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).
Today, biometric authentication has become a central component of user security in social governance systems, where each government department demands access to user-specific data that varies across agencies. However, storing such data in centralized repositories increases serious privacy concerns, as unrestricted access by multiple entities maximizes the risk of data leakage. To address this, our research presents a novel biometric authentication system integrating robust privacy-preserving techniques, built on advanced deep learning architectures and differential privacy algorithms. A blockchain ledger integrated with a Merkle tree is used to securely store user identities, providing tamper-evident cryptographic validation of registered users. We further develop a novel hybrid model by integrating a pre-trained Vision Transformer (ViT) with a differential privacy-based machine learning enhanced training strategy, wherein the model is trained on noise-induced images to resist inference attacks. The system without differential privacy achieves 90.80% accuracy, 0.94 precision, 0.91 recall, and an F1-score of 0.90 in the standard configuration, while the differentially private model maintains 68.97% accuracy with ε = 6.2, ensuring a strong privacy—accuracy balance. The evaluation confirms that our proposed model, incorporating differential privacy, provides a secure and scalable solution for managing sensitive citizen data while achieving reliable performance in privacy-aware biometric verification for real-world e-governance applications.
Abstract Traditional Identity and Access Management (IAM) systems rely on static credentials and centralized authorities, leaving organizations vulnerable to single points of failure, credential theft, insider misuse, and increasingly sophisticated deepfake impersonation attacks. In this paper, we propose a Decentralized AI-powered Zero-Trust IAM (DAZT-IAM) framework that combines permissioned blockchain infrastructure, self-sovereign identity (SSI) principles, and deepfake-resistant multimodal biometric authentication (face, voice, and behavioral keystroke dynamics) with a continuous, risk-adaptive AI trust scoring engine. The proposed system is compliant with ZTA principles and checks every access request continuously, unlike the older “authenticate-once” models. Blockchain-anchored Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs) eliminate the dependency on a central identity provider. The biometric pipeline includes a dedicated deepfake-detection module that employs frequency-domain artifact analysis and temporal consistency checks to counteract synthetic media spoofing. We describe the system architecture, consensus and smart-contract design, the multi-modal fusion and liveness detection pipeline, and a risk-scoring model for adaptive access decisions. The experimental evaluation on simulated and benchmark datasets demonstrates that the proposed framework provides competitive authentication accuracy, high detection rates of deepfake attacks, and low average access decision latency, while removing single points of failure for identity. Our results demonstrate that the integration of blockchain-based decentralization and AI-based continuous trust evaluation provides a pragmatic approach of resilient and privacy-preserving IAM for sustainable digital infrastructure.