PrivacyGuard: Collaborative Edge-Cloud Computing Architecture for Attribute-Preserving Face Anonymization in CCTV Networks

Closed-circuit television (CCTV) footage, a vital component of security and law enforcement, also poses a potential privacy threat. To mitigate this, it’s crucial to anonymize personal identifiers in the footage, preventing their unauthorized or unintended use or disclosure. An effective anonymization system should remove sensitive identity information while preserving attributes necessary for other identity-agnostic tasks. Among several biometric personal identifiers, the face is the primary one. However, anonymizing facial images while retaining facial attributes is a challenging task. Furthermore, neural networks, instrumental in face detection and recognition, often come with significant computational demands, making them unsuitable for resource-constrained edge devices. In a novel approach, our study proposes PrivacyGuard, a modular architecture with two major parts: the image quality assessment and dispatching module, and the attribute-preserving anonymization module. The former categorizes images by quality, sending low-quality images to cloud servers and processing high-quality ones on local edge servers. Simultaneously, the anonymization module leverages lightweight NN models on the edge to generate obfuscated images while preserving the relevant attributes for downstream machine-learning tasks. Experimental results on a diverse dataset (i.e., ethnicities, genders, etc.) demonstrate the model’s effectiveness in quality assessment and anonymization while retaining the relevant attributes. The anonymization model achieves an attribute preservation rate of 98%, along with obfuscated identities.