Semantic Privacy-Preserving for Video Surveillance Services on the Edge

Intelligent Video surveillance systems, leveraging edge computing, have become increasingly prevalent in various facilities, providing advanced monitoring and management capabilities. However, these systems can inadvertently compromise personally identifiable information, such as human images, leading to privacy violations. We introduced a semantic privacy-preserving video surveillance service on the edge to address this critical issue. Unlike traditional centralized models, the solution operates as a decentralized machine learning framework within the video surveillance infrastructure at the edge. Its primary focus is protecting private information extracted from captured video streaming data. This research integrates cutting-edge machine learning techniques, including scene graph generation and semantic communication approaches, by enabling edge nodes to exchange parameters for training, referencing, and safeguarding data privacy and ownership. These innovations collectively contribute to the protection of human privacy. The performance evaluation confirms that the solution is an efficient and effective privacy protection platform, offering a significant advancement over conventional centralized solutions.