Balancing Privacy and Utility in Surveillance Systems: An Overview

This paper delves into the critical issue of privacy protection in video surveillance systems, emphasizing the need for comprehensive and interdisciplinary approaches. The study presents recent advancements in de-and re-identification technologies aimed at ensuring personal information protection. The mechanisms for creating high-quality anonymized images that do not compromise the original data distribution and methodologies for effective facial image restoration are explored. Special attention is given to the operational dynamics of systems under specific conditions and their management of distinct facial features. The necessity for an integrated approach in constructing AI systems in edge computing is highlighted, focusing on anonymization techniques, re-identification prevention, and data distribution maintenance. Through these research efforts, we anticipate balancing privacy protection and data identifiability, offering a solid foundation for future exploration in this field.