On August 28, 2026, Google, Microsoft, Anthropic, OpenAI, and 100 other companies signed an open letter warning of a large-scale AI attack. AI has created systemic risks in the digital world, but the physical world has no defense mechanism. This paper defines the Physical Ledger—a physical world namespace rooted in the Cui coordinate. The Physical Ledger DNS is not a copy of the domain name system; it is an object-addressing protocol for the physical world: every object (shelf position, robot, door, vehicle, starship) is assigned a unique Cui coordinate address. This paper presents a draft protocol for the Physical Ledger DNS, a catalog of 108 problems, the genesis valuation of $100,000,000, and a reward distribution scheme. It proposes the §13 security mechanism (Proof-of-Problem): a distributed firewall for the Physical Ledger DNS, powered by the 108 problems. The more solvers participate, the thicker the firewall. AI can attack digital protocols, but it cannot solve problems—because solving requires understanding the coordinate origin itself. The genesis valuation of the Cui-attribute Shell is defined as US$100,000,000, anchored at 2026-08-27. The appendix includes the Cui-coordinate naming rights and the passphrase lock (recognition of 1/7/8 for entry).
Henry Ohiani Ohize, Adeiza James Onumanyi, Lukman Adewale Ajao, Buhari Ugbede Umar · 9 authors
Despite significant advances in electronic voting technologies, voter accreditation in many electoral systems remains vulnerable to identity fraud, database tampering, equipment failure, and centralized security breaches. Existing accreditation solutions often rely on single-modal biometric authentication and centralized architectures, limiting their robustness, transparency, and public trust. This paper proposes a Blockchain-based Bimodal Voter Accreditation System (Block-BVAS), together with a practical framework for its deployment in electronic voting systems. The proposed system integrates multimodal biometric authentication using facial and fingerprint recognition with a private Ethereum blockchain and conventional cryptographic mechanisms to provide secure, tamper-resistant, and auditable voter accreditation to provide secure, decentralized, and tamper-resistant voter accreditation. A Raspberry Pi 5 serves as the embedded processing platform, demonstrating the feasibility of implementing the framework on cost-effective hardware. By combining distributed-ledger technology with encrypted biometric verification, the proposed architecture enhances the integrity, confidentiality, and immutability of election-related records while addressing limitations associated with single-factor authentication and conventional centralized record management. Experimental evaluation of the biometric authentication module performed effectively, with fingerprint recognition achieving an average authentication accuracy (AA) of 97.8% and facial recognition averaging 95.1%. The blockchain storage overhead (BSO) displayed a near-linear growth pattern relative to the number of transactions, consistent with theoretical expectations for blockchain architectures. Reliability analysis indicated system uptime exceeding 95%, with only minimal operational failures recorded during the test period. This blockchain implementation further demonstrated reliable transaction processing and secure record management, indicating the effectiveness of the proposed Block-BVAS in enhancing the security, transparency, and trustworthiness of electronic voter accreditation.
Open access
Internet Traffic Analysis and Secure E-voting
Advanced Steganography and Watermarking Techniques
The classical taxonomy of remote voting security requirements is organized into two levels: basic and counter-attack requirements. This classification has remained largely unchanged over a decade, even through scheme innovations such as verifiable re-voting, tally-hiding, and post-quantum protocols. However, this stability does not survive the new threat model of adversarial artificial intelligence (AI). A security requirement is considered promoted when AI raises the adversarial grade at which it must be defended above its original assumption. In this paper, we introduce requirement promotion as a framework for re-evaluating the classical taxonomy, contending that the destabilizing factor is not new cryptography but the emergence of a new adversary. We analyze five requirements under both AI-amplified threats and AI-enabled defenses, demonstrating that promotion fires selectively. Basic requirements such as privacy, fairness, and eligibility undergo tier promotion to counter-attack grade, driven by machine-learning-based deanonymization, pre-tally outcome inference, and synthetic-identity fraud. Incoercibility undergoes supra-tier promotion, surpassing the existing counter-attack toolkit, as deepfake-generated coercion evidence compromises the fake-credential assumptions of classical coercion-resistant schemes. Verifiability, where AI-based defense is robust, resists headline promotion but acquires a new sub-requirement at its seam with software independence: verifying the opaque machine-learning components in the audit pipeline itself. We then propose a reference architecture, integrating existing primitives such as lattice-based zero-knowledge proofs, deniable re-voting, statistical election forensics, and time-lock decryption into a layered design that addresses the promoted requirements, with explicit analysis of residual gaps.
