An Edge-to-Access Secure Framework for Privacy-Aware Urban Surveillance

Abstract

As smart cities increasingly rely on IoT-enabled surveillance for public safety, the industry-standard practice of streaming raw footage to centralized cloud servers has introduced critical vulnerabilities regarding data privacy and accountability. Current "collect-first, protect-later" architectures create massive targets for cyberattacks and unauthorized administrative access. This report will propose an exceptional, three-layer security framework which consists of the Edge Layer, the Cloud Layer, and the Access Layer. Our particular design is to introduce a "Privacy-by-Design" methodology. At the Edge Layer, we implement an atomic, real-time anonymization process using lightweight deep learning models like YOLO (You Only Look Once), BlazeFace, and Reversible Chaotic Masking. This ensures that Personally Identifiable Information (PII) is redacted in ephemeral memory before network transmission, effectively neutralizing Man-in-the-Middle attacks. The Cloud Layer secures data that is not being transmitted via AES-256-GCM and ensures model integrity through OpenSSF Model Signing. Crucially, the Access Layer addresses the risk of data breaches and unauthorized access by utilizing Role-Based Access Control (RBAC) with a blockchain-based immutable audit ledger. To address hardware constraints, like a device with outdated hardware, this system utilizes Particle Swarm Optimization (PSO) for intelligent task offloading. After extensive comparisons, we can confirm that this holistic approach offers superior privacy protection, bandwidth efficiency, and forensic non-repudiation compared to other existing centralized surveillance models.