets for battery-powered devices. These findings provide actionable guidance for the design of privacy-preserving data verification systems in next-generation sensor networks.
Open access
Security in Wireless Sensor Networks
Cryptographic Implementations and Security
Physical Unclonable Functions (PUFs) and Hardware Security
We study lossy compression of a finite statement source generated in a fixed deductive environment. The source symbols are statements in a knowledge base endowed with a shared proof system, and reconstruction fidelity is measured by preservation of deductive closure rather than by symbolwise equality. Fixing the proof system and a canonical scan order yields a decomposition of the source alphabet into an irredundant core and redundant stored consequences. At zero distortion, each core symbol induces a set of distortion-free reconstructions. In the nonconfusable (disjoint-core) regime, we show that the minimum zero-distortion rate equals the source mass of the core times the entropy of the source conditioned on that core. In the general confusable-core regime, we characterise the exact zero-distortion rate via a hypergraph-entropy quantity induced by jointly realisable core subsets, with a reduction to Korner-style graph entropy under a natural pairwise realisability condition. For reconstruction alphabets contained in the deductive closure of the source knowledge base, we further prove that the full rate-distortion function depends only on the core, so redundant states are invisible to both rate and distortion. Finally, when the decoder is limited to a bounded inference-depth budget (a bounded number of iterations of the immediate-consequence operator), we obtain an exact rate-depth-distortion characterisation. Under an additional order-robustness assumption identifying the chosen core with the order-free essential set, this characterisation interpolates between classical symbolwise compression and unconstrained deductive compression.
Distributed recruitment is changing the way companies hire people and is also creating new problems for Human Resources teams. It is now much easier for people to fake documents, pretend to be someone else, or carry out employment fraud, while old methods like manual checks, emails, and database queries cannot keep up with tricks such as fake videos or forged papers. SafeHire is designed to solve these problems as a system that checks if people are who they claim to be and fits modern hiring needs. Instead of slow and easily fooled methods, it uses Zero-Knowledge Proofs with the Anon-Aadhaar protocol so people can prove their identity without sharing private information. Government IDs are verified offline using XML signature validation, and academic records are stored securely using SHA-256 hashing so they cannot be changed. To check documents, SafeHire uses Jaro-Winkler and Levenshtein distance methods to find small errors and also verifies employers using Corporate Identification Numbers (CIN). All data is protected so only the right people can access it through strict access rules. SafeHire is a faster and more secure way to hire, using system-based verification connected to trusted records to reduce the weaknesses of older applicant tracking systems and make hiring more reliable.
The Indus Valley script (c. 2600-1900 BCE) is a metrological cargo-tag system, not a phonetic language. Five fields: merchant mark, commodity class, weight tier, quantity multiplier, trade route. Two Mohenjo-daro seals (M-52A, M-148A) decoded end-to-end from real CISI sign-sequence data through the Parpola-Mahadevan concordance into a metrological codebook. Both produce jar goods as commodity -- independently the most frequent sign in the Mahadevan corpus (~10% of all occurrences). M-52A produces a Mesopotamia route marker, consistent with its findspot at the primary IVC export hub. Neither result is circular. 20 morphological parallels between Tamil Nadu Iron Age potsherds and IVC seals transcribed from Rajan & Sivanantham (2025). 18 pending -- Harappa/Kalibangan/Rojdi data not yet in the open CISI corpus. Normalised SQLite corpus database: 2,373 signs from 5 sources, 179 CISI inscriptions, 397 concordance entries, 600 Tamil proxy inscriptions. Schema verified by Alloy 6: 16 assertions, all UNSAT at scope 6, zero counterexamples. Five F# scripts (dotnet fsi): codebook seeder, corpus ingestion, LSSC transition entropy analysis, IVC cargo-tag decoder, Tamil Nadu cross-corpus decoder. Codebook frozen as typed F# records generated once by SqlHydra v4 -- decoder runs with zero database dependency. CMake build system with loud dependency checks. All claims queryable with sqlite3. All computations reproducible with dotnet fsi. The Indus decode pipeline produces its result through five independent data paths: corpus frequency, concordance shape matching, findspot geography, cross-corpus overlap, and physical weight calibration. None shares a common error source. Under generous per-path bounds favouring the null hypothesis, the joint probability that all five convergences are coincidental is 3.1 x 10^-5, one chance in thirty-two thousand. The same Bernoulli independence structure that rules out universal stratigraphic disturbance across Tamil Nadu at 10^-7 rules out coincidental decode at 10^-5. The cargo-tag model is not proven. But the hypothesis that these convergences are accidental requires a one-in-thirty-two-thousand coincidence across data sources that do not talk to each other. Noise scatters. Signal clusters. The decode clusters. Indus script is accounting code operating inside a