Reversible Facial Anonymization with Adversarial Privacy and Edge-Aware Security for Multimedia Applications in CIoT

As facial recognition becomes increasingly integrated into consumer Internet of Things (CIoT) ecosystems such as smart cameras, mobile devices, and home surveillance protecting multimedia identity data while retaining utility and ensuring security has become a pressing challenge. Existing anonymization techniques often result in irreversible transformations that prevent legitimate identity recovery, limiting their applicability in scenarios like access control or forensic verification. To address this, we propose a reversible facial anonymization framework designed for secure multimedia processing in CIoT environments. Our approach combines Reversible Noise Injection (RNI) for learnable encryption, Hybrid Adversarial Training (HAT) for privacy preserving transformation, and a Zero Trust Identity Recovery (ZTIR) module that enables authorized identity restoration through cryptographic key verification. The system enforces security through multi factor authentication, TLS encrypted communication, and optional blockchain based key management. Implemented on edge devices, the framework supports real time anonymizationwith low computational overhead and empirically strong privacy protection, as measured by reduced recognition accuracy and perceptual or distributional metrics. These results validate the framework’s suitability for privacy preserving and secure multimedia intelligence in real world CIoT deployments.