Mrs. A. Asrin Mahmootha, B. Aysha Banu, Mohammed Muhajir S, Mohamed Anas A · 5 authors
Voting is a central component of a country’s political life cycle. Privacy, authentication, and integrity of citizens’ votes are essential requirements of any electronic voting programme. To address these concerns, this paper proposes a hybrid e-voting system that integrates personal and public blockchain with a machine learning–based intrusion detection mechanism. The personal blockchain governs voter registration and vote casting, while the public blockchain stores the Merkle root hash for result integrity verification. An ML-based intrusion detection system monitors voting data centres and e-voting stations for anomalous behaviour. Homomorphic encryption and zero-knowledge proofs preserve voter anonymity. Experimental evaluation demonstrates that the proposed framework achieves an accuracy of 97.4%, precision of 96.8%, recall of 97.1%, and F1-score of 96.9% in detecting intrusion attempts. The system also reduces transaction latency by 34% compared to conventional blockchain voting systems. Results confirm that the framework delivers transparency, tamper-resistance, and strong security guarantees, making it a viable solution for modern democratic elections
The article discusses a decentralized electronic voting system based on blockchain technology. This study aims to improve the performance and fault tolerance of blockchain-based electronic voting systems by introducing the Automated Leaderless Byzantine Fault Tolerance (AL-BFT) consensus protocol. This study aims to develop and evaluate an electronic voting system model that applies the proposed AL-BFT consensus mechanism in a permissioned peer-to-peer network. The methods used include computer modeling of a peer-to-peer (P2P) network, implementation of a decentralized ledger, and experimental load testing of the consensus protocol. System performance is evaluated using key metrics, such as transaction latency, throughput (requests per second), fault tolerance threshold, and scalability. The study results include the development of a conceptual architecture for the electronic voting system, the identification of its core components, and the analysis of their interactions to ensure data integrity and the reliability of voting results. At each stage of the electoral process, data security is considered, and additional protection mechanisms are analyzed to enhance system robustness. Eliminating the leader election phase from the consensus process is a key feature of the proposed approach, thereby reducing coordination overhead and enabling more efficient agreement among nodes. The proposed AL-BFT protocol reduces transaction latency and improves throughput while maintaining the fault tolerance level of traditional Byzantine Fault Tolerance-based approaches. The results confirm improved efficiency compared to classical leader-based consensus mechanisms, particularly in small permissioned blockchain networks. Conclusions. A practical implementation of the system has been developed and tested under real simulated load conditions. The proposed solution ensures stable system operation and reliable consensus formation. The system can be effectively applied to university elections, organizational voting, and other scenarios that require transparency, security, and manipulation resistance
Novan Ilham Ramadhan, Rizky Parlika, Ardhon Rakhmadi
Electronic voting (e-voting) systems continue to face challenges related to transparency, result validation, and duplicate voting prevention. Blockchain technology offers characteristics such as decentralization, transparency, and immutability that can support more auditable voting processes. This study presents a prototype implementation of a blockchain-based e-voting system using Ethereum smart contracts to support transparent vote recording, public auditability, and wallet-level double voting prevention. The system was implemented using Solidity-based smart contracts deployed on the Ethereum Sepolia Testnet and integrated with MetaMask for transaction authentication. Smart contracts manage election creation, candidate registration, voter registration, vote recording, duplicate vote prevention, and voting result finalization. An audit trail mechanism was implemented to allow voting activities and transaction records to be publicly verified through the Etherscan blockchain explorer. All predefined functional testing scenarios were executed successfully. The evaluation results indicate an average gas usage of 75,109 gas, an estimated transaction cost of 0.000113 ETH, and a transaction latency of approximately 4–5 seconds. The implemented wallet validation mechanism and hasVoted mapping effectively prevent duplicate voting attempts associated with the same wallet address. However, the proposed system represents a prototype-level evaluation conducted on the Ethereum Sepolia Testnet and does not provide voter identity verification, ballot anonymity, or real-world election readiness. The findings demonstrate the feasibility of Ethereum smart contracts for transparent auditability and wallet-level double voting prevention in blockchain-based voting environments.
Online voting platforms that rely on classical cryptography and centralized trust anchors face escalating challenges as the demand for secure and transparent digital elections grows. Such systems remain exposed to quantum-era threats, insider manipulation, and delayed audit mechanisms, which together can undermine public confidence and electoral legitimacy. To counter these risks, a quantum-resistant, multi-layer blockchain architecture has been developed to enable remote voting with continuous verifiability and resilience. This architecture resolves key weaknesses through five integrated layers. Quantum-Resistant Distributed Ledger Initialization (QR-DLI) embeds lattice-based cryptography, specifically Kyber and Dilithium variants, directly within the genesis block, ensuring the ledger is tamper-proof from inception and immune to quantum brute-force attacks. The Self-Adaptive Smart Contract Governance Engine (SASCG) introduces dynamic, participation-aware rule adjustments, allowing principled governance without manual overrides and ensuring that voting periods and eligibility rules adapt securely in real time. Homomorphic Vote Encryption with Multi-Authority Shard Key Distribution (HVE-MASKD) guarantees ballot confidentiality and authenticity by combining fully homomorphic encryption with distributed key shares, eliminating single points of trust. The Zero-Knowledge Proof–Based Real-Time Audit Layer (ZKP-RTAL) continuously validates ballot integrity while concealing vote content, creating a public and immutable audit trail. Finally, the Federated Performance & Threat Intelligence Optimizer (FPTIO) aggregates live telemetry and historical attack data to proactively tune consensus parameters and predict potential intrusions without interrupting the election process. Collectively, these layers achieve sub-second cryptographic operations, transaction throughput exceeding 1,500 TPS, over 99 % fraud detection accuracy, and strong scalability. The model provides a future-ready, auditable replacement for current e Voting infrastructures, strengthening digital democracy through post-quantum security, adaptive governance, and intelligent, continuous optimizations.