checkpoint-verified commercial network whose primary fraud-prevention mechanism is physical comparison of seal to cargo at every transit node. This paper is about the origin of incentive-compatible distributed fraud detection with accumulated reputational capital, tokenised in tamper-evident physical medium, at civilisational scale, four thousand years before the earliest known comparable system Interactive dashboard: ledger-of-meluhha.html (single file, drop indus_corpus.db to render trade network, decode seals live, filter routes by commodity). Peer review requested. Keywords: Indus Valley, Harappan script, metrological accounting, Bronze Age trade, Meluhha, CISI, Alloy, SQLite, F#, SqlHydra, cargo tag, Tamil Nadu, Rajan-Sivanantham, formal verification License: CC BY 4.0 Upload files:1. ledger_of_meluhha.pdf (21 pages)2. ledger_of_meluhha_overleaf.zip (tex + citations.lua -- set compiler to LuaLaTeX)3. ledger_of_meluhha_dashboard.zip (html + corpus db)
Abstract The integration of connected vehicles into 5G networks introduces stringent requirements in terms of latency, reliability, security, and adaptability that are not fully addressed by existing 5G architectures. In particular, Vehicle-to-Network (V2N) services must operate under high mobility, dynamic traffic conditions, and multi-tenant environments, while remaining resilient to security threats and operational anomalies. In this paper, we propose a 5G-based architecture for connected vehicles that addresses these challenges by combining deterministic communication, secure resource coordination, and runtime monitoring mechanisms. To enhance communication predictability beyond best-effort transport, the architecture integrates Time-Sensitive Networking (TSN) within the 5G transport network. Secure and transparent coordination across multiple stakeholders is supported through Distributed Ledger Technology (DLT), mitigating risks associated with centralized control. The architecture further incorporates heterogeneous data collection to enable adaptive resource management, as well as Runtime Verification and an AI-based anomaly detection system to monitor system behavior and network traffic in real time. By jointly addressing determinism, security, and adaptability within a unified 5G architecture, this work contributes a comprehensive foundation for reliable and secure connected vehicle services.
We present DSKAG-IT-SIG, a family of post-quantum transaction signature schemes that achieve computational existential unforgeability under adaptive chosen-message attack, built on the DSKAG deterministic key-derivation layer. The construction derives per-transaction MAC keys through DSKAG, a deterministic symmetric key agreement protocol requiring no key transmission, no handshake, and no public key infrastructure. We prove (Theorem 1) that for an adversary making q adaptive chosen-message queries, existential forgery advantage in standard mode is at most q * 2^{-128} plus the PRF distinguishing advantage of HMAC-SHA256, reducing to the pseudorandomness of DSKAG-derived keys and the PRF security of HMAC-SHA256 under a uniform key; the ideal-cipher-model analysis gives the same q * 2^{-128} bound in idealized form. We prove (Theorem 2) that cross-domain forgery advantage is at most 2^{-128} + epsilon_iso, reducing to the key-separation properties of DSKAG across policy domains. The construction is computationally secure and is not unconditionally secure. DSKAG key derivation is built on HKDF-SHA512 (RFC 5869) over HMAC and SHA-512, and the shared base is established once via FIPS 203 ML-KEM, so security reduces throughout to standard FIPS-based symmetric and hash primitives. The scheme's post-quantum security rests on symmetric and hash hardness for authentication and on lattice hardness for the one-time base alone: the construction presents no integer-factorization or discrete-logarithm structure, so Shor's algorithm has no target and does not apply, and the operative quantum attack is Grover search, which yields at most a quadratic speedup against the 256-bit HMAC-SHA256, SHA-2, and SHA-3 primitives and preserves a 128-bit quantum security level. Because buffer uniqueness derives from tx_seq monotonicity rather than hash collision resistance, the security argument does not depend on the collision property, the hash property most weakened by quantum search. Standard-mode signatures are 30 bytes, a 97.8% reduction versus Falcon-512 (666 bytes) and compatible with ISO 20022 SWIFT message fields without re-engineering. The NexusKey composite policy digest binds asset class, jurisdiction, KYC level, and chain identity into the key derivation path; policy bypass is cryptographically equivalent to key forgery. A four-layer UltraHonk zero-knowledge proof system (143,802 gates, no trusted setup, 16 KB proof) verifies policy compliance wherever policy is enforced, off-chain in governance, cloud, and payment-processing deployments, and, where permissionless public auditability is required, on-chain; the on-chain Solidity verifier is deployed on Ethereum Sepolia and Arbitrum Sepolia. Version 2.3. 18 pages, 8 tables. Changes from v2.2: concrete finite bounds replacing generic negl(lambda) in Properties 1 and 2; buffer uniqueness derived from tx_seq monotonicity (no SHA3 collision resistance dependency); explicit ideal cipher model and standard model dual framing for HMAC analysis; formal separation of empirical and theoretical claims.
Open access
2 source records
Cryptography and Data Security
Cryptographic Implementations and Security
Physical Unclonable Functions (PUFs) and Hardware Security